An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection

Epilepsy is one of the most common neurological diseases globally (around 50M people globally). Fortunately, up to 70% 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 current gold standard, video-EEG (v-EEG), involves attaching over 20 electrodes to the scalp, is costly, requires hospitalization, trained professionals, and is uncomfortable for patients. To address this gap, we developed EarSD , a lightweight and unobtrusive ear-worn system to detect seizure onsets by measuring physiological signals behind the ears. This system can be integrated into earphones, headphones, or hearing aids, providing a convenient solution for continuous monitoring. EarSD is an integrated custom-built sensing - computing - communication ear-worn platform to capture seizure signals, remove the noises caused by motion artifacts and environmental impacts, and stream the collected data wirelessly to the computer/mobile phone nearby. EarSD ’s ML algorithm, running on a server, identifies seizure-associated signatures and detects onset events. We evaluated the proposed system in both in-lab and in-hospital experiments at the University of Texas Southwestern Medical Center with epileptic seizure patients, confirming its usability and practicality.

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Publication Details

Journal
ACM Transactions on Computing for Healthcare
Published
2024-11-04
DOI
https://doi.org/10.1145/3703164
Citations
4
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
1.37
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article

An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection

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4 citations
ACM Transactions on Computing for Healthcare
EEG and Brain-Computer Interfaces
1.37
article

An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection

Neel Vora, Kan Ding, Jay Harvey, Cerisa R. Reynolds, Phuc Le Hoang Nguyen, Nhat Pham, Pooja Venkatesh, Abdul Aziz, Jaime Lehnen, Zhuoran Yao, Thi-Minh-Luan Vu
article en
4 citations

Abstract

Epilepsy is one of the most common neurological diseases globally (around 50M people globally). Fortunately, up to 70% 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 current gold standard, video-EEG (v-EEG), involves attaching over 20 electrodes to the scalp, is costly, requires hospitalization, trained professionals, and is uncomfortable for patients. To address this gap, we developed EarSD , a lightweight and unobtrusive ear-worn system to detect seizure onsets by measuring physiological signals behind the ears. This system can be integrated into earphones, headphones, or hearing aids, providing a convenient solution for continuous monitoring. EarSD is an integrated custom-built sensing - computing - communication ear-worn platform to capture seizure signals, remove the noises caused by motion artifacts and environmental impacts, and stream the collected data wirelessly to the computer/mobile phone nearby. EarSD ’s ML algorithm, running on a server, identifies seizure-associated signatures and detects onset events. We evaluated the proposed system in both in-lab and in-hospital experiments at the University of Texas Southwestern Medical Center with epileptic seizure patients, confirming its usability and practicality.

ACM Transactions on Computing for HealthcareVol. 6(1)
Amherst College (US), University of Colorado Boulder (US), The University of Texas at Arlington (US), University of Massachusetts Amherst (US), Cardiff University (GB), The University of Texas Southwestern Medical Center (US)
Openalex Percentile: Top 19%
EEG and Brain-Computer Interfaces
1.37
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