TY  - CONF
EP  - 167
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961793416&doi=10.1109%2fICBAPS.2015.7292238&partnerID=40&md5=ef42740f7c30ddbfbee8db2f51195d29
A1  - Elmahdy, A.E.
A1  - Yahya, N.
A1  - Kamel, N.S.
A1  - Shahid, A.
SN  - 9781479968794
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2015///
KW  - Algorithms; Neurodegenerative diseases; Neurophysiology; Singular value decomposition
KW  -  Classification technique; Eigen decomposition; Epileptic seizure detection; Epileptic seizures; Event detection algorithm; Performance comparison; Singular values; Sliding Window
KW  -  Feature extraction
ID  - scholars5670
TI  - Epileptic seizure detection using singular values and classical features of EEG signals
SP  - 162
N1  - 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
N2  - 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. © 2015 IEEE.
AV  - none
ER  -