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Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the no...

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Autores principales: Statsenko, Yauhen, Babushkin, Vladimir, Talako, Tatsiana, Kurbatova, Tetiana, Smetanina, Darya, Simiyu, Gillian Lylian, Habuza, Tetiana, Ismail, Fatima, Almansoori, Taleb M., Gorkom, Klaus N.-V., Szólics, Miklós, Hassan, Ali, Ljubisavljevic, Milos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525492/
https://www.ncbi.nlm.nih.gov/pubmed/37760815
http://dx.doi.org/10.3390/biomedicines11092370
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author Statsenko, Yauhen
Babushkin, Vladimir
Talako, Tatsiana
Kurbatova, Tetiana
Smetanina, Darya
Simiyu, Gillian Lylian
Habuza, Tetiana
Ismail, Fatima
Almansoori, Taleb M.
Gorkom, Klaus N.-V.
Szólics, Miklós
Hassan, Ali
Ljubisavljevic, Milos
author_facet Statsenko, Yauhen
Babushkin, Vladimir
Talako, Tatsiana
Kurbatova, Tetiana
Smetanina, Darya
Simiyu, Gillian Lylian
Habuza, Tetiana
Ismail, Fatima
Almansoori, Taleb M.
Gorkom, Klaus N.-V.
Szólics, Miklós
Hassan, Ali
Ljubisavljevic, Milos
author_sort Statsenko, Yauhen
collection PubMed
description Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95–100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.
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spelling pubmed-105254922023-09-28 Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach Statsenko, Yauhen Babushkin, Vladimir Talako, Tatsiana Kurbatova, Tetiana Smetanina, Darya Simiyu, Gillian Lylian Habuza, Tetiana Ismail, Fatima Almansoori, Taleb M. Gorkom, Klaus N.-V. Szólics, Miklós Hassan, Ali Ljubisavljevic, Milos Biomedicines Article Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95–100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG. MDPI 2023-08-24 /pmc/articles/PMC10525492/ /pubmed/37760815 http://dx.doi.org/10.3390/biomedicines11092370 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
Statsenko, Yauhen
Babushkin, Vladimir
Talako, Tatsiana
Kurbatova, Tetiana
Smetanina, Darya
Simiyu, Gillian Lylian
Habuza, Tetiana
Ismail, Fatima
Almansoori, Taleb M.
Gorkom, Klaus N.-V.
Szólics, Miklós
Hassan, Ali
Ljubisavljevic, Milos
Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach
title Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach
title_full Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach
title_fullStr Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach
title_full_unstemmed Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach
title_short Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach
title_sort automatic detection and classification of epileptic seizures from eeg data: finding optimal acquisition settings and testing interpretable machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525492/
https://www.ncbi.nlm.nih.gov/pubmed/37760815
http://dx.doi.org/10.3390/biomedicines11092370
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