Detection of Epilepsy Disorder Using Spectrogram Images Generated From Brain EEG Signals
Epilepsy (EP) is a persistent neurological condition of chronic brain disorder characterized by repeated seizures and causes psychological issues such as anxiety and depression. There is a need to detect the presence of epilepsy at an earlier stage with the help of technological intervention. Early...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10810442/ |
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| author | Venkatesh Bhandage Tejeswar Pokuri Devansh Desai Andrew Jeyabose |
| author_facet | Venkatesh Bhandage Tejeswar Pokuri Devansh Desai Andrew Jeyabose |
| author_sort | Venkatesh Bhandage |
| collection | DOAJ |
| description | Epilepsy (EP) is a persistent neurological condition of chronic brain disorder characterized by repeated seizures and causes psychological issues such as anxiety and depression. There is a need to detect the presence of epilepsy at an earlier stage with the help of technological intervention. Early detection of epilepsy can help medical practitioners treat patients effectively and in a better way. Electroencephalography (EEG) signals are more suitable for monitoring brain activity and detecting brain disorders. In this paper, we propose a deep learning based approach for the early detection of epilepsy via EEG Spectrogram images. The proposed approach is 3-fold. First, we propose an algorithm to generate spectrogram images from the EEG signals, and then, we adapt an efficient Convolutional Neural Network (CNN) model to classify the spectrogram images. Finally, we utilized SmoothGradCAM++ and saliency maps to interpret the decision-making process of the deep learning models. We examined the use of three different pretrained CNN architectures, namely, EfficientNetB0, MobileNetV2, and ResNet18. The methodology is tested on two publicly available datasets to validate the performance of the classifiers in terms of sensitivity, accuracy, specificity, precision, and F1-Score. We observed that the modified MobileNetV2 architecture achieved a state-of-the-art accuracy of 99.24%. The proposed approach can be instrumental in the early detection of epilepsy and other neurological disorders using EEG. |
| format | Article |
| id | doaj-art-7473f9878ae34331a7c46d9533151fa2 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7473f9878ae34331a7c46d9533151fa22025-08-20T02:56:47ZengIEEEIEEE Access2169-35362024-01-011219505419506410.1109/ACCESS.2024.352086110810442Detection of Epilepsy Disorder Using Spectrogram Images Generated From Brain EEG SignalsVenkatesh Bhandage0https://orcid.org/0000-0002-9503-8196Tejeswar Pokuri1https://orcid.org/0009-0003-2646-0988Devansh Desai2https://orcid.org/0009-0000-6994-1296Andrew Jeyabose3https://orcid.org/0000-0003-3592-6543Department of Computer Science and Engineering, Manipal Academy of Higher Education, Manipal Institute of Technology, Manipal, Karnataka, IndiaDepartment of Computer Science and Engineering, Manipal Academy of Higher Education, Manipal Institute of Technology, Manipal, Karnataka, IndiaDepartment of Computer Science and Engineering, Manipal Academy of Higher Education, Manipal Institute of Technology, Manipal, Karnataka, IndiaDepartment of Computer Science and Engineering, Manipal Academy of Higher Education, Manipal Institute of Technology, Manipal, Karnataka, IndiaEpilepsy (EP) is a persistent neurological condition of chronic brain disorder characterized by repeated seizures and causes psychological issues such as anxiety and depression. There is a need to detect the presence of epilepsy at an earlier stage with the help of technological intervention. Early detection of epilepsy can help medical practitioners treat patients effectively and in a better way. Electroencephalography (EEG) signals are more suitable for monitoring brain activity and detecting brain disorders. In this paper, we propose a deep learning based approach for the early detection of epilepsy via EEG Spectrogram images. The proposed approach is 3-fold. First, we propose an algorithm to generate spectrogram images from the EEG signals, and then, we adapt an efficient Convolutional Neural Network (CNN) model to classify the spectrogram images. Finally, we utilized SmoothGradCAM++ and saliency maps to interpret the decision-making process of the deep learning models. We examined the use of three different pretrained CNN architectures, namely, EfficientNetB0, MobileNetV2, and ResNet18. The methodology is tested on two publicly available datasets to validate the performance of the classifiers in terms of sensitivity, accuracy, specificity, precision, and F1-Score. We observed that the modified MobileNetV2 architecture achieved a state-of-the-art accuracy of 99.24%. The proposed approach can be instrumental in the early detection of epilepsy and other neurological disorders using EEG.https://ieeexplore.ieee.org/document/10810442/Epilepsy (EP)electroencephalography (EEG)spectrogram imagesconvolution neural network (CNN)deep learning (DL)MobileNetV2 |
| spellingShingle | Venkatesh Bhandage Tejeswar Pokuri Devansh Desai Andrew Jeyabose Detection of Epilepsy Disorder Using Spectrogram Images Generated From Brain EEG Signals IEEE Access Epilepsy (EP) electroencephalography (EEG) spectrogram images convolution neural network (CNN) deep learning (DL) MobileNetV2 |
| title | Detection of Epilepsy Disorder Using Spectrogram Images Generated From Brain EEG Signals |
| title_full | Detection of Epilepsy Disorder Using Spectrogram Images Generated From Brain EEG Signals |
| title_fullStr | Detection of Epilepsy Disorder Using Spectrogram Images Generated From Brain EEG Signals |
| title_full_unstemmed | Detection of Epilepsy Disorder Using Spectrogram Images Generated From Brain EEG Signals |
| title_short | Detection of Epilepsy Disorder Using Spectrogram Images Generated From Brain EEG Signals |
| title_sort | detection of epilepsy disorder using spectrogram images generated from brain eeg signals |
| topic | Epilepsy (EP) electroencephalography (EEG) spectrogram images convolution neural network (CNN) deep learning (DL) MobileNetV2 |
| url | https://ieeexplore.ieee.org/document/10810442/ |
| work_keys_str_mv | AT venkateshbhandage detectionofepilepsydisorderusingspectrogramimagesgeneratedfrombraineegsignals AT tejeswarpokuri detectionofepilepsydisorderusingspectrogramimagesgeneratedfrombraineegsignals AT devanshdesai detectionofepilepsydisorderusingspectrogramimagesgeneratedfrombraineegsignals AT andrewjeyabose detectionofepilepsydisorderusingspectrogramimagesgeneratedfrombraineegsignals |