Title: Early onset/offset detection of epileptic seizure using M-band wavelet decomposition

Authors: Yash Vardhan Varshney; Garima Chandel; Prashant Upadhyaya; Omar Farooq; Yusuf Uzzaman Khan

Addresses: Psychophysiology Lab, IIT Bombay, India ' Department of Electronics and Communication Engineering, Chandigarh University, India ' Department of Electronics and Communication Engineering, Chandigarh University, India ' Department of Electronics Engineering, Aligarh Muslim University, Aligarh, India ' Department of Electrical Engineering, Aligarh Muslim University, Aligarh, India

Abstract: Early detection of the seizure and its diagnosis play an important role for effective treatment of epileptic patients. Most of the research used in this field has been focused on detection of the seizure. However, it is also very important to detect seizure with minimum delay, which can be useful to take care of the patient. In this paper, an efficient approach for seizure detection with low onset/offset latency is proposed using three-band wavelet decomposition. Variance and higher order moments are computed from wavelet-based feature extracted using three level wavelet decomposition. For comparative analysis, the extracted features are classified using two classifiers; decision tree (DT) and a shallow artificial neural network (ANN). The DT shows better classification performance as compare to ANN with classification specificity, sensitivity and accuracy of 99.6%, 98.97% and 99.49% respectively with onset and offset latency of 4.01 s and -0.21 s.

Keywords: onset/offset seizure detection; M-band wavelet transform; decision tree; DT; shallow network.

DOI: 10.1504/IJBET.2022.126494

International Journal of Biomedical Engineering and Technology, 2022 Vol.40 No.3, pp.205 - 223

Received: 29 Mar 2019
Accepted: 16 Apr 2020
Published online: 27 Oct 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article