@inproceedings{scholars5670, pages = {162--167}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, journal = {2015 International Conference on BioSignal Analysis, Processing and Systems, ICBAPS 2015}, title = {Epileptic seizure detection using singular values and classical features of EEG signals}, year = {2015}, doi = {10.1109/ICBAPS.2015.7292238}, note = {cited By 7; Conference of 1st International Conference on BioSignal Analysis, Processing and Systems, ICBAPS 2015 ; Conference Date: 26 May 2015 Through 28 May 2015; Conference Code:118420}, abstract = {In this paper, an epileptic seizure event detection algorithm utilizing five features namely singular values, total average power, delta band average power, variance and mean, is proposed. Using CHB-MIT Scalp EEG Database, the calculations of the features are performed over a sliding window of one second. The algorithm was evaluated in terms of accuracy, sensitivity, specificity and failure rate. This investigation used SVM as the classification technique. The performance comparisons are made with techniques based on classical features alone, singular value alone and combination of classical features and singular values. The results show that the proposed algorithm achieves better results than using singular values alone or using classical features alone with an average accuracy of 94.82. {\^A}{\copyright} 2015 IEEE.}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961793416&doi=10.1109\%2fICBAPS.2015.7292238&partnerID=40&md5=ef42740f7c30ddbfbee8db2f51195d29}, keywords = {Algorithms; Neurodegenerative diseases; Neurophysiology; Singular value decomposition, Classification technique; Eigen decomposition; Epileptic seizure detection; Epileptic seizures; Event detection algorithm; Performance comparison; Singular values; Sliding Window, Feature extraction}, isbn = {9781479968794}, author = {Elmahdy, A. E. and Yahya, N. and Kamel, N. S. and Shahid, A.} }