An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic
  Seizure Detection
Abdul Aziz Nhat Pham Neel Vora Cody Reynolds Jaime Lehnen Pooja Venkatesh Zhuoran Yao Jay Harvey Tam Vu Kan Ding Phuc Nguyen
Epilepsy is one of the most common neurological diseases globally, affecting
around 50 million people worldwide. Fortunately, up to 70 percent of people
with epilepsy could live seizure-free if properly diagnosed and treated, and a
reliable technique to monitor the onset of seizures could improve the quality
of life of patients who are constantly facing the fear of random seizure
attacks. The scalp-based EEG test, despite being the gold standard for
diagnosing epilepsy, is costly, necessitates hospitalization, demands skilled
professionals for operation, and is discomforting for users. In this paper, we
propose EarSD, a novel lightweight, unobtrusive, and socially acceptable
ear-worn system to detect epileptic seizure onsets by measuring the
physiological signals from behind the user's ears. EarSD includes an integrated
custom-built sensing, computing, and communication PCB to collect and amplify
the signals of interest, remove the noises caused by motion artifacts and
environmental impacts, and stream the data wirelessly to the computer or mobile
phone nearby, where data are uploaded to the host computer for further
processing. We conducted both in-lab and in-hospital experiments with epileptic
seizure patients who were hospitalized for seizure studies. The preliminary
results confirm that EarSD can detect seizures with up to 95.3 percent accuracy
by just using classical machine learning algorithms.