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Transfer learning based epileptic seizure classification using scalogram images of EEG signals

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Abstract

Epilepsy is a common neurological disorder that occurs due to an abnormality of the nerve cells in the brain. Electroencephalogram (EEG) analysis is one of the most vital tools used to detect seizure events. Normally, EEG analysis is done manually by expert neurologists, which is time-consuming, tedious and error-prone, hence an automatic detection approach based on deep learning (DL) is essential in the dignosis process. In this study, we focused on automated epileptic seizure classification using a transfer learning. A pre-trained network ResNet50 is used for the feature extraction and classification using a deep neural network (dense layer) on 2D scalogram images of EEG signal. The time-frequency scalogram images were generated using continuous wavelet transform (CWT) from EEG signals collected from the CHBMIT scalp EEG database. The method was evaluated in terms of accuracy, sensitivity, specificity and computational time. The results are compared with the tested results of standard pre-trained models such as VGG16 and InceptionV3 which are also implemented in this study to classify the EEG events. The proposed approach achieved a best classification with an accuracy of 95.23%, sensitivity of 99.54%, and specificity of 90.28% respectively, which is better than the result obtained from the VGG16 and InceptionV3 networks. The training time for ResNet50 + DNN, VGG16, InceptionV3 are 2340, 2520, and 1440 s respectively, which indicates the computational complexity is more in transfer learning but classification accuracy is better than the standard models. Therefore, ResNet50 based transfer learning using 2D-scalogram images of EEG signals has evidently proved to be a better choice to a neurologist for fast anticipation of epileptic seizure.

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Data used for the research is available online.

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Authors and Affiliations

  1. Sensing & Computing Lab, School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India

    Sasweta Pattnaik, B. Nageswara Rao, Nirmal Kumar Rout & Sukanta Kumar Sabut

Authors
  1. Sasweta Pattnaik
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  2. B. Nageswara Rao
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  3. Nirmal Kumar Rout
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  4. Sukanta Kumar Sabut
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Contributions

SP, BNR, NKR and SKS designed the study; SP and SKS collected data; SP, SP, SKS and BNR conducted the study; SP and SKS conducted analysis, SP and SKS wrote the paper; BNR, NKR and SKS conducted review and editing.

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Correspondence to Sukanta Kumar Sabut.

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Pattnaik, S., Rao, B.N., Rout, N.K. et al. Transfer learning based epileptic seizure classification using scalogram images of EEG signals. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19129-4

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  • DOI: https://doi.org/10.1007/s11042-024-19129-4

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