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Epileptic Seizure Detection using Spectral Transformation and Convolutional Neural Networks

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Abstract

Automatic seizure detection and classification of seizures, as well as identification of pre-ictal activity in the electroencephalogram (EEG), are extremely important in clinical research. This decreases the time it takes to identify seizures and, as a result, improves seizure activity prediction. We propose a computer-aided method to detect the pre-ictal and ictal activity from a multichannel EEG signal. Three pre-processing techniques that are applied to EEG time-domain signals to generate an image database are proposed here. This image database is given as input to the machine learning algorithm for classification. Conversion of a time domain EEG signal to an image is accomplished by extracting EEG signal features such as correlation coefficient, short-time Fourier transform (spectrogram), and mutual information. The processed EEG waveform, which is represented as images, is used to train a convolutional neural network (CNN). The CNN classifies input signals into three classes—Seizure, Normal and Pre-ictal. We used the transfer learning method, which uses Alexnet, a pre-trained CNN architecture, for image training and classification. After training on the Spectrogram, Mutual Information, and Correlation coefficient image representations of the EEG signals, we have obtained a validation accuracy of 99.33%, 95.33%, and 97.5%, respectively.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, BMS Institute of Technology and Management, Bangalore, India

    T. Saneesh Cleatus

  2. Visvesvaraya Technological University, Bangalore, India

    T. Saneesh Cleatus

  3. Department of Computer Science, College of Horticulture, University of Horticultural Sciences, GKVK, Bangalore, India

    M. Thungamani

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  1. T. Saneesh Cleatus
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Cleatus, T.S., Thungamani, M. Epileptic Seizure Detection using Spectral Transformation and Convolutional Neural Networks. J. Inst. Eng. India Ser. B 103, 1115–1125 (2022). https://doi.org/10.1007/s40031-021-00693-4

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  • DOI: https://doi.org/10.1007/s40031-021-00693-4

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