Privacy-preserving Early Detection of Epileptic Seizures in Videos
Deval Mehta Shobi Sivathamboo Hugh Simpson Patrick Kwan Terence O`Brien Zongyuan Ge
In this work, we contribute towards the development of video-based epileptic
seizure classification by introducing a novel framework (SETR-PKD), which could
achieve privacy-preserved early detection of seizures in videos. Specifically,
our framework has two significant components - (1) It is built upon optical
flow features extracted from the video of a seizure, which encodes the seizure
motion semiotics while preserving the privacy of the patient; (2) It utilizes a
transformer based progressive knowledge distillation, where the knowledge is
gradually distilled from networks trained on a longer portion of video samples
to the ones which will operate on shorter portions. Thus, our proposed
framework addresses the limitations of the current approaches which compromise
the privacy of the patients by directly operating on the RGB video of a seizure
as well as impede real-time detection of a seizure by utilizing the full video
sample to make a prediction. Our SETR-PKD framework could detect tonic-clonic
seizures (TCSs) in a privacy-preserving manner with an accuracy of 83.9% while
they are only half-way into their progression. Our data and code is available
at https://github.com/DevD1092/seizure-detection