Epileptic seizure prediction based on multiresolution convolutional neural networks
Epilepsy withholds patients’ control of their body or consciousness and puts them at risk in the course of their daily life. This article pursues the development of a smart neurocomputational technology to alert epileptic patients wearing EEG sensors of an impending seizure. An innovative approach f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Signal Processing |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frsip.2023.1175305/full |
| _version_ | 1827936912597319680 |
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| author | Ali K. Ibrahim Hanqi Zhuang Emmanuelle Tognoli Ali Muhamed Ali Nurgun Erdol |
| author_facet | Ali K. Ibrahim Hanqi Zhuang Emmanuelle Tognoli Ali Muhamed Ali Nurgun Erdol |
| author_sort | Ali K. Ibrahim |
| collection | DOAJ |
| description | Epilepsy withholds patients’ control of their body or consciousness and puts them at risk in the course of their daily life. This article pursues the development of a smart neurocomputational technology to alert epileptic patients wearing EEG sensors of an impending seizure. An innovative approach for epileptic seizure prediction has been proposed to improve prediction accuracy and reduce the false alarm rate in comparison with state-of-the-art benchmarks. Maximal overlap discrete wavelet transform was used to decompose EEG signals into different frequency resolutions, and a multiresolution convolutional neural network is designed to extract discriminative features from each frequency band. The algorithm automatically generates patient-specific features to best classify preictal and interictal segments of the subject. The method can be applied to any patient case from any dataset without the need for a handcrafted feature extraction procedure. The proposed approach was tested with two popular epilepsy patient datasets. It achieved a sensitivity of 82% and a false prediction rate of 0.058 with the Children’s Hospital Boston-MIT scalp EEG dataset and a sensitivity of 85% and a false prediction rate of 0.19 with the American Epilepsy Society Seizure Prediction Challenge dataset. This technology provides a personalized solution for the patient that has improved sensitivity and specificity, yet because of the algorithm’s intrinsic ability for generalization, it emancipates from the reliance on epileptologists’ expertise to tune a wearable technological aid, which will ultimately help to deploy it broadly, including in medically underserved locations across the globe. |
| first_indexed | 2024-03-13T08:17:59Z |
| format | Article |
| id | doaj.art-b22a1a27c53a434ca90cf2dc21279f4a |
| institution | Directory Open Access Journal |
| issn | 2673-8198 |
| language | English |
| last_indexed | 2024-03-13T08:17:59Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Signal Processing |
| spelling | doaj.art-b22a1a27c53a434ca90cf2dc21279f4a2023-05-31T14:27:39ZengFrontiers Media S.A.Frontiers in Signal Processing2673-81982023-05-01310.3389/frsip.2023.11753051175305Epileptic seizure prediction based on multiresolution convolutional neural networksAli K. Ibrahim0Hanqi Zhuang1Emmanuelle Tognoli2Ali Muhamed Ali3Nurgun Erdol4Department of Computer Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United StatesDepartment of Computer Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United StatesCenter for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, United StatesDepartment of Computer Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United StatesDepartment of Computer Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United StatesEpilepsy withholds patients’ control of their body or consciousness and puts them at risk in the course of their daily life. This article pursues the development of a smart neurocomputational technology to alert epileptic patients wearing EEG sensors of an impending seizure. An innovative approach for epileptic seizure prediction has been proposed to improve prediction accuracy and reduce the false alarm rate in comparison with state-of-the-art benchmarks. Maximal overlap discrete wavelet transform was used to decompose EEG signals into different frequency resolutions, and a multiresolution convolutional neural network is designed to extract discriminative features from each frequency band. The algorithm automatically generates patient-specific features to best classify preictal and interictal segments of the subject. The method can be applied to any patient case from any dataset without the need for a handcrafted feature extraction procedure. The proposed approach was tested with two popular epilepsy patient datasets. It achieved a sensitivity of 82% and a false prediction rate of 0.058 with the Children’s Hospital Boston-MIT scalp EEG dataset and a sensitivity of 85% and a false prediction rate of 0.19 with the American Epilepsy Society Seizure Prediction Challenge dataset. This technology provides a personalized solution for the patient that has improved sensitivity and specificity, yet because of the algorithm’s intrinsic ability for generalization, it emancipates from the reliance on epileptologists’ expertise to tune a wearable technological aid, which will ultimately help to deploy it broadly, including in medically underserved locations across the globe.https://www.frontiersin.org/articles/10.3389/frsip.2023.1175305/fullepilepsyseizure predictionCNNwavelet transformmultiresolution convolutional neural networks |
| spellingShingle | Ali K. Ibrahim Hanqi Zhuang Emmanuelle Tognoli Ali Muhamed Ali Nurgun Erdol Epileptic seizure prediction based on multiresolution convolutional neural networks Frontiers in Signal Processing epilepsy seizure prediction CNN wavelet transform multiresolution convolutional neural networks |
| title | Epileptic seizure prediction based on multiresolution convolutional neural networks |
| title_full | Epileptic seizure prediction based on multiresolution convolutional neural networks |
| title_fullStr | Epileptic seizure prediction based on multiresolution convolutional neural networks |
| title_full_unstemmed | Epileptic seizure prediction based on multiresolution convolutional neural networks |
| title_short | Epileptic seizure prediction based on multiresolution convolutional neural networks |
| title_sort | epileptic seizure prediction based on multiresolution convolutional neural networks |
| topic | epilepsy seizure prediction CNN wavelet transform multiresolution convolutional neural networks |
| url | https://www.frontiersin.org/articles/10.3389/frsip.2023.1175305/full |
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