Model-Based Spike Detection of Epileptic EEG Data

Sensors

Yung-Chun Liu²  Chou-Ching K. Lin¹  Jing-Jane Tsai¹ 

[1] Department of Neurology, National Cheng Kung University Hospital, No. 138, Sheng Li Road, Tainan City 704, Taiwan; E-Mails:;Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan; E-Mail:

关键词: epilepsy;    slow wave;    spike detection;    spike classification;    nonlinear energy operator;   

DOI  :  10.3390/s130912536

来源: mdpi

【 摘 要 】

Accurate automatic spike detection is highly beneficial to clinical assessment of epileptic electroencephalogram (EEG) data. In this paper, a new two-stage approach is proposed for epileptic spike detection. First, the k-point nonlinear energy operator (k-NEO) is adopted to detect all possible spike candidates, then a newly proposed spike model with slow wave features is applied to these candidates for spike classification. Experimental results show that the proposed system, using the AdaBoost classifier, outperforms the conventional method in both two- and three-class EEG pattern classification problems. The proposed system not only achieves better accuracy for spike detection, but also provides new ability to differentiate between spikes and spikes with slow waves. Though spikes with slow waves occur frequently in epileptic EEGs, they are not used in conventional spike detection. Identifying spikes with slow waves allows the proposed system to have better capability for assisting clinical neurologists in routine EEG examinations and epileptic diagnosis.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.