Application of deconvolutional networks for feature interpretability in epilepsy detection
IntroductionScalp electroencephalography (EEG) is commonly used to assist in epilepsy detection. Even automated detection algorithms are already available to assist clinicians in reviewing EEG data, many algorithms used for seizure detection in epilepsy fail to account for the contributions of diffe...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1539580/full |
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| author | Sihao Shao Yu Zhou Ruiheng Wu Aiping Yang Qiang Li |
| author_facet | Sihao Shao Yu Zhou Ruiheng Wu Aiping Yang Qiang Li |
| author_sort | Sihao Shao |
| collection | DOAJ |
| description | IntroductionScalp electroencephalography (EEG) is commonly used to assist in epilepsy detection. Even automated detection algorithms are already available to assist clinicians in reviewing EEG data, many algorithms used for seizure detection in epilepsy fail to account for the contributions of different channels. The Fully Convolutional Network (FCN) can provide the model’s interpretability but has not been applied in seizure detection.MethodsTo address these challenges, a novel convolutional neural network (CNN) model, combining SE (Squeeze-and-Excitation) modules, was proposed on top of the FCN. The epilepsy detection performance for patient-independent was evaluated on the CHB-MIT dataset. Then, the SE module was removed from the model and integrated the model with Inception, ResNet, and CBAM modules separately.ResultsThe method showed superior advancement, stability, and reliability compared to the other three methods. The method demonstrated a G-Mean of 82.7% for sensitivity (SEN) and specificity (SPE) on the CHB-MIT dataset. In addition, The contributions of each channel to the seizure detection task have also been quantified, which led us to find that the FZ, CZ, PZ, FT9, FT10, and T8 brain regions have a more pronounced impact on epileptic seizures.DiscussionThis article presents a novel algorithm for epilepsy detection that accurately identifies seizures in different patients and enhances the model’s interpretability. |
| format | Article |
| id | doaj-art-246304d2bff54479be27bd29bb7f0267 |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-246304d2bff54479be27bd29bb7f02672025-01-24T07:13:28ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-01-011810.3389/fnins.2024.15395801539580Application of deconvolutional networks for feature interpretability in epilepsy detectionSihao Shao0Yu Zhou1Ruiheng Wu2Aiping Yang3Qiang Li4School of Microelectronics, Tianjin University, Tianjin, ChinaCollege of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, ChinaDepartment of Electronic and Electrical Engineering, Brunel University London, Uxbridge, United KingdomSchool of Electrical and Information Engineering, Tianjin University, Tianjin, ChinaSchool of Microelectronics, Tianjin University, Tianjin, ChinaIntroductionScalp electroencephalography (EEG) is commonly used to assist in epilepsy detection. Even automated detection algorithms are already available to assist clinicians in reviewing EEG data, many algorithms used for seizure detection in epilepsy fail to account for the contributions of different channels. The Fully Convolutional Network (FCN) can provide the model’s interpretability but has not been applied in seizure detection.MethodsTo address these challenges, a novel convolutional neural network (CNN) model, combining SE (Squeeze-and-Excitation) modules, was proposed on top of the FCN. The epilepsy detection performance for patient-independent was evaluated on the CHB-MIT dataset. Then, the SE module was removed from the model and integrated the model with Inception, ResNet, and CBAM modules separately.ResultsThe method showed superior advancement, stability, and reliability compared to the other three methods. The method demonstrated a G-Mean of 82.7% for sensitivity (SEN) and specificity (SPE) on the CHB-MIT dataset. In addition, The contributions of each channel to the seizure detection task have also been quantified, which led us to find that the FZ, CZ, PZ, FT9, FT10, and T8 brain regions have a more pronounced impact on epileptic seizures.DiscussionThis article presents a novel algorithm for epilepsy detection that accurately identifies seizures in different patients and enhances the model’s interpretability.https://www.frontiersin.org/articles/10.3389/fnins.2024.1539580/fullseizure detectionEEGdeconvolution networkinterpretability analysisdeep learning |
| spellingShingle | Sihao Shao Yu Zhou Ruiheng Wu Aiping Yang Qiang Li Application of deconvolutional networks for feature interpretability in epilepsy detection Frontiers in Neuroscience seizure detection EEG deconvolution network interpretability analysis deep learning |
| title | Application of deconvolutional networks for feature interpretability in epilepsy detection |
| title_full | Application of deconvolutional networks for feature interpretability in epilepsy detection |
| title_fullStr | Application of deconvolutional networks for feature interpretability in epilepsy detection |
| title_full_unstemmed | Application of deconvolutional networks for feature interpretability in epilepsy detection |
| title_short | Application of deconvolutional networks for feature interpretability in epilepsy detection |
| title_sort | application of deconvolutional networks for feature interpretability in epilepsy detection |
| topic | seizure detection EEG deconvolution network interpretability analysis deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1539580/full |
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