JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (3): 21-26.doi: 10.6040/j.issn.1672-3961.1.2016.148

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Research on the classification of resting state EEG signal between depression patients and normal controls by EMD and SVM methods

LIU Yan1,2,3, LI Youjun4, CHEN Meng1,2,3   

  1. 1. College of Electronic Information &
    Control Engineering, Beijing University of Technology, Beijing 100124, China;
    2. Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100124, China;
    3. Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China;
    4. North China University of Technology, Beijing 100144, China
  • Received:2016-03-01 Online:2017-06-20 Published:2016-03-01

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Abstract: Automatic detection of depression state was significant for mental disease diagnostics and rehabilitation, which could decrease the duration of work required when inspecting the electroencephalography(EEG)signals. A novel method for feature extraction and pattern recognition from subjects resting state EEG signal, based upon empirical mode decomposition(EMD)and support vector machine(SVM)was proposed to make a distinction between depression patients and normal controls. The EEG signals were collected from 20 depression patients and 25 normal persons, and the EEG was filtered and extracted as features. The SVM was used as classifier for recognition which showed whether the person was a depression patient. The experimental results showed that the algorithm could achieve the specificity of 93.3%. And the classification accuracy from the features extracted by EMD was higher than the classification accuracy from features extracted by wavelet clearly.

Key words: EEG, intrinsic mode function(IMF), EMD, depression, SVM

CLC Number: 

  • TP391.4
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