Model-based spike detection of epileptic EEG data

Yung Chun Liu, Chou Ching K. Lin, Jing Jane Tsai, Yung Nien Sun

研究成果: Article同行評審

45 引文 斯高帕斯(Scopus)

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.

原文English
頁(從 - 到)12536-12547
頁數12
期刊Sensors (Switzerland)
13
發行號9
DOIs
出版狀態Published - 2013 9月 17

All Science Journal Classification (ASJC) codes

  • 分析化學
  • 生物化學
  • 原子與分子物理與光學
  • 儀器
  • 電氣與電子工程

指紋

深入研究「Model-based spike detection of epileptic EEG data」主題。共同形成了獨特的指紋。

引用此