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Automatic Detection of Epileptic Seizures in EEG Using Sparse CSP and Fisher Linear Discrimination Analysis Algorithm.

In order to realize the automatic epileptic seizure detection, feature extraction and classification of electroencephalogram (EEG) signals are performed on the interictal, the pre-ictal, and the ictal status of epilepsy patients. There is no effective strategy for selecting the number of channels an...

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Published in:Journal of Medical Systems Vol. 44; no. 2; pp. 1 - 14
Main Authors: Fu, Rongrong, Tian, Yongsheng, Shi, Peiming, Bao, Tiantian
Format: algorithm equations & formulas research tables/charts Journal Article
Published: Springer Nature Feb2020
Online Access:View this record in EBSCOhost
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      dt: Feb2020
      vid: 44
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-019-1504-1
        141512194
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        atl: Automatic Detection of Epileptic Seizures in EEG Using Sparse CSP and Fisher Linear Discrimination Analysis Algorithm.
      aug:
        au:
          Fu, Rongrong
          Tian, Yongsheng
          Shi, Peiming
          Bao, Tiantian
        affil: Key Lab of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, 066004, Qinhuangdao, China
      sug:
        subj:
          Epilepsy Diagnosis
          Seizures Diagnosis
          Electroencephalography
          Algorithms Utilization
          Signal Processing, Computer Assisted Classification
          Human
          China
          Male
          Female
          Child, Preschool
          Child
          Adolescence
          Adult
          Descriptive Statistics
          ROC Curve
          Funding Source
          Child, Preschool: 2-5 years
          Child: 6-12 years
          Adolescent: 13-18 years
          Adult: 19-44 years
          Male
          Female
      ab: In order to realize the automatic epileptic seizure detection, feature extraction and classification of electroencephalogram (EEG) signals are performed on the interictal, the pre-ictal, and the ictal status of epilepsy patients. There is no effective strategy for selecting the number of channels and spatial filters in feature extraction of multichannel EEG data. Therefore, this paper combined sparse idea and greedy search algorithm to improve the feature extraction of common space pattern. The feature extraction can effectively overcome the repeating selection problem of feature pattern in the eigenvector space by the traditional method. Then we used the Fisher linear discriminant analysis to realize the classification. The results show that the proposed method can get high classification accuracy using fewer data. For 10 subjects, the averaged accuracy of epilepsy detection is more than 99%. So, the detection of an epileptic seizure based on sparse features using Fisher linear discriminant analysis classifiers is more suitable for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life.
      pubtype: Academic Journal
      doctype:
        algorithm
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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