An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909362311659520 |
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| author | Aziz, Abdul Pham, Nhat Vora, Neel Reynolds, Cody Lehnen, Jaime Venkatesh, Pooja Yao, Zhuoran Harvey, Jay Vu, Tam Ding, Kan Nguyen, Phuc |
| author_facet | Aziz, Abdul Pham, Nhat Vora, Neel Reynolds, Cody Lehnen, Jaime Venkatesh, Pooja Yao, Zhuoran Harvey, Jay Vu, Tam Ding, Kan Nguyen, Phuc |
| contents | Epilepsy is one of the most common neurological diseases globally (around 50 million people worldwide). 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 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-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/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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_05425 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection Aziz, Abdul Pham, Nhat Vora, Neel Reynolds, Cody Lehnen, Jaime Venkatesh, Pooja Yao, Zhuoran Harvey, Jay Vu, Tam Ding, Kan Nguyen, Phuc Signal Processing Machine Learning Epilepsy is one of the most common neurological diseases globally (around 50 million people worldwide). 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 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-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/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. |
| title | An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2401.05425 |