Biomedical Engineering Letters

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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The earth mover's distance and Bayesian linear discriminant analysis for epileptic seizure detection in scalp EEG

  • Yuan, Shasha (School of Information Science and Engineering, Qufu Normal University) ;
  • Liu, Jinxing (School of Information Science and Engineering, Qufu Normal University) ;
  • Shang, Junliang (School of Information Science and Engineering, Qufu Normal University) ;
  • Kong, Xiangzhen (School of Information Science and Engineering, Qufu Normal University) ;
  • Yuan, Qi (Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University) ;
  • Ma, Zhen (Department of Information Engineering, Binzhou University)
  • Received : 2018.04.26
  • Accepted : 2018.07.31
  • Published : 2018.11.30
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Abstract

Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover's distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover's distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China, Shandong Provincial Natural Science Foundation, China Postdoctoral Science Foundation

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