Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices
Open accessAbstract
We present the implementation of seizure detection algorithms based on a minimal number of EEG channels on a parallel ultra-low-power embedded platform. The analyses are based on the CHB-MIT dataset, and include explorations of different classification approaches (Support Vector Machines, Random Forest, Extra Trees, AdaBoost) and different pre/post-processing techniques to maximize sensitivity while guaranteeing no false alarms. We analyze global and subject-specific approaches, considering all 23-electrodes or only 4 temporal channels. For 8 s window size and subject-specific approach, we report zero false positives and 100% sensitivity. These algorithms are parallelized and optimized for a parallel ultra-low power (PULP) platform, enabling 300h of continuous monitoring on a 300 mAh battery, in a wearable form factor and power budget. These results pave the way for the implementation of affordable, wearable, long-term epilepsy monitoring solutions with low false-positive rates and high sensitivity, meeting both patient and caregiver requirements.
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The setup in [103] accomplished up to 40.87 hours of monitoring using a single battery charge, approximately a whole day of work.
“The entire setup accomplish up o 40.87 hours of monitoring using a single battery charge (approximately a whole day of work); in”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [103]
The Results & Discussion section estimates roughly 300 hours of monitoring on a 300 mAh battery for the paper's approach, not 40.87 hours. It reports a 7.34-fold battery-life improvement over a different implementation; 40.87 hours is approximately 300 divided by 7.34, suggesting the claim confuses that comparator with the cited setup. (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredAuthors in [103] deployed an ultra low powered wearable device for treatment of epilepsy through detection of epileptic seizure.
“The authors in [103] deploy an ultra low powered wearable device in treatment of this neurological deficiency through the detection of epileptic seizure.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [103]
The Abstract and ‘D. Embedded platform’ describe seizure-detection algorithms implemented on an ultra-low-power wearable device. The Introduction says monitoring can help clinicians refine therapies, but the paper does not report deploying the device as an epilepsy treatment; its analyses use an existing EEG dataset. Page numbers are unavailable in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measured
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- Authors
- Ingolfsson, T. M., Cossettini, A., Wang, X., Tabanelli, E., Tagliavini, G., Ryvlin, P., Benini, L., & Benatti, S.
- Affiliations
- ETH Zurich (CH)
- University of Bologna (IT)
- Centre Hospitalier Universitaire Vaudois (CH)
- University of Modena and Reggio Emilia (IT)
- ORCID
- Venue
- 2021 IEEE Biomedical Circuits and Systems Conference (BioCAS)
- Publisher
- IEEE
- Published
- 2021-10-07
- Pages
- 01-04
- OpenAlex
- W3171973026
- Cited by
- 35
- References
- 27
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 12 · 2023: 7 · 2022: 7
- FWCI
- 10.42
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Biomedical Circuits and Systems Conference (BioCAS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Swiss National Science Foundation (Project PEDESITE) (agreement)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- epilepsy, seizure detection, wearable EEG, long-term monitoring, CHB-MIT dataset, false alarm reduction, parallel machine learning
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