Automatic seizure detection by convolutional neural networks with computational complexity analysis
Background and Objectives: Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model perform...
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my.utm.1063882024-06-29T07:14:10Z http://eprints.utm.my/106388/ Automatic seizure detection by convolutional neural networks with computational complexity analysis Cimr, Dalibor Fujita, Hamido Tomaskova, Hana Cimler, Richard Selamat, Ali T Technology (General) Background and Objectives: Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems. Methods: The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network. Results: Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset. Conclusions: Through the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support. Elsevier Ireland Ltd 2023 Article PeerReviewed Cimr, Dalibor and Fujita, Hamido and Tomaskova, Hana and Cimler, Richard and Selamat, Ali (2023) Automatic seizure detection by convolutional neural networks with computational complexity analysis. Computer Methods and Programs in Biomedicine, 229 (NA). NA-NA. ISSN 0169-2607 http://dx.doi.org/10.1016/j.cmpb.2022.107277 DOI : 10.1016/j.cmpb.2022.107277 |
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T Technology (General) Cimr, Dalibor Fujita, Hamido Tomaskova, Hana Cimler, Richard Selamat, Ali Automatic seizure detection by convolutional neural networks with computational complexity analysis |
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Background and Objectives: Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems. Methods: The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network. Results: Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset. Conclusions: Through the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support. |
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Article |
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Cimr, Dalibor Fujita, Hamido Tomaskova, Hana Cimler, Richard Selamat, Ali |
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Cimr, Dalibor Fujita, Hamido Tomaskova, Hana Cimler, Richard Selamat, Ali |
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Cimr, Dalibor |
title |
Automatic seizure detection by convolutional neural networks with computational complexity analysis |
title_short |
Automatic seizure detection by convolutional neural networks with computational complexity analysis |
title_full |
Automatic seizure detection by convolutional neural networks with computational complexity analysis |
title_fullStr |
Automatic seizure detection by convolutional neural networks with computational complexity analysis |
title_full_unstemmed |
Automatic seizure detection by convolutional neural networks with computational complexity analysis |
title_sort |
automatic seizure detection by convolutional neural networks with computational complexity analysis |
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Elsevier Ireland Ltd |
publishDate |
2023 |
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http://eprints.utm.my/106388/ http://dx.doi.org/10.1016/j.cmpb.2022.107277 |
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1803335001069060096 |
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13.189138 |