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Seizure Detection Based on EEG Signals Using Asymmetrical Back Propagation Neural Network Method

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Abstract

Abnormal activity in the human brain is a symptom of epilepsy. Electroencephalogram (EEG) is a standard tool that has been widely used to detect seizures. A number of automated seizure detection systems based on EEG signal classification have been employed in present days, which includes a mixture of approaches but most of them rely on time signal features, time intervals or time frequency domains. Therefore, in this research, deep learning-based automated mechanism is introduced to improve the seizure detection accuracy from EEG signal using the Asymmetrical Back Propagation Neural Network (ABPN) method. The ABPN system includes four levels of repetitive training with weight adjustment, feed forward initialization, error and update weight and bias back-propagation. The proposed ABPN-based seizure detection system is validated using Physionet EEG dataset with matlab simulation, and the effectiveness of proposed seizure system is confirmed through simulation results. As compared with Deep Convolutional Neural Network (CNN) and Support Vector Machine–Particle Swarm Optimization (SVM-PSO)-based seizure detection system, the proposed ABPN system gives the best performance against various parameters. The sensitivity, specificity and accuracy are 96.32%, 95.12% and 98.36%, respectively.

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Availability of Data and Material

The data and material are taken from Physionet data. The dataset link is https://physionet.org/content/?topic=eeg.

Code Availability

The code is a custom code. It was developed by using MATLAB software.

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Acknowledgements

The authors would like to thank the editors and anonymous reviewers for their insightful comments and suggestions.

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Authors and Affiliations

  1. Department of Computer Technology-UG, Kongu Engineering College, Perundurai, Erode, Tamilnadu, India

    S. Poorani

  2. Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamilnadu, India

    P. Balasubramanie

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  1. S. Poorani
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AM contributed to technical and conceptual content, architectural design. MP contributed to guidance and counseling on the writing of the paper.

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Correspondence to S. Poorani.

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Poorani, S., Balasubramanie, P. Seizure Detection Based on EEG Signals Using Asymmetrical Back Propagation Neural Network Method. Circuits Syst Signal Process 40, 4614–4632 (2021). https://doi.org/10.1007/s00034-021-01686-w

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