Feature exaction and classification of autism spectrum disorder children related electroencephalographic signals based on entropy_Journal of Biomedical Engineering

Authors：

ZHAO Jie ¹ , DING Meng ¹ , TONG Zhen ² , HAN Junxia ³ , LI Xiaoli ³ ,  KANG Jiannan ¹

1. Institute of Electronic Information Engineering, Hebei University, Baoding, Hebei 071000, P.R.China;
2. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China;
3. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, P.R.China;

Corresponding author：

KANG Jiannan, Email: kangjiannan81@163.com

Keywords：

autism spectrum disorders; electroencephalography; entropy; classification; feature selection

DOI：

10.7507/1001-5515.201709047

Video：

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Abstract Full text Figures/Tables Video References Cited by

The early diagnosis of children with autism spectrum disorders (ASD) is essential. Electroencephalography (EEG) is one of most commonly used neuroimaging techniques as the most accessible and informative method. In this study, approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn) and wavelet entropy (WaEn) were extracted from EEGs of ASD child and a control group, and Student's t-test was used to analyze between-group differences. Support vector machine (SVM) algorithm was utilized to build classification models for each entropy measure derived from different regions. Permutation test was applied in search for optimize subset of features, with which the SVM model achieved best performance. The results showed that the complexity of EEGs in children with autism was lower than that of the normal control group. Among all four entropies, WaEn got a better classification performance than others. Classification results vary in different regions, and the frontal lobe showed the best performance. After feature selection, six features were filtered out and the accuracy rate was increased to 84.55%, which can be convincing for assisting early diagnosis of autism.

Citation： ZHAO Jie, DING Meng, TONG Zhen, HAN Junxia, LI Xiaoli, KANG Jiannan. Feature exaction and classification of autism spectrum disorder children related electroencephalographic signals based on entropy. Journal of Biomedical Engineering, 2019, 36(2): 183-188, 198. doi: 10.7507/1001-5515.201709047 Copy

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17.	Abasolo D, Hornero R, Espino P, et al. Electroencephalogram background activity characterization with approximate entropy and auto mutual information in Alzheimer's disease patients. Conf Proc IEEE Eng Med Biol Soc, 2007, 2007: 6192-6195.
18.	Bruce E N, Bruce M C, Vennelaganti S. Sample entropy tracks changes in electroencephalogram power spectrum with sleep state and aging. J Clin Neurophysiol, 2009, 26(4): 257-266.
19.	Lee S H, Park H W, Kim M J, et al. External validation of pharmacokinetic and pharmacodynamics models of microemulsion and long-chain triglyceride emulsion propofol in beagle dogs. J Vet Pharmacol Ther, 2012, 35(4): 329-341.
20.	Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
21.	Li X, Ouyang G, Richards D A. Predictability analysis of absence seizures with permutation entropy. Epilepsy Res, 2007, 77(1): 70-74.
22.	Ferlazzo E, Mammone N, Cianci V, et al. Permutation entropy of scalp EEG: a tool to investigate epilepsies: suggestions from absence epilepsies. Clin Neurophysiol, 2014, 125(1): 13.
23.	Li Xiaoli, Cui Suyuan, Voss L J. Using permutation entropy to measure the electroencephalographic effects of sevoflurane. Anesthesiology, 2008, 109(3): 448-456.
24.	Rosso O A, Blanco S, Yordanova J, et al. Wavelet entropy: a new tool for analysis of short duration brain electrical signals. J Neurosci Methods, 2001, 105(1): 65-75.
25.	Kim H J, Fay M P, Feuer E J, et al. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med, 2000, 19(3): 335-351.
26.	Mueller A, Candrian G, Kropotov J D, et al. Classification of ADHD patients on the basis of independent ERP components using a machine learning system. Nonlinear Biomed Phys, 2010, 4(Suppl 1): 1-12.
27.	Tenev A, Markovska-Simoska S, Kocarev L A, et al. Machine learning approach for classification of ADHD adults. Int J Psychophysiol, 2014, 93(1): 162-166.
28.	奉国和. SVM分类核函数及参数选择比较. 计算机工程与应用, 2011, 47(3): 123-124, 128.
29.	林升梁, 刘志. 基于RBF核函数的支持向量机参数选择. 浙江工业大学学报, 2007, 35(2): 163-167.
30.	李盼池, 许少华. 支持向量机在模式识别中的核函数特性分析. 计算机工程与设计, 2005, 26(2): 302-304.

1. Coben R, Mohammad-Rezazadeh I, Cannon R L. Using quantitative and analytic EEG methods in the understanding of connectivity in autism spectrum disorders: a theory of mixed over- and under-connectivity. Front Hum Neurosci, 2014, 8(104): 45.
2. Blaxill M F. What's going on? The question of time trends in autism. Public Health Rep, 2004, 119(6): 536-551.
3. 冯士刚, 唐一源. 用脑功能成像的方法研究大脑的视觉成像机制//大连理工大学生物医学工程学术论文集, 2005.
4. Braeutigam S, Swithenby S J, Bailey A J. Contextual integration the unusual way: a magnetoencephalographic study of responses to semantic violation in individuals with autism spectrum disorders. Eur J Neurosci, 2008, 27(4): 1026-1036.
5. Coben R, Clarke A R, Hudspeth W, et al. EEG power and coherence in autistic spectrum disorder. Clin Neurophysiol, 2008, 119(5): 1002-1009.
6. Bernier R, Dawson G, Webb S et al. EEG mu rhythm and imitation impairments in individuals with autism spectrum disorder. Brain & Cognition, 2007, 64(3): 228-237.
7. Léveillé C, Barbeau E B, Bolduc C et al. Enhanced connectivity between visual cortex and other regions of the brain in autism: a REM sleep EEG coherence study. Autism Res, 2010, 3(5): 280-285.
8. Bosl W, Tierney A, Tager-Flusberg H A. EEG complexity as a biomarker for autism spectrum disorder risk. BMC Med, 2011, 9(1): 18.
9. Kulisek R, Hrncir Z, Hrdlicka M, et al. Nonlinear analysis of the sleep EEG in children with pervasive developmental disorder. Neuro Endocrinol Lett, 2008, 29(4): 512-517.
10. Ahmadlou M, Adeli H, Adeli A. Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder. J Clin Neurophysiol, 2010, 27(5): 328-333.
11. Catarino A, Churches O, Baron-Cohen S, et al. Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis. Clin Neurophysiol, 2011, 122(12): 2375-2383.
12. Lippe S, Kovacevic N, Mcintosh A R. Differential maturation of brain signal complexity in the human auditory and visual system. Front Hum Neurosci, 2009, 3(4): 48.
13. Meyer-Lindenberg A. The evolution of complexity in human brain development: An EEG study. Electroencephalogr Clin Neurophysiol, 1996, 99(5): 405-411.
14. Pincus S M. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U S A, 1991, 88(6): 2297-2301.
15. Pincus S M. Approximate entropy as a measure of irregularity for psychiatric serial metrics. Bipolar Disord, 2006, 8(5, 1): 430-440.
16. Stam C J. Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field. Clin Neurophysiol, 2005, 116(10): 2266-2301.
17. Abasolo D, Hornero R, Espino P, et al. Electroencephalogram background activity characterization with approximate entropy and auto mutual information in Alzheimer's disease patients. Conf Proc IEEE Eng Med Biol Soc, 2007, 2007: 6192-6195.
18. Bruce E N, Bruce M C, Vennelaganti S. Sample entropy tracks changes in electroencephalogram power spectrum with sleep state and aging. J Clin Neurophysiol, 2009, 26(4): 257-266.
19. Lee S H, Park H W, Kim M J, et al. External validation of pharmacokinetic and pharmacodynamics models of microemulsion and long-chain triglyceride emulsion propofol in beagle dogs. J Vet Pharmacol Ther, 2012, 35(4): 329-341.
20. Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
21. Li X, Ouyang G, Richards D A. Predictability analysis of absence seizures with permutation entropy. Epilepsy Res, 2007, 77(1): 70-74.
22. Ferlazzo E, Mammone N, Cianci V, et al. Permutation entropy of scalp EEG: a tool to investigate epilepsies: suggestions from absence epilepsies. Clin Neurophysiol, 2014, 125(1): 13.
23. Li Xiaoli, Cui Suyuan, Voss L J. Using permutation entropy to measure the electroencephalographic effects of sevoflurane. Anesthesiology, 2008, 109(3): 448-456.
24. Rosso O A, Blanco S, Yordanova J, et al. Wavelet entropy: a new tool for analysis of short duration brain electrical signals. J Neurosci Methods, 2001, 105(1): 65-75.
25. Kim H J, Fay M P, Feuer E J, et al. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med, 2000, 19(3): 335-351.
26. Mueller A, Candrian G, Kropotov J D, et al. Classification of ADHD patients on the basis of independent ERP components using a machine learning system. Nonlinear Biomed Phys, 2010, 4(Suppl 1): 1-12.
27. Tenev A, Markovska-Simoska S, Kocarev L A, et al. Machine learning approach for classification of ADHD adults. Int J Psychophysiol, 2014, 93(1): 162-166.
28. 奉国和. SVM分类核函数及参数选择比较. 计算机工程与应用, 2011, 47(3): 123-124, 128.
29. 林升梁, 刘志. 基于RBF核函数的支持向量机参数选择. 浙江工业大学学报, 2007, 35(2): 163-167.
30. 李盼池, 许少华. 支持向量机在模式识别中的核函数特性分析. 计算机工程与设计, 2005, 26(2): 302-304.

Journal of Biomedical Engineering

Feature exaction and classification of autism spectrum disorder children related electroencephalographic signals based on entropy

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