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Neural Computing and Applications

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26-10-2020 | Original Article

AHW-BGOA-DNN: a novel deep learning model for epileptic seizure detection

Authors: H. Anila Glory, C. Vigneswaran, Sujeet S. Jagtap, R. Shruthi, G. Hariharan, V. S. Shankar Sriram

Published in: Neural Computing and Applications | Issue 11/2021

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Abstract

“Brain–Computer Interface” (BCI)—a real-life support system provides a way for epileptic patients to improve their quality of life. In general, epileptic seizure detection using Electroencephalogram (EEG) signals provide a significant solution in preventing seizures through medication. Thus, the design of efficient machine learning-based seizure detection model is highly acclaimed by various academic and health professionals. In a motive to address the challenges posed by the state-of-the-art techniques in terms of noise, non-stationarity, and transient nature of EEG signals, this paper presents a novel Deep Learning model for epileptic seizure detection which hybridizes Adaptive Haar Wavelet-based Binary Grasshopper Optimization Algorithm and Deep Neural Network (AHW-BGOA-DNN). The experimental analysis was carried out using three benchmark EEG datasets obtained from the University of Bonn, the University of Bern and CHB-MIT EEG database which confirm the proposed technique to be reliable and accurate over the existing state-of-the-art techniques in terms of stability analysis, classification accuracy, AUC–ROC Curve (Area Under Curve–Receiver Operating Characteristics), sensitivity, and specificity.

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Title: AHW-BGOA-DNN: a novel deep learning model for epileptic seizure detection
Authors: H. Anila Glory
C. Vigneswaran
Sujeet S. Jagtap
R. Shruthi
G. Hariharan
V. S. Shankar Sriram
Publication date: 26-10-2020
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 11/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05384-7

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