Abnormal electroencephalogram features extraction of autistic children based on wavelet transform combined with empirical modal decomposition.
10.7507/1001-5515.201705067
- Author:
Xin LI
1
,
2
,
3
;
Erjuan CAI
1
,
4
;
Luyun QIN
1
,
4
;
Jiannan KANG
1
,
5
Author Information
1. Institute of Biomedical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China
2. Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, P.R.China
3. College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, P.R.China.yddylixin@ysu.edu.cn.
4. Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, P.R.China.
5. College of Electronic Information Engineering, Hebei University, Baoding, Hebei 071000, P.R.China.
- Publication Type:Journal Article
- Keywords:
autism;
electroencephalogram;
empirical mode decomposition;
wavelet transform
- From:
Journal of Biomedical Engineering
2018;35(4):524-529
- CountryChina
- Language:Chinese
-
Abstract:
Early detection and timely intervention are very essential for autism. This paper used the wavelet transform and empirical mode decomposition (EMD) to extract the features of electroencephalogram (EEG), to compare the feature differences of EEG between the autistic children and healthy children. The experimental subjects included 25 healthy children (aged 5-10 years old) and 25 children with autism (20 boys and 5 girls aged 5-10 years old) respectively. The alpha, beta, theta and delta rhythm wave spectra of the C3, C4, F3, F4, F7, F8, FP1, FP2, O1, O2, P3, P4, T3, T4, T5 and T6 channels were extracted and decomposed by EMD decomposition to obtain the intrinsic modal functions. Finally the support vector machine (SVM) classifier was used to implement assessment of autism and normal classification. The results showed that the accuracy could reach 87% and which was nearly 20% higher than that of the model combining the wavelet transform and sample entropy in the paper. Moreover, the accuracy of delta (1-4 Hz) rhythm wave was the highest among the four kinds of rhythms. And the classification accuracy of the forehead F7 channel, left FP1 channel and T6 channel in the temporal region were all up to 90%, which expressed the characteristics of EEG signals in autistic children better.
