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Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning
Epilepsy, a prevalent neurological disorder, profoundly affects patients’ quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. Ho...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346886/ https://www.ncbi.nlm.nih.gov/pubmed/37447653 http://dx.doi.org/10.3390/s23135805 |
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author | Gelbard-Sagiv, Hagar Pardo, Snir Getter, Nir Guendelman, Miriam Benninger, Felix Kraus, Dror Shriki, Oren Ben-Sasson, Shay |
author_facet | Gelbard-Sagiv, Hagar Pardo, Snir Getter, Nir Guendelman, Miriam Benninger, Felix Kraus, Dror Shriki, Oren Ben-Sasson, Shay |
author_sort | Gelbard-Sagiv, Hagar |
collection | PubMed |
description | Epilepsy, a prevalent neurological disorder, profoundly affects patients’ quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing electrode configurations for such systems, which is crucial for balancing accuracy and practicality, remains to be explored. This study addresses this gap by developing a systematic approach to optimize electrode configurations for a seizure detection machine-learning algorithm. Our approach was applied to an extensive database of prolonged annotated EEG recordings from 158 epilepsy patients. Multiple electrode configurations ranging from one to eighteen were assessed to determine the optimal number of electrodes. Results indicated that the performance was initially maintained as the number of electrodes decreased, but a drop in performance was found to have occurred at around eight electrodes. Subsequently, a comprehensive analysis of all eight-electrode configurations was conducted using a computationally intensive workflow to identify the optimal configurations. This approach can inform the mechanical design process of an EEG system that balances seizure detection accuracy with the ease of use and portability. Additionally, this framework holds potential for optimizing hardware in other machine learning applications. The study presents a significant step towards the development of an efficient wearable EEG system for seizure detection. |
format | Online Article Text |
id | pubmed-10346886 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103468862023-07-15 Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning Gelbard-Sagiv, Hagar Pardo, Snir Getter, Nir Guendelman, Miriam Benninger, Felix Kraus, Dror Shriki, Oren Ben-Sasson, Shay Sensors (Basel) Article Epilepsy, a prevalent neurological disorder, profoundly affects patients’ quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing electrode configurations for such systems, which is crucial for balancing accuracy and practicality, remains to be explored. This study addresses this gap by developing a systematic approach to optimize electrode configurations for a seizure detection machine-learning algorithm. Our approach was applied to an extensive database of prolonged annotated EEG recordings from 158 epilepsy patients. Multiple electrode configurations ranging from one to eighteen were assessed to determine the optimal number of electrodes. Results indicated that the performance was initially maintained as the number of electrodes decreased, but a drop in performance was found to have occurred at around eight electrodes. Subsequently, a comprehensive analysis of all eight-electrode configurations was conducted using a computationally intensive workflow to identify the optimal configurations. This approach can inform the mechanical design process of an EEG system that balances seizure detection accuracy with the ease of use and portability. Additionally, this framework holds potential for optimizing hardware in other machine learning applications. The study presents a significant step towards the development of an efficient wearable EEG system for seizure detection. MDPI 2023-06-21 /pmc/articles/PMC10346886/ /pubmed/37447653 http://dx.doi.org/10.3390/s23135805 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gelbard-Sagiv, Hagar Pardo, Snir Getter, Nir Guendelman, Miriam Benninger, Felix Kraus, Dror Shriki, Oren Ben-Sasson, Shay Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning |
title | Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning |
title_full | Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning |
title_fullStr | Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning |
title_full_unstemmed | Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning |
title_short | Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning |
title_sort | optimizing electrode configurations for wearable eeg seizure detection using machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346886/ https://www.ncbi.nlm.nih.gov/pubmed/37447653 http://dx.doi.org/10.3390/s23135805 |
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