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The earth mover’s distance and Bayesian linear discriminant analysis for epileptic seizure detection in scalp EEG

  • Shasha Yuan
  • Jinxing Liu
  • Junliang Shang
  • Xiangzhen Kong
  • Qi Yuan
  • Zhen Ma
Original Article

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

Epilepsy Seizure detection Scalp EEG The earth mover’s distance BLDA classification 

Notes

Funding

This study was funded by National Natural Science Foundation of China (No. 61701279) and Shandong Provincial Natural Science Foundation (No. ZR2017PF006), and jointly supported by the National Natural Science Foundation of China (Nos. 61572284, 61502272, 61702299 and 61501283), and China Postdoctoral Science Foundation (No. 2015M582129).

Compliance with ethical standards

Conflict of interest

All authors declared that they have no conflicts of interest to this work.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Korean Society of Medical and Biological Engineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringQufu Normal UniversityRizhaoPeople’s Republic of China
  2. 2.Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and ElectronicsShandong Normal UniversityJinanPeople’s Republic of China
  3. 3.Department of Information EngineeringBinzhou UniversityBinzhouPeople’s Republic of China

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