• Submit to Sensors Login Register
• MDPI
• Journals A-Z
• For Authors
• For Editors
• For Librarians
• About
• Open Access Policy

• Abstract
• Full-Text HTML
• Full-Text PDF [399 KB]
• Full-Text XML
• Article Versions Notes

• Article Statistics
• PubMed/Medline
• Google Scholar
• Order Reprints

• Cite this article
• Export to BibTeX
• Export to EndNote

• [+] on DOAJ
• Liu, Y
• Lin, CK.
• Tsai, J
• Sun, Y
• [+] on Google Scholar
• Liu, Y
• Lin, CK.
• Tsai, J
• Sun, Y
• [+] on PubMed
• Liu, Y
• Lin, CK.
• Tsai, J
• Sun, Y

•  CiteULike
•  Facebook
•  Mendeley
•  Twitter
• More services

Open AccessThis article is

• freely available
• re-usable

Sensors 2013, 13(9), 12536-12547; doi:10.3390/s130912536

Article

Model-Based Spike Detection of Epileptic EEG Data

Yung-Chun Liu ^1,2, Chou-Ching K. Lin ³, Jing-Jane Tsai ³ and Yung-Nien Sun ^1,2,*

¹ Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan ² Medical Device Innovation Center, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan ³ Department of Neurology, National Cheng Kung University Hospital, No. 138, Sheng Li Road, Tainan City 704, Taiwan

* Author to whom correspondence should be addressed.

Received: 17 June 2013; in revised form: 6 September 2013 / Accepted: 13 September 2013 / Published: 17 September 2013

(This article belongs to the Special Issue Biomedical Sensors and Systems)

Download PDF Full-Text [399 KB, uploaded 17 September 2013 11:45 CEST]

Abstract: 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.

Keywords: epilepsy; slow wave; spike detection; spike classification; nonlinear energy operator

Article Statistics

Citations to this Article

Cite This Article