Weighted multiple multiscale entropy and its application in electroencephalography analysis of autism assessment_Journal of Biomedical Engineering

Authors：

 LI Xin ^1,2 , AN Zhanzhou ^1,2 , LI Qiuyue ^1,2 , SHI Chunyan ^1,2 , ZHANG jie ^1,2 , KANG Jiannan ³

1. Institute of Biomedical Engineering, Yanshan University, Qinhuangdao 066004, P.R.China;
2. Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao 066004, P.R.China;
3. Institute of Biomedical Engineering, Heibei University, Baoding 071028, P.R.China;

Corresponding author：

LI Xin, Email: yddylixin@ysu.edu.cn

Keywords：

autism; electroencephalography; sample entropy; weighted multiple multiscale entropy

DOI：

10.7507/1001-5515.201806047

Video：

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In this paper, a feature extraction algorithm of weighted multiple multiscale entropy is proposed to solve the problem of information loss which is caused in the multiscale process of traditional multiscale entropy. Algorithm constructs the multiple data sequences from large to small on each scale. Then, considering the different contribution degrees of multiple data sequences to the entropy of the scale, the proportion of each sequence in the scale sequence is calculated by combining the correlation between the data sequences, so as to reconstruct the sample entropy of each scale. Compared with the traditional multiscale entropy the feature extraction algorithm based on weighted multiple multiscale entropy not only overcomes the problem of information loss, but also fully considers the correlation of sequences and the contribution to total entropy. It reduces the fluctuation between scales, and digs out the details of electroencephalography (EEG). Based on this algorithm, the EEG characteristics of autism spectrum disorder (ASD) children are analyzed, and the classification accuracy of the algorithm is increased by 23.0%, 10.4% and 6.4% as compared with the EEG extraction algorithm of sample entropy, traditional multiscale entropy and multiple multiscale entropy based on the delay value method, respectively. Based on this algorithm, the 19 channel EEG signals of ASD children and healthy children were analyzed. The results showed that the entropy of healthy children was slightly higher than that of the ASD children except the FP2 channel, and the numerical differences of F3, F7, F8, C3 and P3 channels were statistically significant (P<0.05). By classifying the weighted multiple multiscale entropy of each brain region, we found that the accuracy of the anterior temporal lobe (F7, F8) was the highest. It indicated that the anterior temporal lobe can be used as a sensitive brain area for assessing the brain function of ASD children.

Citation： LI Xin, AN Zhanzhou, LI Qiuyue, SHI Chunyan, ZHANG jie, KANG Jiannan. Weighted multiple multiscale entropy and its application in electroencephalography analysis of autism assessment. Journal of Biomedical Engineering, 2019, 36(1): 33-39, 49. doi: 10.7507/1001-5515.201806047 Copy

1.	Lugnegård T, Hallerbäck M U, Gillberg C. Psychiatric comorbidity in young adults with a clinical diagnosis of Asperger syndrome. Res Dev Disabil, 2011, 32(5): 1910-1917.
2.	Mattila M L, Hurtig T, Haapsamo H, et al. Comorbid psychiatric disorders associated with asperger syndrome/high-functioning autism: a community- and clinic-based study. J Autism Dev Disord, 2010, 40(9): 1080-1093.
3.	Zablotsky B, Black L I, Maenner M J, et al. Estimated prevalence of autism and other developmental disabilities following questionnaire changes in the 2014 National health interview survey. Natl Health Stat Report, 2015(87): 1-20.
4.	Majumdar K K, Vardhan P. Automatic seizure detection in ECoG by differential operator and windowed variance. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2011, 19(4): 356-365.
5.	Duffy F H, Heidelise A. A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls-a large case control study. BMC Med, 2012, 10(1): 64.
6.	Dawson G, Klinger L G, Panagiotides H, et al. Subgroups of autistic children based on social behavior display distinct patterns of brain activity. J Abnorm Child Psychol, 1995, 23(5): 569-583.
7.	Pineda J A, Brang D, Hecht E, et al. Positive behavioral and electrophysiological changes following neurofeedback training in children with autism. Res Autism Spectr Disord, 2008, 2(3): 557-581.
8.	Acharya U R, Sudarshan V K, Adeli H A, et al. A novel depression diagnosis index using nonlinear features in EEG signals. Eur Neurol, 2015, 74(1/2): 79-83.
9.	Janjarasjitt S, Loparo K A. Scale-invariant behavior of epileptic ECoG. J Med Biol Eng, 2014, 34(6): 535-541.
10.	Song Y, Zhang J. Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine[J]. Journal of Neuroscience Methods, 2016, 257: 45-54.
11.	Srinivasan V, Eswaran C, Sriraam N. Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol Biomed, 2007, 11(3): 288-295.
12.	Catarino A, Churches O, Baron-Cohen S, et al. Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis. Clinical Neurophysiology, 2011, 122(12): 2375-2383.
13.	Bornas X, Llabrés J, Noguera M, et al. Fear induced complexity loss in the electrocardiogram of flight phobics: a multiscale entropy analysis. Biol Psychol, 2006, 73(3): 272-279.
14.	Thuraisingham R A, Gottwald G A. On multiscale entropy analysis for physiological data. Physica A: Statistical Mechanics and its Applications, 2006, 366(1): 323-332.
15.	Li Duan, Li Xiaoli, Liang Zhenhu, et al. Multiscale permutation entropy analysis of EEG recordings during sevoflurane anesthesia. J Neural Eng, 2010, 7(4, SI): 046010-046014.
16.	Wu S D, Wu C W, Lee K Y, et al. Modified multiscale entropy for short-term time series analysis. Physica A: Statistical Mechanics and its Applications, 2013, 392(23): 5865-5873.
17.	Zhao Miaoying, Xu Gang. Feature extraction of power transformer vibration signals based on empirical wavelet transform and multiscale entropy. IET Science Measurement & Technology, 2018, 12(1): 63-71.
18.	Ecker C, Ronan L, Feng Yue, et al. Intrinsic gray-matter connectivity of the brain in adults with autism spectrum disorder. Proc Natl Acad Sci U S A, 2013, 110(32): 13222-13227.
19.	Song Yuedong, Crowcroft J, Zhang Jiaxiang. Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J Neurosci Methods, 2012, 210(2): 132-146.
20.	Jaime M, Mcmahon C M, Davidson B C, et al. Brief report: reduced temporal-central EEG alpha coherence during joint attention perception in adolescents with autism spectrum disorder. J Autism Dev Disord, 2016, 46(4): 1477-1489.
21.	Sheikhani A, Behnam H, Mohammadi M R, et al. Detection of abnormalities for diagnosing of children with autism disorders using of quantitative electroence pnalography analysis. J Med Syst, 2012, 36(2): 957-963.
22.	Greimel E, Nehrkorn B, Schulte-Ruether M A, et al. Changes in grey matter development in autism spectrum disorder. Brain Struct Funct, 2013, 218(4): 929-942.
23.	Ecker C, Suckling J, Deoni S C, et al. Brain anatomy and its relationship to behavior in adults with autism spectrum disorder: a multicenter magnetic resonance imaging study. Arch Gen Psychiatry, 2012, 69(2): 195-209.

1. Lugnegård T, Hallerbäck M U, Gillberg C. Psychiatric comorbidity in young adults with a clinical diagnosis of Asperger syndrome. Res Dev Disabil, 2011, 32(5): 1910-1917.
2. Mattila M L, Hurtig T, Haapsamo H, et al. Comorbid psychiatric disorders associated with asperger syndrome/high-functioning autism: a community- and clinic-based study. J Autism Dev Disord, 2010, 40(9): 1080-1093.
3. Zablotsky B, Black L I, Maenner M J, et al. Estimated prevalence of autism and other developmental disabilities following questionnaire changes in the 2014 National health interview survey. Natl Health Stat Report, 2015(87): 1-20.
4. Majumdar K K, Vardhan P. Automatic seizure detection in ECoG by differential operator and windowed variance. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2011, 19(4): 356-365.
5. Duffy F H, Heidelise A. A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls-a large case control study. BMC Med, 2012, 10(1): 64.
6. Dawson G, Klinger L G, Panagiotides H, et al. Subgroups of autistic children based on social behavior display distinct patterns of brain activity. J Abnorm Child Psychol, 1995, 23(5): 569-583.
7. Pineda J A, Brang D, Hecht E, et al. Positive behavioral and electrophysiological changes following neurofeedback training in children with autism. Res Autism Spectr Disord, 2008, 2(3): 557-581.
8. Acharya U R, Sudarshan V K, Adeli H A, et al. A novel depression diagnosis index using nonlinear features in EEG signals. Eur Neurol, 2015, 74(1/2): 79-83.
9. Janjarasjitt S, Loparo K A. Scale-invariant behavior of epileptic ECoG. J Med Biol Eng, 2014, 34(6): 535-541.
10. Song Y, Zhang J. Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine[J]. Journal of Neuroscience Methods, 2016, 257: 45-54.
11. Srinivasan V, Eswaran C, Sriraam N. Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol Biomed, 2007, 11(3): 288-295.
12. Catarino A, Churches O, Baron-Cohen S, et al. Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis. Clinical Neurophysiology, 2011, 122(12): 2375-2383.
13. Bornas X, Llabrés J, Noguera M, et al. Fear induced complexity loss in the electrocardiogram of flight phobics: a multiscale entropy analysis. Biol Psychol, 2006, 73(3): 272-279.
14. Thuraisingham R A, Gottwald G A. On multiscale entropy analysis for physiological data. Physica A: Statistical Mechanics and its Applications, 2006, 366(1): 323-332.
15. Li Duan, Li Xiaoli, Liang Zhenhu, et al. Multiscale permutation entropy analysis of EEG recordings during sevoflurane anesthesia. J Neural Eng, 2010, 7(4, SI): 046010-046014.
16. Wu S D, Wu C W, Lee K Y, et al. Modified multiscale entropy for short-term time series analysis. Physica A: Statistical Mechanics and its Applications, 2013, 392(23): 5865-5873.
17. Zhao Miaoying, Xu Gang. Feature extraction of power transformer vibration signals based on empirical wavelet transform and multiscale entropy. IET Science Measurement & Technology, 2018, 12(1): 63-71.
18. Ecker C, Ronan L, Feng Yue, et al. Intrinsic gray-matter connectivity of the brain in adults with autism spectrum disorder. Proc Natl Acad Sci U S A, 2013, 110(32): 13222-13227.
19. Song Yuedong, Crowcroft J, Zhang Jiaxiang. Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J Neurosci Methods, 2012, 210(2): 132-146.
20. Jaime M, Mcmahon C M, Davidson B C, et al. Brief report: reduced temporal-central EEG alpha coherence during joint attention perception in adolescents with autism spectrum disorder. J Autism Dev Disord, 2016, 46(4): 1477-1489.
21. Sheikhani A, Behnam H, Mohammadi M R, et al. Detection of abnormalities for diagnosing of children with autism disorders using of quantitative electroence pnalography analysis. J Med Syst, 2012, 36(2): 957-963.
22. Greimel E, Nehrkorn B, Schulte-Ruether M A, et al. Changes in grey matter development in autism spectrum disorder. Brain Struct Funct, 2013, 218(4): 929-942.
23. Ecker C, Suckling J, Deoni S C, et al. Brain anatomy and its relationship to behavior in adults with autism spectrum disorder: a multicenter magnetic resonance imaging study. Arch Gen Psychiatry, 2012, 69(2): 195-209.

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