Hey everybody, Dr. O here, another frequently asked question, should I take breaks from fasting? Now there are times that I've taken breaks from fasting. I don't think you need to, but I do think it's important if you have a lot of weight to lose that occasionally you do focus on maintaining your weight. So the two reasons that I've taken breaks from fasting and not from a health standpoint not because they were no longer working, but number one, I take breaks from fasting when my focus is building muscle, right? If fat loss is your primary goal, then you should focus on the calorie deficit and you should keep fasting. If building muscle is your goal, then you should focus on training and fueling that training. So I have done that, so because fasting is a great tool, but you should only use it when it's the right tool for the job. Fasting is not the right tool when you're trying to build muscle. So why do I try to build muscle? Well, it makes you look better, but I really want to keep my metabolic rate high, right? I think one of the main reasons that we gain weight back so easily after we lose it is that our metabolic rate is dropped. So if we go back to eating like we used to, not only will we gain the weight back, we'll probably gain back more. So I think that so I do like to keep my metabolism stoked up by focusing on building muscle and retaining and building lean mass, but also taking breaks from fasting and taking breaks from really dieting and focusing on maintaining your weight can be really important because who cares how fast you can lose weight if you can't keep it off. So taking breaks where you focus on maintaining your weight, basically reset your kind of body set point and say, okay, I can live here. I can live here forever and then move on to the next one. So taking breaks is good probably physically, but especially psychologically. Let me tell you about the first break that I took from fasting, right? I'd lost a lot of weight. I don't know, I don't know exactly where I was at, but let's say I'd lost a hundred pounds and we were coming up on Thanksgiving. So the week before Thanksgiving, I decided I'm going to, I'm going to take a break and focus on maintaining through the holidays, right? Most people gain weight during the holidays. So I was, I was hoping that I would just maintain my weight and I was going to be okay, even if I gained a pound or two, because if you're eating more, then you're going to, you're going to be holding on to more glycogen and more water and things like that, even just having more food in your stomach. So I purposely took that break. I wanted to maintain my weight through the holidays, not because I was trying to prove it to myself, but I did. And then I, on January 2nd, I weighed myself and I had lost three pounds during a six week break. So my, what I thought was going to be maintenance for the rest of my life was still putting me in enough calorie deficit where I was losing half a pound a week. I was so, so happy because honestly, I'm 44 years old now, I was 43 then. This was the first time, maybe 42. This was my first, the first time in my entire life that I believed I could keep the weight off. I have lost a thousand pounds and gained it all back. I've lost weight 49 times, gained it back, or 40 times and gained it back 39 times. And that was honestly the moment that I realized I could do this forever. It was a breakthrough moment for me psychologically. So should you take breaks from fasting? You don't need to. If you're having great progress and things are going awesome, then go for it. If your weight loss is stalling or you have a vacation or holidays coming up or you just want to focus on maintaining or if your goals switch from fat loss to performance and building muscle, those are all times that you, you could or should take breaks from fasting, but it is never required. But I, you know, let's think about the long haul, short breaks from fasting where you can focus on maintaining your weight and making it a lifestyle you can stick with forever. It's more important than eking out another half a pound here or there. Hope this helps. Have a wonderful day. Be blessed.


This year has definitely been the capstone of many years of training academically. It's been an amazing experience getting to know all the different attendings, all the different staff here at the Stelman Clinic, both on the research end and on the clinical side of things. And then personally for my family, getting to be here and bail and experience the mountains and an amazing ski season has been really special. Match day I still remember I was in clinic actually and I was constantly checking my computer and interestingly enough my wife had my login and she found out before me and she called me and didn't initially tell me where I had matched but we were very excited to learn that we were getting the move to bail and learn from some of the best in the world. This year we have just a great group of six fellows in total and we all really get along. They'll definitely be colleagues and friends for the rest of my career, the rest of our lives and our family's lives.


Okay, everybody, it's 12.15 and welcome to this week's webinar with myself Jasper Lawler on Bar for CMC Markets. Quite a lot of things going on this week, so glad you could attend. We've got the risk warnings on the screen that we're going to look at first. If you have any questions at any point, certainly feel free to send them through the chat or the Q&A box and I'm happy to answer as best I can. So you can see we're red across equity markets today. I'm hoping a few of you managed to read my morning update. Basically, it's just you've got risk aversion here, so any of you who are long gold, any of you who are short equities, any of you who are long the yen, you know, you're doing well, maybe long bonds, short yields, that's all risk aversion type stuff. Why is that risk aversion? Well, specifically when we're looking at sterling, you know, we can highlight the Brexit risks. Now, there were some polls showing the biggest lead for leaving the EU that we've seen so far, but also there's a lot of stuff going on for the UK economy this week. On Tuesday we've got inflation data, Wednesday we've got unemployment data and wage data and on Thursday, of course, we've got the Bank of England. So there's some selling going in ahead of that data and ahead of the Bank of England meet. The data for the UK, if we look last week at the industrial production, for example, retail sales before that have been pretty decent. So I'm not sure that this is selling in the pound necessarily ahead of inflation data. Oil price has obviously been rising and the other data has been okay. We've seen a bit of a growth slowdown, so that could impact inflation and maybe that growth slowed down again, could weigh in on unemployment. It seems like businesses are actually those more hesitant ahead of the referendum than consumers so much, but even that seems to be vaguely limited looking at that industrial production data. So maybe there's been a bit of a hold back on hiring before the referendum. I'm fairly doubtful of that to be honest. I don't think it'll be anything substantial. So while we're on the matter, why not just have a look at sterling because that's one of the big, big, big movers that we've seen. This is the chart that I've had on for a while. Now we've been in this holding pattern since we collapsed when the vote was first announced, the data of the vote was announced. We crashed all the way down, but we've not got really back below those 138s, 139s lows. We've basically fallen into a trading pattern. We had a few, it looked like a week or so ago that actually the remain camp was well ahead. The polls have changed since then. We've seen a big drop in sterling and obviously last week was a particularly big one. Suddenly getting hammered with phone calls as soon as I start a webinar. So we highlighted in last week that there was a couple of areas of potential support. Obviously we got down and we basically already tested this 14340 type area on that drop there. And so this rebound to the ties which we then failed at, once we'd found that support but then failed again at the resistance of this trading range area, it did increase the odds that we're going to drop through that support. Obviously that happened pretty spectacularly on Friday and we followed through on that today. So there was a bit of a pause at the 142 round number. So I think this formally broken trend line, we held above that last week but we've actually gapped onto the trend line and below it today, suggesting that to me looks like 14050. This support down here, obviously the lows, we're only 50 odd pips away from that anyway. That looks fairly likely that that kind of area is going to get challenged. We're already well down there on the 141 but 140 is obviously the big round number. I think we come pretty close out again. There's going to be chop and change with the poles. So Sterling is no doubt tricky to trade at the moment. I think we've probably got to say that it's going to be in this choppy trading range until we have the result. By default, an in-vote decent for Sterling and an out-vote bad for Sterling, I think that's a fair assumption. What probably is worth noting as well is that the Bank of England are going to be on hold for a while with the Fed on hold and with the UK economy looking a little bit shakier and a natural sort of dovish inclination on the BOE. In my opinion, they're going to be holding on for a while. That is a net negative for Sterling. Even if the referendum turns out all right from a Sterling perspective, it may not be that we've made much progress beyond 147. So difficulty with the sharp turn down here is that we've obviously to some extent missed the boat. We've had a decent rebound off the lows. So if you are still looking to try and capture some downside, I would say there's risk here but the fact that we're below this declining trend line that was broken now we're back below it suggests that the trend is still down. That was a good technical barrier and really all you need to do is just pull down to the lower timeframes and see if we can find some pullbacks from these former areas. We've also, we already had a little bit from 142.30. Obviously if we could get back up to these peaks at 142.70, look for any turnaround in that area. If not there, then back to this consolidation point at the 143.30, the 40 area that we previously mentioned. Well, on the subject of FX, we've obviously got the Fed this week. That is event numero uno for markets and the fact that we've been getting a bit of a rebound in the dollar recently certainly weighs on other markets. Rebound in the dollar, that's negative for oil. Obviously oil has been a big scare factor for markets but it also affects China. China devalued their currency at the fix by about two handles today. That was quite a big drop basically in the value of the Chinese yuan against the dollar as the People's Bank of China set it back down close to those lows that we saw this year which were actually in themselves five year lows. That feeds again into that devaluation story with the Chinese yuan. That devaluation happens as the dollar goes up in value. China has to then devalue against the dollar because it's pegged. China has to bring that exchange rate lower in order to stay competitive. Because the dollar is a big part of the basket of currencies that they monitor, they have to devalue against the dollar to keep that basket in line. That devaluation risk comes back into play when the dollar is rising. I don't think the dollar has much basis for rising and maybe what we could see this week is if the Fed are fairly hesitant and dovish, then this dollar rally eases up and maybe that benefits the risk on behavior in markets. Keeping in mind that last week we came as high as one percent of the record highs in US stocks. Let's just have a look at the US 30 while we're at it. This is typically where I start things anyway, but let's just get back to where we normally are. This is the range that we've been looking at for a long time. It's acting fairly orderly. Here was the first warning sign obviously with the benefit of hindsight, but we had the support. We've got a false break. If you bought just on this hammer type pattern at the lows here on the 19th of May, you're laughing now obviously. You pretty much made 17,400 up to 600 points just on that little reversal at support. Trading can be simple, but obviously you get a bit worried on the journey. Technically things worked out quite smoothly. We hit the resistance. We pulled back to the resistance. We came back, hit the broken declining trend line, and we pushed up and made a new high, but we're now pulling back from the round number of 18,000. Here's the record peaks from pretty much this time last year. This is understandably a big area of resistance. I have to say I'm mindful of the next big turn lower. We've not quite got the makings for it yet. We're still making higher highs and higher lows. I'm still positive on the market now, but I'm mindful of us making the big turn lower inside this trading range. As I said, as we hit this 18K mark, that was within 1%, so you can't exactly really describe us in a state of risk-off markets in terms of US stocks. Little bit different when it comes to stocks on the continent, obviously. Let's have a look at the FTSE here. We've had this support in for a while. I've not changed this trend line since I first drew it in against this low in May, and we've held it again nicely today. I mentioned in the chart forum that I would say, given that we've got this highs in April, we've come down, we've challenged the support multiple times it holds. Good. Move up, retest the 200-day moving average. Try and take out that peak from May 31st, fail to do so, and nowhere close even to taking out the April peak, and now we're back down at the support again for what you could call a 1, 2, 3, 4, 5, 6, 7, 8th time. We're well overdue breaking the support at this point. I think you've got to temper what time frame you're trading off here, and you've got to manage the risk because we've already come down fairly big in the last couple of days, three days including today, but I think the odds are that we're going to break the support. That's my feeling on this, and so if we do that, then to me the next logical area would be this, this low that we put in in December, which has acted a bit of a pivot point, kind of a tertiary break, a down retest, up and above, not so much, but then a retest there. So to me that lines up. Obviously we've got 6,000 below, so we could get a false break down to 6,000. Now up again, depending on how the polls go, global risk sentiment, what the Fed says, that's fairly feasible, but I think the support gives way, even if it is a false break, but my sense is that the next logical area of support again is that 5, 8, 6, 5. Again we're in a trading range, so pick an extended periods of direction one way or the other, this is not the opportune market for trading the trend right now, the FTSE 100, it's choppy as hell. And so, you know, this is just a matter of your own trading style, do you just regularly trade the UK 100, in which case okay, so you're stuck on this market, you have to trade what you get. If you're not stuck on the UK 100 and you're open to trading at the diverse range of markets, look to something that's trending, because this is really a mess at the moment. We've got a nice support at 6,050, but as I said it's, I think it's a good chance for it breaking, it's tricky to call at the moment. Germany 30 looking even weaker, so you can see this is, again had this area of support in since we put these series of lows in here, we've got a nice rebound off, it got above the 200 day, again similar to the FTSE bounce it off the 200 day, but we just haven't even taken out the May, same thing, you haven't taken out the May 31st peak, let alone the April peak, rolled over, broken the fairly well defined rising trend line, and as of today we gapped down beneath this support area here, so this support has completely given way as of now. If we get it close up above this gap, that would actually be a pretty positive development, a gap down and then close above, that's actually more of a bullish reversal, but we're not there yet, but we need to be open to the idea, just because we open down here in itself is a very sign, but you know beware of where we close, but I think you can see it's fairly clear cut on the chart that the next support is the round number 9500 with these two lows from back in April and March being the sort of defining price support, but this you know I've mentioned previously that this is a bearish pattern and we have broken out down to the downside, so things are kind of making sense if you like on the Germany 30, again it's choppy range trading conditions, but you know that we were in a bit of a trend and obviously the fact that we couldn't even take out this high, when theoretically if this pattern was to be holding we should be heading up right here for a challenge, you know that obviously has not in any sense the word happened, so you know we're in at the very very least we're in range trading conditions here, not a strong uptrend. Not so much in the way of Euro data this week, very much a focus on the UK, but also the US, I think the dollar index has maybe fallen off my chart because I think it was a June expiry, so we're on to September now, but if I just go here, if I add this back to my, what's going on, always a risk when you do this sort of thing live, okay let's slide that into my watch list over here, so we'll have a quick look at the dollar, I mean I was a personal preference so I don't actually trade the index, so you know the euro is a big component of the index and you get some of the bigger moves against the individual currencies, yeah like I said this this index this contract has not been live for that long, but we only got a bit of data on this, but you can see as I was talking about last week a couple of big jump-up days in the dollar index and I would say uncoincidentally big sell-off in markets at the same time in stocks, but again just based on US economic data and based on comments from the Fed, not that much justification for dollar strength at the moment, you know the only real arguable reason for dollar strength is that you're still comparable to Europe and the UK which faces its own set of risks maybe on a relative basis, still there's some safety to be had by moving into US assets, so I think that's you know that's you know that's why the dollar acts as a haven in times of uncertainty, so that that sort of haven type basis relative outperformance even though its performance itself is kind of weak the US economy could be a reason for dollar strength, but obviously a lot of will depend on what the Fed and how they how they tune their wordage and it's always very hard to guess exactly, it always makes sense to my experience to lean on the make some dubious assumptions, but the Fed could leave July the July meeting on the table, I'm doubtful of that based on the recent speech from Janet Yellen in Philadelphia, I tend to think there's gonna be a very vague reference of the next few months which perhaps leave September on the table, so let's move small specifically into effects we've looked at sterling but well let's have a look at the yen because obviously we've got the Bank of Japan meeting this week as well that's on Thursday, early hours Thursday, I'll look at Dolly and also look at sterling yen, I mentioned on Twitter that sterling yen has been absolutely smashed, you know you might have seen it yourselves, but failed breakout of the declining trend line here on the Dolly yen, so again one of those were you know if you bought here on the the break of the trend line potentially could have been a good move but actually we failed at the resistance, it wasn't a high probability move but depending on your strategy if you trade trend line breaks you have to take them, you know okay so the so actually we've got an inside day where this this candlestick fit inside this daily candlestick and then we've got a breakdown the next day back below the trend line so hopefully you know this is the thing with trading if this was a failed trade for you, you bought on a break out there you know maybe had your stop under the lows then it got taken out on this candlestick hopefully you've got the where the walls think okay things have changed we're back below the 21 or whatever's that the 50-day moving average back below the the trend line more importantly inside day breakdown lets less change directions and then you've got opportunities and now we're back down right at the support so if you've been you know taking profits around 106 you know you're laughing on the short side so we are back at the I mentioned last week that I thought we were heading back down to these lows but I did say that I think we maybe get where maybe we hold them you know maybe a false break lower down to 105 our support that we've been mentioning for a while which is basically already been hit but you know 105 30 maybe we get a little drop through that down close to 105 and then a pop so just kind of these scenarios scenarios available in your mind there's different ways of playing that you can obviously have an order resting down near 105 I would say perhaps a more prudent approach is wait for some confirmation off 105 before deciding to assume that this is a rangebound market rather than outright downtrend as far as the Bank of Japan meeting they have surprised recently by nature by turning rates negative that was not well received by markets and so be surprising if they were if they've doubled down and and did that again really I think the only thing to be well received by markets would be if they if you if they enacted some sort of extra quantitative easing but the political support for this policy is starting to run out in Japan you know they are literally buying ETFs they own a good chunk of the the JGB bond market they're running out of space to be able to do anything more I'd be surprised if they if they do anything really interventionist particularly after this G7 meeting recently as well where there was a bit of a sort of bias against intervening in currency markets coming from the US so to my mind the BOJ are gonna sit and do nothing and you know maybe reconsider the situation once we're below 100 if we get below 100 but let's have a look at euro as I mentioned not so much in the way euro data so pretty much going to get driven by the by the Fed choppy difficult markets you know in general we're near the top of this historical trading range and the sell orders the higher probability so we had a sell off at 114 you know I didn't particularly highlight that area in my chart you know I think the 114 146 30 114 60 sorry misreading the numbers here 114 60 a bit higher probability into the 115's would truly be the top of the range so what we'll have to see how we go from here obviously dovish Fed long euro sorry dovish Fed short euro and you know hawkish Fed vice versa but again that same principle in terms of talking about I know the euro dollar is one of the most popular products and you know short term it's a different affair and I know some of you use this webinar for short term trading just a bit get a bit of a longer term outlook not necessarily for any specific levels a bit more just general bias for you guys obviously you know there's always a short term trading opportunities every day but you know on that longer term looking for a trend nature you know I'm I wouldn't be looking at this mark at the moment there's issues is chopville I did mention I would look at sterling yen so we basically got down to 150 in sterling we've actually bounced 40 odd pips from from the lows 30 of pips also we actually dipped below 150 I didn't even see that so that that happened in the last hour or so so we've hit that big round number support potentially got further to go if we're going to challenge this declining trend line but it's only two touches not too much to that just something to be aware of we're obviously also coming up to this RSI trend line so potentially a bit further to go really the only thing you got going for you on this if you're going long here is that we hit the 150 mark so round numbers by themselves not really a reason enough to to go along the currency my bias is that the Bank of Japan not doing any more easing so that's that's positive yen and obviously stocks are selling off yen is a haven currency is a safe haven so about five minutes left of the webinar at this point any cues send them through but another big factor that I can't remember if I mentioned at the start of the webinar but oil back below 50 so I've been highlighting this divergence we got another pop through the rising trend line I'd highlighted a pop through the diverging RSI line but we're back below it now so that's that's a bit negative what I had mentioned previously is that we you know it made a bit more sense when we hadn't had that big nice run up above 50 but still to some extent we're got this rising price trend line and we've got this RSI line so RSI normally a leading indicator obviously gives false signals but we're below that rising RSI line and the next test would be this rising price trend line here so to get down there would be quite a big drop-off so maybe some you know maybe some dip buyers down there but if we do finally give up on that rising trend line you know that may be the confirmation that 50 it is the short-term top gold is gold's been a lot of fun gold has been a you know a great market for trading also a bit range bound but it's just held the range very well so here was them here was the one area considered to be the bottom of the range and then here is the the one and 190 we basically came down in the mid of those two supports you know you could argue that we basically came off 1200 so we're two areas of support we hit the mid of those two supports which happens to be the round number we bounced pretty aggressively we're right back up to the top of the range and gold it's still at this point though you know even though at the moment it feels like everything is is risk-off keep in mind that just a week and a half ago everything felt very bullish and the gold was selling off so that can sentiment can change on a dime and we're basically running back up into this area that we they're kind of triggered the sell-off in the first place which is the sort of one to 90 kind of area I'm assuming we're going up to test one one 300 but you've got some sort of pretty a pretty short-term outlook with some controlled stops to begin along at this point because we've had a big old run up four days of gains but actually we're up for six days out of the last seven with a big old move almost up well up $90 so that's that's pretty huge certainly that you know we've had a false break to the downside but keep in mind again not too distant history where we basically had a break to the false break to the top side so kind of we've had it both both areas of the range here anyone trading the breakouts not loving gold anyone who's doing trading the range loving gold so I think that's about it we pretty much hit the 30-minute mark so thank you very much for attending it's going to be a fun we've got the central bank meetings obviously they're going to be the the main drivers but always back below 50 we'll have to see if that can hold and it's you know we've seen bond yields at record lows so will the German bund drop below 0% into negative the German 10-year that'll be a big question market markets could all happen this week so hopefully there's be some action for you to trade but thanks very much for attending and good luck with trading just below the signing out


Alright, so why don't we get started here. I take great pleasure in choosing Professor Gadak. John asked me to do this bio shot, so I'll just say a couple of sentences. John is the Duke-L.C. Jackson Professor of Electrical Engineering and Computer Science at MIT, and also former head of the ECS department. Among other things, he's also my former PhD advisor. So you'll say some dirts down about it, right? Well, I think that's why you want me to keep it short, right? Yeah, he knows way too much about me. But one of the things that you probably have noticed is that John has worked in many different areas from hardware verification, software abstraction, software radios, and now telemedicine and medical decision making. So he's been talking about a couple of projects, but broad reaching interest over the years has ultimately made it to this. One way to describe it, the other way to describe it is adult attention deficit disorder. My attention span is too short to work, and I need one thing for very long. Alright, so thank you, Zeishan. And as I've told several people today, one of the biggest mistakes I ever made as a faculty member was letting Zeishan graduate. He was way too good to let him leave. But you guys went for that. Alright, so we'll get on with it. I was always taught that there's a high risk that people will leave your talk before it's finished, and therefore the things you really want to say, you should say at the very beginning if you're not getting what you said. So here's the real message of today's talk, which is not what I'm going to actually be talking about, and I address this mostly to graduate students and undergraduates who might be in the room, but even to faculty who are not too old to learn new tricks. I think it's time for computer scientists to really focus a lot of their attention on research that impacts health. It's intellectually tremendously interesting. It lets us do interesting computer science. And it's psychically tremendously rewarding to know that you're actually doing something that might someday help people. And in case there are any doctors in the room, they should learn some computer science because it will make them better physicians. And when you're talking to undergraduates, what I've done over the last several years is spend a lot of time convinced in undergraduates who want to become physicians that they should major in computer science, not in chemical engineering or some such thing. They will get into medical school and they will be better physicians for it. All right. Quickly, here's my research group at a glance, what we do. This is a dated picture. And I brought it out only because I wanted to show you what Zeeshan looked like before he adopted all the burdens of being a faculty member. You were the tallest member of the group? Yes. So the good news is that since Zeeshan left, I'm the tallest member of the group, which is not easy at 5' 9". So that was one disadvantage of having him around. We work at the intersection of computer science and medicine, mostly in algorithm design, signal processing, software systems, and more and more attention on machine learning, clustering, data mining, things of that nature. OK. Here's to the real body of the talk. I think it's productive to view management of health. And so I'm looking at health care very broadly construed from the perspective of someone who studied a little control theory. And any of you who've taken a control theory course are familiar with this kind of diagram. You've got a controller. You've got some actuators. You've got what a control theorist calls the plant, more humanely might be called the patient. There's a hidden control system which unfortunately we can never see, the homeostatic control system that all animals come equipped with. There's an environment which we usually can't control. Is it hot out? Is it cold out? What is the patient doing? There are some control variables. We don't want to see everything about the patient, or we can't. And so we might look at the course of the disease or measuring some side effects. There's a huge error element. And it's one of the things I'll talk about a little bit is that, to my mind, way too much of medical decision making these days is based upon evidence that's subjective rather than objective.  And then they change the dosage based on the answer. Who knows how reliable that is? I work in epilepsy a lot. And there have been some control studies which show that patients' reports on incidents and severity of seizures are incredibly inaccurate. So there's a lot of that. And then back to the controller. Well, that's a complicated picture. Here's a much simpler picture of the same thing. And this is what I think of the medical controller, get information, evaluate the patient. And that's different from getting the information, drawing conclusions from it. Choose some intervention, perform the intervention, go back to the top and do it again. Unfortunately, for a lot of interventions, for example, typically drugs that are taken on, say, a daily basis, the intervention is done over and over and over again. Every time you take a pill, you're making an intervention. And very little checking is done as to what the effect of that intervention is. As anyone who's ever looked at any control theory knows, the length of the loop should be governed by the rate of change of the plant, in this case, the patient. If the plant is changing quickly, you've got to interrogate quickly. Otherwise, you do it more slowly. Unfortunately, in medicine, it's much more likely to be controlled by the availability of the physician than by the actual need. So people get high blood pressure medication, and they come back six weeks later when, in fact, there is something measurable much faster than that if someone would take the time to measure it. So when we control complex systems, we would like them to stay with intolerance. It's much easier to keep a plane from going into a tail spin than to get a plane out of a tail spin. And you'd like to minimize the severity of the interventions, because almost every intervention has some unpleasant side effects. How do you do that? You measure often. You analyze the data quickly and thoroughly. You would like to be able to predict future states, which is very important, and then intervene before it becomes unstable. So for those of you who don't know about control theory, here's a very simple control system. You all use it most days.  You notice that the water is too cold. So you turn the faucet a little bit. Water gets warmer, but not warm enough. You turn the faucet a little bit more. Water is now a bit too warm. You turn it the other way.  Well, what would a shower look like if the doctors were in charge, or the medical system were in charge? You'd get in, and instead of the faucet, you'd just push a button that would turn the shower on. You'd notice that the water is too cold. So you telephone the plumber, because you can't control it yourself. And unfortunately, you're not allowed to get out of the shower. You have to wait until the plumber arrives. The plumber arrives, and instead of going to the shower, goes into the basement somewhere and does something to the hot water heater. The plumber leaves. Minutes later, the water becomes scalding hot. You telephone the plumber again.  This is kind of what the control system looks like in the world of medicine. And the shower is kind of like that. Well, obviously, we should be able to do better. Here's an example, a congestive heart failure. Probably the leading cause of disability these days in the United States, typically managed using some combination of a half dozen drugs. The appropriate combination dosages should vary a lot from person to person. And in fact, for every person, they should vary from day to day, depending upon exercise, diet, things like that. In most cases, the adjustments are clearly non-optimal. They're not done very often. They're done with very poor information, using things like weight as a proxy, resulting in millions of unnecessary days in the hospital. And who knows how many unnecessary deaths? So all of this is just by a way of saying there's a problem. There's a logical way to think about it. And we really can do better. So we can reduce the length of the loop. We can get better information, more representative. And by that, I mean too much of our information about people is gathered in the clinic. Well, fortunately, most of us do not live in clinical environments. And maybe our blood pressure in the doctor's office is not the same as our blood pressure during the day. Certainly, if you're exercising, things are different than when you're sitting in a chair or lying on an exam table. So you'd like to get information in vivo that's representative of what the patient's life is really like. You'd like to get it in a timely fashion. And mostly, you'd like to get a lot more of it. And then you want to make better use of the information. And people hear a lot about personalized medicine these days. But mostly, when you read about it, they're talking about genetics, talking about the genotype. Well, most of our health is not about our genetics. It's about things that happen to us. What viruses have we been exposed to or bacteria? What do we eat? How well do we sleep? Many, many things that are not part of our genes. And yet, when people think about personalized medicine, they don't usually think about that. And I think that's something we could fix. And time series data. As all of us in this room know, first and second derivatives can carry a lot of information in them. And yet, very often in medicine, people focus on the current state and don't pay enough time attention to the derivatives. Well, we'd like to improve the quantity and quality of the controllers. It's not obvious we can improve the quality of clinicians, at least not dramatically, because clinicians are people. And evolution is a very slow process. People do not get smarter very quickly, or better eyes, or anything else. So really, if we want to improve the controllers quickly, we have to rely less on people doing the control and more on machines and software, which we can evolve very quickly. Furthermore, if we think about scaling health care, today in the US, we have the same number of caregivers per 1,000 patients as we had in 1970. Yet, we have an aging population that's in far more need of care than we had in 1970. We're not going to fix that. Certainly, anyone who reads the paper and looks at the economics of health care, most of the money goes to paying salaries. Almost all the money in health care goes to salaries. We can't afford to add more caregivers, at least professional caregivers. So the only thing that's going to scale is if you have patients and family members providing more of their own health care. And so as a computer scientist, if we think about scalable systems, this is where we have to focus on the scale. All right, I want to give two examples of my current work. Now, before you laugh, I can actually count to two, even though I do computer science. I can even count to three. I know there are three things here. I'm not going to talk about Zeshan's work because I would get it wrong and he would laugh at me and that wouldn't be fun. But I've recommended it to all of you. I think it's incredibly exciting work in which he's doing work on how do you anticipate the need for an intervention, figure out who might need an intervention and what should it be. And particularly in the area, many areas, but when he was working with me, it was in cardiovascular risk gratification. But I'm not going to talk about that today. He's right over there. You saw his picture earlier. You should talk to him about it. I do want to talk about the work that my students have done in, I'm a professor, right? Professors, professors. The actual work is done by the students. So I'm going to talk about the work that my students have done in the area of medical telepresence, mostly the work of Espanja Hershey and Eugene Shea, and real-time detection of events, mostly the work of Ali Shoah. So medical telepresence as a way to shorten the loop. So here the idea is to change the point of care from the clinic to wherever the patient happens to be. And to the extent we need to involve clinicians, and I do think we need to involve them, is to bring data, not people, to the clinic. I don't know how many of you, I'm sure all of you had the experience, you go to the doctor, you wait in the office seemingly forever, and in fact, instead of looking at you at all, he or she ends up looking at data about you. So maybe we could just send the data. Bring appropriate and accurate information to the patients and empower the patients and the family members to participate in decision-making and the application of the therapies. There are many different kinds of telemedicine. You can talk about the levels of mobility, is it stationary? So for example, there's a large telestroke project from Mass General Hospital because somebody has a stroke out in the middle of nowhere, they go to a center where there's a dedicated line which connects them to a stroke specialist who decides how to treat the patient. It can be nomadic, you can imagine visiting nurses, carrying systems in a briefcase, and or it could be mobile, ambulatory monitoring as people wander around or in vehicles with some communications. There is latency, it can be non-real time, and there's a lot of non-real time telemedicine today. Most of, much of radiology is done that way. Radiologists, when they get a call in the middle of the night and no longer rushing to the hospital, they open their computer screen and look at it, at least in Massachusetts. And it's very productive. Pathology is typically, it's almost always in some sense telemedicine, but it's not real time. Or you can do real time. And there's a amount of information. There's a lot of telemedicine today goes on by text. Almost all of my interactions with my primary care physician or by email, very convenient for me. Phone is a little more, you can imagine shipping physiological data around images and video. And so these are the most demanding spaces, right? Mobile, real time with high bandwidth data. That's the area that we've mostly been working in. Our current project is designed to help very sick babies. This is not a Photoshop job. This is the real size of that baby in hands, and that's not Shaquille O'Neal's hands. Those are hands sort of the size of my hands. A baby that size is very sick by definition. Mostly they're premature. And as you can see from this chart, the number of premature births in the U.S. is rising. It's slowing down the pace of increase, but it's still up, 12.7% in 2005. This is because women are now having babies at later ages, which increases the chance of a difficult birth. It's because of the increased use of fertility drugs of one sort or another, many of which will say lead to multiple births, which are often premature. And these premature babies, particularly the ones down here at the bottom, less than 32 weeks are typically very sick when they're born. Globally, somewhere between three and a half and four million deaths per year. And typically the difference between expert care and proper facilities is a difference maker. Not only the difference between life and death, but between life and lifelong, between lifelong disabilities and not having them. We're increasingly good at helping these premature babies survive, but many of them will go through life with very serious disabilities, typically related to some sort of brain damage occurring early on. Newborn medicine is highly specialized. Obstetrics is not. So every community hospital is willing to deliver babies. But if a sick baby is born in one of these hospitals, they are typically not well-qualified to treat them. There's a hospital I've done some work with in our area, I won't name it, where they have a big obstetrics process and care for the nursery at night is provided by local on-call pediatricians who spend their days prescribing amoxicillin for ear aches and then are expected to come in at night and know how to deal with the premature infant and respiratory distress. Not a good situation. So increasingly we see a lot of inter-hospital transport. The current mode of these community hospitals is to take the baby and if it's sick, move it to a tertiary care center. They're few and far between. I know you have one in Ann Arbor, but I'll bet there are no more than three or four in the whole state of Michigan. Air transport is usually not feasible and so it's done by ground and so the transport can take a long time. Here again is a premature baby. It's actually with the hands of one of my graduate students that is his child. So there's pattern of usage we're interested in is the baby is born in the hospital, put in an incubator with communication capabilities, evaluated by specialists at the remote site, decide whether transport is necessary. If that transport is necessary and this is the way it's done today, a truck and I use the word advisedly, these are big vehicles. It's sent from the receiving hospital. The baby is transferred to the truck, transported back. The baby is at high risk during the transport and so we've been focusing then on how do you provide monitoring at the receiving hospital during transport. Legally, as soon as the baby is loaded into the vehicle, it becomes the responsibility of the receiving hospital, even though the physicians there have never seen the baby. So there are three parts of the project. Picture here of one of the more scenic hospitals that were in our program, an instrumented incubator, a physician friendly application, or at least I hope so, and most interestingly, technically the communication subsystem, which is really what I wanna talk about the next few minutes because it is the technically fun part. The goal is to provide sustained high throughput, low latency in a way that's economically viable to deploy, operate, and maintain. This is very important. There's been a long history of telemedicine projects which get built, they do a really slick demo, and if you go back three years later and say how's it doing, they say oh, it doesn't exist anymore. In fact, it never existed post demo because it was not viable to actually deploy, operate, and maintain. Something changed and the whole system stopped working end of story. So, if you think about this, what are your network options? There are people who've done this by creating their own networking infrastructure. It's just not sustainable for a hospital or any medical system to do that. So, systems that have done really nice demos with satellite networks and things like that don't ever get used. Well, in the hospital, you can probably use something like Wi-Fi. Today, almost certainly most of them. That doesn't work in the vehicles. Anyone who's tried to do Wi-Fi from a moving vehicle knows how kind of hopeless it is and people want afterwards, I'd be happy to talk about the technical reasons why that's a problem. So really the only option is to use cellular networks, the wide area wireless networks, at least during the actual transport. Well, the problems with this, there are multiple problems. When we started the bandwidth was very low, it's now higher, but it's still low and in particular, we're interested in the upstream bandwidth from the vehicle. Most of these networks are optimized for the downstream bandwidth. Therefore, the iPhone user to download YouTube videos, not to upload the YouTube videos. So that's an issue. There's lots of packet loss. I'll talk about that in a minute. And of course, anyone who's used a cell phone knows you do lose the connection from time to time. So what we've done is we've multiplexed the streams, the video streams in particular, over multiple channels, up to nine. We've used it a single time. This of course gives you greater average aggregate bandwidth because you get to aggregate the channels. Network striping for those of you who work in that networking area. You get a dramatic reduction in the variation in bandwidth. There's sometimes 18, sometimes not often, but sometimes AT&T is working well. Sometimes horizon is good, sometimes sprint is good. So having variety gives you, reduces the variation in the bandwidth and the round-trip times and the losses and get increased reliability when we run our system loss of ways. So the trick was to build it around middleware that made all of this work over a sustained period of time. So the middleware manages the multiple connections, takes continuous measurements of round-trip time and bandwidth, other things of the network and adapts accordingly. So decides which channels to put the data over based upon measuring how the channels are behaving. Hides most of the details of this from the application layers, but not all because some of the adaptation, as I'll point out, can only be done intelligently at the application level. So if you abstract all of this away, then you won't be in such a good shape either. So here's the architecture. There's the patient node and then there's a whole group of specialist nodes. This is a slightly outdated picture of what the user interface looks like. That's not a real baby. That's not a real doctor either, so, it's okay. So we've got video, we've got audio, we've got telemetry. They go into our middleware. It gets shipped over multiple channels, gets picked up by the backbone network and then we assume that the specialist nodes, for the most part, there's a broadband connection. Everything gets reassembled and gets put into the user interface. So we want to send the highest quality real-time video possible. The horde and slash tribe levels provide sufficient aggregate bandwidth on average in most areas. When we go through a long tunnel, bandwidth is not adequate. But the real issues are that the channel throughput is highly variable and the packet loss rate is highly variable. So to mitigate the effects of the bandwidth variation is where we use dynamic feedback with the application. Horde tells the application not anything about individual channels but the recently available aggregate transmission rate. The application then adapts to this as appropriate. So it could adjust the quantization parameter. It could adjust the frame rate. Could adjust the latency. Why do we care? Why would the application need to know? Well, if, for example, the physician at the other end is trying to determine whether or not the baby has a seizure, is having a seizure, they don't need high resolution video, but they need smooth video. They need to be able to understand the movements. On the other hand, if at the other end the physician is trying to examine a lesion to see what it looks like, they may not need video at all but they may need as a really high resolution still. And so there's no way for the networking layer to make those decisions as to what can be traded for what. So we have the physician indicate in an abstract way of what they care about. Frame rate, latency maybe is important. Resolution, they speak in that language and then the application takes the information from the networking layer and makes the appropriate adjustment. The other problem I mentioned is packet loss. We've measured it and these numbers are in the Boston area. When our notice station area, we get up to 2% packet loss. When the node is moving, let's say 60 miles an hour, we get up to around 10% packet loss. That's a lot of packet loss. Why do you get them? Well, when you're moving, because you have base station handoffs, you have signal occlusions, all the usual kinds of things. Because this is real time, we can't just use TCP or something and use retransmissions. So in fact, we don't retransmit anything. If a packet is lost, it's lost. Because we're often throughput limited, we can only do a limited amount of forward error correction. We do some, but less than one might and as throughput gets better, we'll do more. But effectively we have to somehow contain the effects of a lost packet. And we do that with the way we encode the video. Each frame is segmented into a grid of subframes. And then each subframe is encoded and decoded independently. The issue here, I don't know if any of you or I'm sure some of you, some of you probably haven't done video encoding, you've typically got iframes and pframes. And the iframes include a lot of information and the pframes are small and include deltas from the iframes. The problem with this from a networking point of view is every time you send an iframe, you're slamming the network. You're putting 20 packets on the kind of thing, all in a burst. So what we do is we split the image into small frames. Typically eight seems to be empirically a good number. Each of which is encoded independently, out of phase with each other. So sometimes you're sending an iframe for one piece of the grid, sometimes an iframe for another piece of the grid. And what this does is it takes the burstiness out of the transmission and is now sending a much smoother, less bursty stream of packets. If furthermore means that if a packet was lost, we're losing only a piece of the image rather than the whole image. And the good news is in our empirical studies, the pieces we typically lose are not the baby. It's usually some, there's a little sliver of the window where something is moving quickly, where things are changing fast. That's what our node looks like. It's quite an interesting thing, the mobile node. This is a really, it's not just an incubator. It's got a respirator, this has got air tanks in it. Image is a little hard to see. A lot of it is not our stuff, it's what comes with it.  There's a camera here looking at the vital signs monitor. Believe it or not, there is no data output on this device. And so this very clue-gee thing of pointing a camera at the screen, capturing the image, and then trying to reconstruct what the data really is.  And then I realized it was wonderful because I didn't have to change anything. Whereas if I had known what their proprietary encoding of the data was, I'd have had to recode something. See, everything still worked. So it's a pretty, it's a silly, but it's a pretty robust mechanism. I guess it's why fax machines still live. And then there's a really fun part, which is the laptop computer, and all the ports for the modems, et cetera. So I'm just gonna show you a less than one minute video of not the system in actual action, but just to give you an idea of what it really looks like. Oops, I'm back. Maybe I'll show you a one minute video. So here we are loading it into the truck. The woman there is actually a new natal intensivist, and you can see why we call it a truck. It's really big. It's got room for up to two babies. This is me pretending to be a doctor and Monica Kleinman, who really is a doctor, operating the system and checking out the health of this doll. Here's a picture of the Vital Signs Monitor. The doll did not have a very good heart rate. Here we're zooming in to look at a lesion on the doll's face. Interestingly found, they do a lot of instant messaging back and forth between the hospital and the doctors. And just to show you what it really looks like, here's a real baby, not a sick baby. And this is a real image taken from the system in operation. So you can see it's pretty good quality video to be sending over the real time over the cellular networks. Here's one more one, and this one I'm just showing you so you can see my enormous linguistic skills. Now I can go back in again. Good thing I'm a computer scientist, right? Nope. Well, maybe you won't get to see my skills. You don't believe that's my voice? We get a lot of the funding from companies in Taiwan, so I produced a version of it in Mandarin, which, anyway. All right, just to show you what we get from the aggregation. These are, this is old data, but this was from an experiment that I did. So I'm going to show you what it looks like. So I'm going to show you what it looks like. But this was from an experiment where we had two Verizon lines and a Sprint line. And what you can see is that the mean for each of these is about the same. And when we aggregate them, we don't quite get three times the bandwidth, but close to it. So in fact, the aggregation works very well. I haven't shown you, I won't show you the data, but there's also a lot of data showing that the packet loss is typically, in many ways, uncorrelated on the different channels, which is also a good thing. This is just to show you, we spent a lot of time worrying about robustness. So here we were running an experiment with, I think, five cards. And, whoops, we removed the cards one at a time. These are the modem cards. And as you can see, it degraded nicely. And the nice thing is, this was all done automatically. This was to simulate dropouts and things. And it all worked quite smoothly. And during that period, this is the same period down at the bottom, the signal actually was reasonably static that we were receiving. We had enough bandwidth at 200, we were okay. Where is the project? We expect to actually deploy it for real use. This calendar year, let me emphasize the word expect. The IRB has been in progress for six months now. Why am I showing you this picture? Well, one of the first things that the IRB came back is I got an email saying, you don't talk about what you're gonna do about crashes. And I responded, my software isn't gonna crash. And they said, no, no, no, no, no. That's not what we mean. We mean this kind of crash. How are you gonna ensure that you're, for example, computer doesn't fly up in the air and hit somebody in the head? And we actually had to spend a fair amount of time doing a very different kind of crash proofing of the system. Apparently these vehicles have accidents fairly frequently. There is, of course, room for improvement. We can improve the UI. In particular, the collaboration tools. Our original model was wrong. We thought of it as point to point, but it's actually point to many points. The physicians often wanna call in specialists to come in and help them do things. The specialists might be sitting off and looking at things on a phone, for example. So we are working in collaboration tools. And we're looking at adding better and more kinds of sensors. Wireless cameras, pressure pads, ultrasounds. And as I mentioned to some of you, also, analysis and enhancement of the data. We think there's a lot of information that can be extracted from the video and analyzed by machines. All right, so I'm finished with part one of the talk. I wanna move on to part two. Totally different way of closing the loop. So one there, we sort of close the loop by getting humans in sooner. Now I wanna talk about closing the loop by getting humans out of the loop altogether. So it's about epilepsy. It's a disease of the central nervous system. Results in recurrent seizures. A seizure is a transient disturbance in the electrical activity of the brain. And it's important to say, for it to be epilepsy, the origin of the seizures has to be the central nervous system. There are other ways you get seizures. For example, cardiac induced seizures. Those are not what you think of, typically, as epilepsy. It's astonishingly common. Prevalence is somewhere, maybe, in the neighborhood of 1% of the population. Increasingly, it's becoming a problem in elderly people. About two-thirds of the people with epilepsy, the disease is well-controlled by pharmaceuticals, drugs. But about one-third have uncontrolled seizures despite daily treatment by multiple drugs. And that's the third that we're really focused on in our work. So here's what a seizure looks like. Pretty horrific. And even more horrific, I'll tell you, when you see these things in person. Yeah? I have a really big question on the point you made in the previous slide. So that epilepsy is more common in people over the age of 65 now? Is it that... No, I didn't. Well, it will be. Increasingly more common in people over the age of 65. So is it because you are seeing better survival rates in younger people, so that people with epilepsy continue to live as a society? Yeah, I don't have a good answer for why. Epilepsy is, in many ways, a description of symptoms rather than a disease process. There are lots of different causes. Some of it can be inherited. For example, ion channel deficiencies and things. But a lot of epilepsy is caused by injury, some insult to the brain. Most professional football players will at some point probably. We're gonna see a lot of epilepsy from these people from the wars who've been exposed to blasts, I think, where there's been an insult to the brain. So, but the answer, I don't know why. So this is, not all seizures are this bad. Some are very mild. They're so-called absence seizures, which are just loss of attention. The key thing that makes seizures bad is that, well, obviously having this is bad, but they are to the patient unpredictable. And so you can imagine if this young girl had been riding a bicycle when that seizure struck. The seizures are usually self-limiting, not always. And they'll just stop. If not, some patients go into status and then you have to intervene, but most of the time they'll stop. But what terrifies most people with epilepsy is not the seizure itself, but the collateral damage. That they'll fall down a flight of stairs and crack their skull. Everybody who's had epilepsy for very long has the battle scars of multiple injuries. About one in every 100 patient years, there's a something called SUDEF, sudden death in epilepsy patients. Nobody knows what causes it. And it's carefully phrased that sudden death in epilepsy patients, because they don't even know it's caused by epilepsy, but they'll die. That's pretty frequent one in every 100 patient years. And typically it's not from the seizure, it's from something related, right? One of the patients we've dealt with was a young mother had a baby, she would not pick up her baby. She had about less than one seizure a month and it kept her from picking up her baby, because she says, well, suppose I have a seizure when I'm holding the baby. It was very sad, right? So if you could warn her and say, you're about to have a seizure, and even if you could only give five seconds warning, it would be life changing for these people. So that was sort of a thing that really motivated us and got us excited, that we could actually, if we could do very early detection, we could really ease the burden on the patients and on the family members. There are also therapies, which there is reason to believe, and I'll talk a little bit more about this, if it's delivered in a timely fashion, can actually maybe abort or attenuate the seizures. Typically electrical stimulation today, but there are some pharmaceuticals under development. And as I mentioned, the alerts could make a big difference. The main question is, can we detect them? So you probably couldn't see it so quickly, but next to the gurus, having a seizure was a EEG recording, scalp EEG. So this is a technology for recording the electrical activity on the surface of the scalp. Not to be confused with the electrical activity in the brain itself, but indicative of that, yeah. Okay, so they're so unpredictable. So how did they get the scoral on, they had some outbreaks, she's gonna have a seizure at some point, right? Well, so we have videos we've made of these, and it's because typically we'll have the patient on the video for days at a time. Okay, so it didn't happen to be, that was the only time she was doing this. Now, typically what often happens for a lot of the things you see is patients go off their medications, they have them more frequently. And so if there are reasons I won't get into, why for periods of time, we take the patients off their medications, the doctors take the patients off their medication, and then they will have them more frequently, but we just happen to be counterpointed at her at this moment. Most of the time, there's no seizure. They are not that frequent, goodness, at least usually. Yeah, that's true. All right, so that's a record recording. You might think this is the seizure over here, but it's not.  But we'll look in the little more detail at one later. The thing to think about is there are two kinds of seizure onset, clinical, where there's some visible symptom, and electro-graphic, where there's some change in the electrical activity of the brain. By definition, electro-graphic precedes clinical, since it's causing the clinical. That doesn't mean that we can see it necessarily in the scalp electro-graphically, because if it's in some deep part of the brain, it may not be visible promptly on the scalp. But for the most serious kinds of seizures, you can see it usually pretty promptly on the scalp. The EEG signal varies greatly across patients, and for this reason, the generic detectors have not worked well. Every EEG machine that gets bought gets shipped with a detector. All the hospitals I know turn them off, because they get so many false alarms that they're not very useful. Seizure-specific detectors work better. There's been some work mostly out of Germany in this area where if you say, this patient has seizures of type L or B or A, whatever, and you essentially hand code for that kind of seizure into the detector, you can do a better job. It requires considerable medical expertise to tune these to the patient, and it typically works best on typical kinds of seizures, the kinds you see all along. The good news is that while the EEG signal associated with seizures varies greatly across patients, it's pretty consistent for an individual. So a person with epilepsy will have a very small number of stereotypical waveforms associated with their seizures, most frequently one, but sometimes a other small number. That's not to say everyone is identical, in fact, no two are identical, but they share certain common properties. And this led us to the notion of dealing with patient-specific detectors, saying it doesn't have to work for more than one person at a time. If we could use machine learning to automatically construct a detector for each patient who needed one, we would just use that. And the question was, could we do much better? So the paradigm is pretty simple. You get signals from the brain, they go into some sensors, and we've been looking, today I'm gonna talk about scalp EEG, we've also looked at intracranial and deep electrodes, then extract features, and then build a patient-specific classifier. So for all of you familiar with machine learning, this is not a surprising paradigm. I won't have a chance to talk about it today, but we've looked at other sensors as well as the brain. There's often tachycardia associated with seizure and onset, that's to say, unusually rapid acceleration of the heartbeat. And so we found we can actually have many fewer false alarms if we look at both the heart and the brain simultaneously. The features are spectral, spatial, and temporal. The classifier we've been using is an SVM-based classifier using a radial basis kernel. If you don't know what a radial basis kernel is, don't worry about it. The key in the whole work was nothing to do with the choice of kernel or the SVM. Though the SVM is important because we have a huge class imbalance. We'll get 24 hours of recording during which we'll have maybe 20 seconds of seizure onset. The rest of the data points are not seizure onset, pretty big class imbalance for those of you used to using machine learning. SVMs handle it better than most. But the real magic sauce that Ali invented was the features. So if one looks at textbooks, they typically will talk about the spectral characteristics. Interestingly enough, most people who don't study this think that the activity associated with seizure is chaotic brain activity. In fact, it's just a reverse. Most seizures, what you see is excessive synchronization. That the things are too well organized. And so, but you see things happening in different bands at different time scales. So you have to extract the features and you can do this doesn't, we happen to use filters, we experiments with wavelets as well. Filter banks, wavelets, this seems to make not much difference which you use get more or less the same performance. We do that from each channel. In the scalpy EEG there are typically 18 channels if you have a full montage. You don't need a full montage. Eugene, she has done a lot of work showing that in fact, for a lot of patients we only need one channel, which is really convenient. But we start with those and then we look at them over time and get this vector in each frequency band in each channel. So eight frequency bands in channels. We've worked for a long time with this which is what most people work with, something similar to this. And then we discovered that it worked a lot better if you have temporal features as well. Because what we wanted to detect, what we discovered was useful to detect was the evolution of change. So for example, remember whether I ultimately left it in? No, I didn't. I had an EEG, I was gonna show you of a patient where that patient's seizures were marked by first a spike of sort of broad spectrum noise almost it looks like and then an electro decrement where the brain slowed a little bit and then some theta waves in the theta band. If the patient had only the theta waves it was not a seizure. If the patient only had the decrement it was not a seizure and the patient had spikes all the time. So what was interesting was to learn this sequence. Now we didn't want to hand coat it because there are lots of other sequences and the goal was to do no hand tuning of this. So we ended up aggregating the initial features into a different vector where we recorded them over six windows. And that was ended up to being the magic insight that Ali had that made things work. I wanna get to the results. This has been tested extensively. I'll report today on some results from a children's hospital MIT database which is, Ali did it at great personal cost but it's now available freely to anyone who wants to use it on a fissionette. Nine hundred and 16 hours of continuous EEG all from, well 23 pediatric and we had one adult patient. Story I won't go into. Typically 33 hours of recordings, five seizures per patient divided into one hour records. We did the testing, leave one record out and I wanna take just a second to emphasize the importance of this. As I got into reading the medical literature, I became pretty disgusted with the design of a lot of these tests. Not so much in the big trials but in the little tests that people would do for things like seizure detectors. In that they would, people especially who did learning would ignore the fact that these signals are correlated in time. And if you train on data that's temporally proximate to your test data, you can get really good results. Almost all of the things you wanna read in the literature are done that way. So I just caution you as you read this literature to be cognizant of that form of cheating. We tried not to do that, which is why we wanted to leave out a whole hour long record. So all the training data is pretty far removed from the test data. All right, so here's the sensitivity on this group. These are the children's groups, the children, the pediatric patients. We detected 96% of the seizures. Remember we only had on average five seizures per patient. For some patients we had only two so we had only one training seizure. What you can see is that the missed seizures occurred in a very small subset of patients. And so in fact, the good news is if you can do some testing, it's all right, we're just not gonna give these patients or a detector cause it doesn't look like it works for them. Most of the patients we have perfect sensitivity. Unfortunately sensitivity is not the hard problem in many ways, it's the positive predictivity. Here the number of false alarms per day on the median is two per day. Now, and as you can see again, it's highly variable across the patients. You could say, well, what good is it if you have two false alarms a day? Well, I could say compared to the conventional detectors which have order of magnitude more, it's better. But maybe if twice a day this mother were told this would be a bad time to pick up your baby, it wouldn't be so bad for her. If she were confident that it had perfect sensitivity, two false alarms, each of which lasted a minute would probably be perfectly acceptable to her and transformational to her life. So one of the things you think about these things you always think about how it's gonna be used and what could be the impact. How far ahead of the clinical seizure did you predict? That's a really good question. Again, it's highly variable for some patients as long as 30 seconds and for some patients after the clinical event. So again, but we can sort of tell. It's pretty consistent. It will be the same gap from seizure. So not every patient will be able to do this. You'll have to qualify the patients. Yeah, here's the latency, by the way. I'll get to you in a second, just a minute. So this is the latency measured from not the clinical event but from what we had is we had an epileptologist would detect the clinical, we find the clinical seizure on a video, typically. They would then go backwards on the EEG to find the earliest moment in which they saw what a change that they thought was related to the seizure and we declared that to be the electrographic onset. And this is our latency from that electrographic onset. The notion being since if only we're looking only at that information, that's sort of the best you can do. How much time is there generally between the electro onset and the highly variable? There's no way of knowing. And again, remember, we're measuring only what we can see in the scalp. So there are people who if the origin of the seizure is on some deep structure, may have some clinical event and we might never see anything on the scalp that looks like a seizure, at least not for a long time. Other people, it will, if the origin is close to the surface of the brain, you'll see it typically very promptly. I can't actually give you a real, a good answer to you. It's the right question, but I don't have the right answer. The question is better than the answer. All right, there's the latency. Here's the rate of learning. And here you'll see the latency in seconds and the miss rate. And you can see we clearly train very quickly and starts to flatten out. We've consistently seen that after three seizures we're typically not getting much. And the good news is we've had patients come back a year later and tested them on the data trained and it works just as well. So it seems to be pretty stable across time for patients. This is a comparison to what I think is probably the best commercial product. This was also built using Malik machine learning, using neural networks, something called Reveal. They trained on thousands and thousands of patients and then built a non-patient specific detector. There are parameters you can adjust. I should say this is not designed to be an onset detector, but a seizure detector. The parameters of L and T, L is the latency, how long it waits before it declares a seizure. So it needs to see 20 seconds or something it thinks it will see, think there's a seizure. And T is the probability of it being a seizure is 0.9. So you can set these to anything you want. You get the trade-offs as you would expect. If you make L bigger, sensitivity goes down. If you make L smaller, specificity goes down. Lots of different things. We picked this and you'll notice with even a very long latency, well it's a long latency here. Detected 93 out of 152 in the same population. We got 147 out of 152. Big difference there and a lot of difference for patients. But the more interesting piece is the specificity, same parameters. So we win here on sensitivity. You say, well, you cheated. Maybe you should have given them parameters that would improve their sensitivity. But if we do that, their specificity becomes impossible. As you can see, even here with those parameters, we have two, their median was 14. Our average was four, their average was 40. So there's a huge difference and it gets to be unusable for most of these patients. So the notion, the point I'm making is not that we have the world's best patient-specific detector. I think we do, but the state doesn't prove that. But the patient-specific detectors are gonna way outperform generic detectors. Do the patients follow your clusters in terms of the patterns they exhibit? We, with 23 patients, I don't know. I would expect they would, but we couldn't tell you that on the basis of the number of patients we had. Just sample size was too small. Very quickly, I wanna talk about one application. This is a vagus nerve stimulator. This is a device that gets implanted under the clavicle attached to the left vagus nerve. Put the longest nerve in the human body, by the way. Puts a stimulus on it. It's kinda like a diode in the nerve. The current flows up into the brain, causes chemicals to get secreted. It's been shown that repeated stimulation of this nerve can, in many patients, produce the average number or severity of the seizures. It's been shown in rats, in case you care about the life of rats, that if you turn the stimulator on at the onset of the seizure, it has a high, very good chance of actually aborting the seizure, stopping it in its tracks, or at least reducing the intensity and duration. Very exciting if you happen to be a rat. Nevertheless, there have been tens of thousands of these implanted, mostly for the repeated effect. But everybody who gets one is given a magnet and said, when you're having a seizure, hold the magnet over it, it will turn it on and maybe it will help. So about 70% of the patients who get these report using the magnet. Either the patient or sometimes a parent. One of the mothers we talked with wears her magnet as a necklace. And whenever her child is having a seizure, runs and holds the magnet to turn it on. 20% of these people who do it report that when they do it, it stops the seizure. 30% report that attenuates it and 50% say, I keep doing it, it doesn't help. Don't get fooled into thinking that this means that it helps 50% of the people. How do they know it attenuated the seizure? They don't. There's no way of knowing how long it would have lasted since the durations are different. And in fact, even the termination, people say, well, I think I was about to have a seizure and I used it and I didn't have a seizure, it worked.  So who knows? So we built a system to do it with a closed group control, stuff in the boxes of stuff we built. We acquire the signal, we use our detector, we energize an electromagnet, turns on the stimulator, voila, the patient gets the kind of klugey-looking hardware. Is this an ambulatory system? No. But it's good enough that we can do a lot of in-clinic tests, which we're now doing. And it reliably works. It reliably detects the seizures, reliably turns on the stimulator. Here's an example. Here's a patient, that's the electrographic onset. This is where we detected the seizure and over here the spike is the VNS getting turned on. And this, you know, we see this over and over again. As you can see, by the way, it's not obvious to the eye that this is where the seizure starts. So it can be kind of subtle. Does it help? I don't know. Worse, I don't even have an opinion. You know. But I will in about a year. We have a study in progress. We've got patients lined up. We're running studies where we're sometimes turning it on, sometimes not turning it on. We'll actually have real data. And maybe in a year from now, I'll be able to tell you whether it works or not. Okay, wrapping up very quickly. I can skip most of this, but it is getting late. And I'll just get really to the bottom two points, which is that personalization is critical. You really need to find some way to use learning to build a model of the patient, to know the difference between what's normal and what's worrisome. And it's just, I think, a great, almost virgin field of research opportunities for CS and EE researchers. And with that, I'll thank you for staying beyond five o'clock. So I'm happy to take questions. I'm also happy to turn my back so people can go out of the room without me. Yeah. So in some cases, your epilepsy patients have like auras, or your sensory hallucinations. Can you use partial seizure? Definitely seizure, but it's kind of a step towards it. That would give you more training information. Can you use that on those patients? That they know they've had an aura? So auras are an interesting thing. So there are patients who, for example, will smell something in advance of having a seizure or hear something, or various kinds of things which are called auras. And for the lucky few patients that those occur reliably before seizures, they're not so unpredictable. Most patients do not enjoy that privilege. Now, what's going on with an aura? Again, this is speculation, but the speculation is that in fact, it does mark the onset of the seizure. And what's happened is, for example, there's some seizure activity in the neighborhood of the part of the brain that related to smell that has not yet reached the parts of the brain that deal with motors, motor cortex. And so, yes, there's a seizure, but there's no, say, effect on muscles yet. We could certainly use that for training. Now, the problem is most of these patients with epilepsy have abnormal brain activity all the time. Their EEG, their baseline EEG looks nothing like the baseline EEG of most people. And they frequently have a leptiform discharges that do not evolve into seizures. And frequently have therefore things like auras that do not eventually evolve into seizures. And the difficulty, a big difficulty in our work has been not generating false alarms on these discharges that are not actually going to turn into seizures. Now, it's arguable in the literature whether these are, quote, seizures or not. Right, and if you look at a outpatient epileptologist, if you talk to them, they will say, if there's no clinical event, it's not a seizure, I don't care what the electrical activity looks like. If you talk to someone who works in an ICU, they will tell you, no, the electrical activity is what determines whether there's a seizure. So there's, the end point is not at all clear here. And what you choose to train on is going to depend upon your definition of what it is you want to learn. Do you want to, for example, fire the stimulator in every discharge or not? So I think that's a long answer to a short question. Yeah. I want to say something about the experience of computer scientists working with medical doctors. How does that kind of feel? I like it a lot. Now I'm very lucky that not all of them, but most of my clinical collaborators, medical collaborators have been really a pleasure to work with. I've chosen to work with people who all do some clinical activities, so these are not bench scientists but care providers. And they care deeply about finding ways to improve the health of their patients. And it's kind of inspiring sometimes. It sounds trite to say that, to actually talk to people who are on the front lines. And it's been wonderful, you know, it's wonderful to be able to meet patients. I think, geez, I could maybe someday help this person. Now I choose my collaborators carefully. For the most part, I like to find collaborators who have a background in engineering or mathematics or physics. And there are a lot of doctors out there today who had the wisdom to do that as an undergraduate. Because really what I want is collaborators. I don't want them to throw clinical problems over the wall to me and say, come back when they're solved. Nor, you know, I really wanna be able to sit there and say, we can't, you know, sit there and say, we're getting all of these problems. What can we do? And the doctor says, well, if you thought about the fact that there are these patterns that maybe we could look at. And it really is very helpful.  Well, none of them can answer that off the top of their head. But finding a doctor who is willing to take an hour to listen to you explain what that means. And I've been able, for the most part, to find collaborators who really wanna do that kind of thing. So the downside I've found is it's easy to find people in the world of medical research, more than clinicians, who are so obsessed about the number of publications they have that they treat data as a proprietary asset, rather than something that could be useful to the community. And so when you run into people saying, I don't wanna do any work with you, I'll give you a little data and I'll meter it out a little bit at a time. And you have to promise to put me and my dog and everyone I've ever met as co-authors of whatever paper you write. So to a computer scientist is kind of a foreign culture. But on the whole, it's, for me, been a great experience. And I really do value my clinical collaborators a lot. You mentioned that you get funding from Taiwan, is NIH not so willing to fund experimental kind of clinical work? Well, I'll tell you in a month or so. So initially, the problem with NIH, right, is I had no track records. And typically, NIH likes to fund people with track records. And so we had to actually get some publications, some data. An NIH proposal, unlike an NSF proposal, typically has to have preliminary data in it. Pretty convincing preliminary data. And it was only recently that we've been able to write a credible proposal with enough data. I have two proposals now pending with NIH. So is it worth a hassle compared to getting a guided source like that? Yeah. You know, I was able to hassle. There was a tremendous amount of boilerplate that goes into these proposals. I'm sure you're all familiar with that. But on the other hand, unlike if it comes through, it's for five years, which is a wonderful thing. And typically renewable. Narrow one. Yeah, Narrow one. So I've got one of those pending and got another one pending. So I'm optimistic. It's an issue. It's been an issue with publications. You know, the stuff that I find really the most interesting in our work is hard to get. The medical literature doesn't so much care about it, right? They don't want to know about this wonderful algorithm that you've invented. But we'll see. So I've been able to... Privates funding, I guess DOD funding. The DOD is pretty interested in brain injury right now for obvious reasons. And that's mostly where it is. Yeah. Yeah, so when it comes to publishing, so you've only been working with commissions who don't care about publications. Oh no, they care. So when it comes, do you publish an article in a computer science conference and then also in their journal? Right, so we've been following a dual publication strategy where we'll typically write an engineering paper that has all the beautiful technical material in it.  And so we've been following the strategy of dual publication in those two venues. We've been, it's been a while. I mean, for me, I was a computer networks guy before I did this stuff. So I didn't, you know, not only did I have no credibility in the medical world, I had no credibility in the machine learning world. So, you know, it's been a little ramp up, but this year we've got papers in ICML and NIPS. So I think we've sort of gotten over the hump. For what it's worth, medical decision making is a nice, can be a nice little room between the exclusively technical paper and the exclusively self-indicated paper. So, because they will care to a certain extent about the technical aspect. I guess more if you have data, you know, more of a well-defined data analysis and methodology like they would call it that you could nail down it. And that gives you some sort of middle ground if you're looking for something that, an audience that would appreciate it a little bit more. And we've done some of those things. And if they're Amy, you know, Jamie is kind of the middle ground and stuff like that. Journal of the American Medical Informatics Association will take some technical, but not overly technical. Yeah. Yeah. The two sort of medical areas that your project's talked about were very dramatically different, right? Yes. In terms of what part of care they address. Are there any portions of medicine that you view as like especially right for computer science intervention where you just pick up the problems or you find them? So a combination of picking up the problems where I find them and where I think my students have some ability to contribute, right? We're not, I'm not a biologist. My students are not biologists. So I have studiously avoided those problems that require deep biological insights or understanding, right? Not because they're unimportant, but because they're beyond me. So that, that's one thing that's just things, a lot of important things we're just not going to do because we couldn't. There are things that are right.  But if I look at the cardiac work and the epilepsy work and other work, we focused on areas where there are either signals because we know some signal processing and electrical signals especially were good or large amounts of data. And you can really do data-driven kinds of things. One of the things I didn't mention though when I talked about my clinical collaborators is I don't want to get carried away with the statistical aspects of the work. And we do spend a lot of time with the clinicians trying to understand the physiology and seeing if we can put together physiological explanations of what we're seeing, right? So that there's some underlying reason to believe that it's not just some statistical aberration but something that makes sense where there's some physiological model that at least plausibly fits the data that we're seeing. Areas that I think are tremendously right, it is especially for the brain work, it is everything related to psychiatry. That there's so little engineering, if you will, that goes on today in those kinds of diseases which are very important. And I think if you could find objective measures of things like schizophrenia, is there a difference between when people hallucinate that they're hearing something and they hear something?  What happens when you give people these various drugs? How does it change the electrical activity? So I think because this stuff has just not been studied and say to the same extent that cardiology has been studied in a scientific way, I think there's a lot of interesting activity to do in various diseases of that sort. I'm interested in moving into various neurodegenerative diseases where again I think there's a lot of interesting things that we can measure and look at particularly what's going on. We do early detection. So I've been focused on things where early detection is valuable. But there are lots of things, right? I probably would avoid orthopedics, which I think will belong more to mechanical engineers than to computer scientists. But the other thing to look at today is a proliferation of devices of one sort or another. The neurostimulation devices are just exploding. And they're exploding in ways where the control systems for them are very unsophisticated. People implant ICDs. And they really don't have a good understanding of when they should be energized. Yeah, sure, if the heart stops or go into VTAC, you should turn it on. But are those the only times you should turn it on? Does it always have to get turned on full? Could it get turned on partially? None of these are questions that people know the answer to. And I think there's a lot of stuff that we can do. And in general, I think a lot of it will be for us is stuff where personalization is important. Because that's the stuff where you really need computation. Because it's just not possible to have an expert tune these devices to each individual. So I think anything where there is personalization is a great opportunity for us to look at. And I like brains and hearts. They're important going against each other. So do you want to have some cash plates? Well, I think I'll take one more question. Does any of you have any? I have to teach tomorrow morning. All right, Phil. Thank you. I get to talk. Thank you.


Do I have to taste it? Yes Is that character wine or is it a flaw? We're gonna talk about it in this video Hello, hello, hello welcome back to exotic wine travel the show that helps you drink adventurously So you can expand your palate and expand your mind. I'm your host Matthew Horky thanks to the team at Vin Natour in Italy an organization dedicated to The quality improvement of natural wines for sending us a wine faults kit We did a online tasting with some of the founders organizers have been at Natour last time and we're going to retaste them here for you today The first wine is tainted by ethyl acetate and acetate acid Acetate acid is more of this vinegar sort of smell and you can really taste it Some people may think that acetate acid actually brings a sense of freshness to the palate But with excessive amount is definitely not going to make the wine taste pleasant A lot of people confuse like ethyl acetate and acetate acid as just volatile acidity, but actually they're two quite different things It's so bad in the morning. I cannot. Oh, I like tissue paper Oh Get a mousiness now The next one we're looking at is a wine tainted by manateau, which is caused by lactic acid Usually that happens when you're starting your second fermentation when the sugar level especially fructose is very high What you get is a dislike sort of like a ropeiness in the palate It's kind of thick and viscous on the nose You definitely get a very strongly lactic a little bit cheesy kind of smell to it. It is bearable three beet root not awful For the first two red wine we're looking at Retardomizes however, a lot of people may not know that protonomizes they're actually different strains So we have for EP for EG and fall easy We won't look at fall easy today We look at fall EP and fall EG the first one that we are tasting has fall EP Which is much more towards the sweeter side of thing But it was not kind of like sweaty like animal like whereas fall EG is actually more of a spicy No, but at the same time It's also very overwhelming in higher amount and covers all of the fruit flavors This is why I don't understand about people who don't mind drinking faulty What it smells so bad that your your body should be telling you don't touch that stuff is disgusting This really smells like band-aid Five literally tastes like I'm licking a band-aid that I took off myself It's a rebel this morning. I thought this was smoke-tasting the tasting. It's not good, but it's not awful The last one we're looking at is acid elder high. Oh my god. I can't stand that It's it's a very strange smell. You know, usually with acid elder high It also comes with a little bit of ethnicity So what you get in low amount of that is actually quite class and like what you get in Amarone You know, like this sweet cherry smell but in high amount.  Oh once you had it now that you will never forget it This is the worst one tastes like homemade wine bad homemade wine. Did you like that? I was to do it again So what do I say about wine faults? I Actually am not so keen to use the word wine faults I kind of prefer wine flaws, but at the same time it's still really not quite the right term Not even defects because I think that like a lot of a lot of those things that we taste they always Almost always come in a little bit amount in every single line So it's just really about how much they have how much each wine has that's sort of like defect and how balanced it is That's it. Um, I think what I learned was quite interesting was that that was the first time I tasted a wine deliberately tainted by 4 e g and 4 e p and then put side by side So that was very exciting because usually you get over right when it's just better than I said So that was fun. Yeah, I you know, what one man's fault is another man's treasure me Because I I'm more tolerant of things like bread than you are and a little bit more volatile city and natural wines You know just because they're you know classic faults different people are you know more tolerant of the other I think the one I learned most from was the bread that do the two different types of bread e p and e g Uh, seeing the bread doesn't we think of bread is just horsiness of leather of like old armpits But it can taste different in a lot of different ways and also for monitor as well I mean actually with a little bit of that amount, right? It does create a little bit of this Mami quality to wine so again exactly like what you said some people like it some people don't However, I still think it's very important to identify all of these things so that you can You can look at it in the eyes and like you figure it out, right? Do I like it or do I do I not you can only make real judgment when you actually can identify that it's present And like I said just because uh, just because they are faults. Maybe that you like them It's fine. Uh, other just understand other people don't like them So thanks for that tour for giving us this opportunity a lot of fun to taste, huh? I will see you guys soon. Hello. Thanks for watching. Hey, you made it to the end Make sure to subscribe to our youtube channel click the bell so you know when new videos are out If you like content like this check out our patreon page where you get some behind the scenes exclusive content. Thanks for watching. Cheers