Robust Epileptic Seizure Detection on Wearable Systems with Reduced False-Alarm Rate
Open accessAbstract
Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can potentially improve the quality of life for such patients and reduce the mortality rate due to seizure-related accidents and sudden death in epilepsy. However, the majority of employed seizure detection techniques and devices suffer from unacceptable false-alarm rate. In this paper, we propose a robust seizure detection methodology for a wearable platform and validate it on the Physionet.org CHB-MIT Scalp EEG database. It reaches sensitivity of 0.966 and specificity of 0.925, and reducing the false-alarm rate by 34.7%. We also evaluate the battery lifetime of the wearable system including our proposed methodology and demonstrate the feasibility of using it in real time for up to 40.87 hours on a single battery charge.
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A ML model deployed on ATMega2560 microcontroller achieved high accuracy of 93.58% for detection and recovery from FoG in Parkinson's patients, balancing power consumption and processing speed [101].
“Such contributions can be recognized in the detection and recovery from FoGnin Parkinson's patients, achieving high accuracy of 93.58% by a ML model deployed on ATMega2560 microcontroller that is able to balance between power consumption and processing speed [101].”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [101]
In [101], the accuracy drop is estimated to be 1.3%.
“In [101], the accuracy drop is estimated to be 1.3%.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [101]
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A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366 · see citing works
“87 hours of monitoring using a single battery charge (approximately a whole day of work); in addition to reduced amount of false-alarms that [101] worked on to obtain.”
BackgroundTowards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices
Ingolfsson, T. M., Cossettini, A., Wang, X., Tabanelli, E., Tagliavini, G., Ryvlin, P., Benini, L., & Benatti, S. (2021) · 2021 IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas49922.2021.9644949 · Cited by 35 · see citing works
- Authors
- Zanetti, R., Aminifar, A., & Atienza, D.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Venue
- 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2020-07
- Volume
- 2020
- Pages
- 4248-4251
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W3037110129
- Cited by
- 45
- References
- 21
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 12 · 2023: 7 · 2022: 8 · 2021: 7
- FWCI
- 7.16
- Type
- conference paper
- Language
- en
- Open access
- green
- embc.embs.org
- Licence
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- 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
- seizure detection, wearable devices, false alarm reduction, scalp EEG, CHB-MIT Scalp EEG Database, drug-resistant epilepsy, real-time patient monitoring, battery life
- MeSH terms
- Wearable Electronic Devices, Electroencephalography, Epilepsy, Humans, Quality of Life, Seizures
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