Effect of music therapy on brain function of autistic children based on power spectrum and sample entropy_Journal of Biomedical Engineering

Authors：

 ZHAO Yunan ¹ , LAI Shixuan ¹ , LYU Wei ¹ , ZHAO Min ² , LI Shouhe ³ , ZHANG Mengyi ⁴ , QI Jinping ⁵

1. School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, P. R. China;
2. Child Health Care Department, Zaozhuang Municipal Hospital, Zaozhuang, Shandong 277100, P. R. China;
3. Department of Education, Liaoning Special Education Teachers College, Shenyang, 110173, P. R. China;
4. Department of Ethnic Instrumental Music, Shenyang Conservatory of Music, Shenyang 110818, P. R. China;
5. School of Basic Medicine, Shenyang Medical College, Shenyang 110034, P. R. China;

Corresponding author：

ZHAO Yunan, Email: zhaoyn@sut.edu.cn

Keywords：

Autism; Music therapy; Power spectrum; Sample entropy

DOI：

10.7507/1001-5515.202401021

Video：

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This study aims to explore whether Guzheng playing training has a positive impact on the brain functional state of children with Autism Spectrum Disorder (ASD) based on power spectral and sample entropy analyses. Eight ASD participants were selected to undergo four months of Guzheng playing training, with one month as a training cycle. Electroencephalogram (EEG) signals and behavioral data were collected for comparative analysis. The results showed that after Guzheng playing training, the relative power of the alpha band in the occipital lobe of ASD children increased, and the relative power of the theta band in the parietal lobe decreased. The differences compared with typically developing (TD) children were narrowed. Moreover, some channels exhibited a gradual increase or decrease in power with the extended training period. Meanwhile, the sample entropy parameter also showed a similar upward trend, which was consistent with the behavioral data representation. The study shows that Guzheng training can enhance the brain function of ASD patients, with better effects from longer training. Guzheng playing training could be used as a daily intervention for autism.

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2.	毕雪. 了解“来自星星的孩子”. 吉林日报, 2023-04-11(005).
3.	周正荣, 王晨光, 王谦倩, 等. 经颅直流电刺激改善孤独症谱系障碍儿童社交认知: 基于图片命名的脑电图研究. 中国康复医学杂志, 2021, 36(1): 38-45.
4.	Genovese A, Butler M G. Clinical assessment, genetics, and treatment approaches in autism spectrum disorder (ASD). Int J Mol Sci, 2020, 21(13): 4726.
5.	张勇. 音乐治疗学. 武汉: 湖北科学技术出版社, 2010: 11-12.
6.	Masataka N. Neurodiversity, giftedness, and aesthetic perceptual judgment of music in children with autism. Front Psychol, 2017, 8: 1595.
7.	Simpson K, Keen D. Music interventions for children with autism: narrative review of the literature. J Autism Dev Disord, 2011, 41(11): 1507-1514.
8.	Buday E M. The erects of signed and spoken words taught with music on sign and speech imitation by children with autism. J Music Ther, 1995, 32(3): 189-202.
9.	樊越波, 彭晓玲, 黄丹. 脑电图在孤独症谱系障碍中的应用. 中国康复, 2017, 32(5): 379-382.
10.	Banerjee A, Sanyal S, Patranabis A, et al. Study on brain dynamics by Non Linear Analysis of music induced EEG signals. Physica A, 2016, 444: 110-120.
11.	Sheikhani A, Behnam H, Mohammadi M R, et al. detection of abnormalities for diagnosing of children with autism disorders using of Quantitative Electroencephalography Analysis. J Med Syst, 2012, 36(2): 957-963.
12.	Neuhaus E, Santhosh M, Kresse A, et al. Frontal EEG alpha asymmetry in youth with autism: Sex differences and social-emotional correlates. Autism Res, 2023, 16(12): 2364-2377.
13.	Zhao Q, Luo Y, Mei X J, et al. Resting-state EEG patterns of preschool-aged boys with autism spectrum disorder: A pilot study. Appl Neuropsychol Child, 2024, 13(4): 413-420.
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15.	雷敏, 孟光, 张文明, 等. 基于虚拟开车环境的自闭症儿童脑电样本熵. 物理学报, 2016, 65(10): 334-345.
16.	Bakheet D. Maharatna K. Linear and nonlinear analysis of intrinsic mode function after facial stimuli presentation in children with autism spectrum disorder. Comput Biol Med, 2021, 133: 104376.
17.	Kang J N, Chen H M, Li X, et al. EEG entropy analysis in autistic children. J Clin Neurosci, 2019, 62: 199-206.
18.	Li F L, Zhang S, Jiang L, et al. Recognition of autism spectrum disorder in children based on electroencephalogram network topology. Cogn Neurodynamics, 2024, 18(3): 1033-1045.
19.	Kang J N, Jin Y J, Liang G H, et al. Accurate assessment of low-function autistic children based on EEG feature fusion. J Clin Neurosc, 2021, 90: 351-358.
20.	Ke X H, Song W, Yang M G, et al. Effectiveness of music therapy in children with autism spectrum disorder: A systematic review and meta-analysis. Front Psychiatry, 2022, 13: 905113.
21.	张梦怡. 古筝特殊音乐教育在自闭症领域的研究与应用——以儿歌弹唱教学为例. 人民音乐, 2019(9): 64-67.
22.	Deng L, He W Z, Zhang Q L, et al. Caregiver-child interaction as an effective tool for identifying autism spectrum disorder: Evidence from EEG analysis. Child Adolesc Psychiatry Ment Health, 2023, 17(1): 138.
23.	闻芳, 庞姣, 李小俚, 等. 经颅直流电刺激对孤独症谱系障碍儿童脑电的影响研究. 中国生物医学工程学报, 2019, 38(5): 566-572.
24.	安占周. 孤独症脑功能状态分析及脑调控效果研究. 秦皇岛: 燕山大学, 2020.
25.	Wang J, Barstein J, Ethridge L E, et al. Resting state EEG abnormalities in Autism Spectrum Disorders. J Neurodev Disord, 2013, 5(1): 24.
26.	Song Y D, Zhang J X. Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine. J Neurosci Methods, 2016, 257: 45-54.
27.	Pincus S M. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U S A, 1991, 88(6): 2297-2301.

1. 陈淑玲. 《儿心量表2016版》在孤独症谱系障碍中的应用研究. 武汉: 华中科技大学, 2020.
2. 毕雪. 了解“来自星星的孩子”. 吉林日报, 2023-04-11(005).
3. 周正荣, 王晨光, 王谦倩, 等. 经颅直流电刺激改善孤独症谱系障碍儿童社交认知: 基于图片命名的脑电图研究. 中国康复医学杂志, 2021, 36(1): 38-45.
4. Genovese A, Butler M G. Clinical assessment, genetics, and treatment approaches in autism spectrum disorder (ASD). Int J Mol Sci, 2020, 21(13): 4726.
5. 张勇. 音乐治疗学. 武汉: 湖北科学技术出版社, 2010: 11-12.
6. Masataka N. Neurodiversity, giftedness, and aesthetic perceptual judgment of music in children with autism. Front Psychol, 2017, 8: 1595.
7. Simpson K, Keen D. Music interventions for children with autism: narrative review of the literature. J Autism Dev Disord, 2011, 41(11): 1507-1514.
8. Buday E M. The erects of signed and spoken words taught with music on sign and speech imitation by children with autism. J Music Ther, 1995, 32(3): 189-202.
9. 樊越波, 彭晓玲, 黄丹. 脑电图在孤独症谱系障碍中的应用. 中国康复, 2017, 32(5): 379-382.
10. Banerjee A, Sanyal S, Patranabis A, et al. Study on brain dynamics by Non Linear Analysis of music induced EEG signals. Physica A, 2016, 444: 110-120.
11. Sheikhani A, Behnam H, Mohammadi M R, et al. detection of abnormalities for diagnosing of children with autism disorders using of Quantitative Electroencephalography Analysis. J Med Syst, 2012, 36(2): 957-963.
12. Neuhaus E, Santhosh M, Kresse A, et al. Frontal EEG alpha asymmetry in youth with autism: Sex differences and social-emotional correlates. Autism Res, 2023, 16(12): 2364-2377.
13. Zhao Q, Luo Y, Mei X J, et al. Resting-state EEG patterns of preschool-aged boys with autism spectrum disorder: A pilot study. Appl Neuropsychol Child, 2024, 13(4): 413-420.
14. Afifi L, Raafat H, Abdullah K, et al. Quantitative EEG in autistic children. Egypt J Neurol Psych, 2015, 52(3): 176.
15. 雷敏, 孟光, 张文明, 等. 基于虚拟开车环境的自闭症儿童脑电样本熵. 物理学报, 2016, 65(10): 334-345.
16. Bakheet D. Maharatna K. Linear and nonlinear analysis of intrinsic mode function after facial stimuli presentation in children with autism spectrum disorder. Comput Biol Med, 2021, 133: 104376.
17. Kang J N, Chen H M, Li X, et al. EEG entropy analysis in autistic children. J Clin Neurosci, 2019, 62: 199-206.
18. Li F L, Zhang S, Jiang L, et al. Recognition of autism spectrum disorder in children based on electroencephalogram network topology. Cogn Neurodynamics, 2024, 18(3): 1033-1045.
19. Kang J N, Jin Y J, Liang G H, et al. Accurate assessment of low-function autistic children based on EEG feature fusion. J Clin Neurosc, 2021, 90: 351-358.
20. Ke X H, Song W, Yang M G, et al. Effectiveness of music therapy in children with autism spectrum disorder: A systematic review and meta-analysis. Front Psychiatry, 2022, 13: 905113.
21. 张梦怡. 古筝特殊音乐教育在自闭症领域的研究与应用——以儿歌弹唱教学为例. 人民音乐, 2019(9): 64-67.
22. Deng L, He W Z, Zhang Q L, et al. Caregiver-child interaction as an effective tool for identifying autism spectrum disorder: Evidence from EEG analysis. Child Adolesc Psychiatry Ment Health, 2023, 17(1): 138.
23. 闻芳, 庞姣, 李小俚, 等. 经颅直流电刺激对孤独症谱系障碍儿童脑电的影响研究. 中国生物医学工程学报, 2019, 38(5): 566-572.
24. 安占周. 孤独症脑功能状态分析及脑调控效果研究. 秦皇岛: 燕山大学, 2020.
25. Wang J, Barstein J, Ethridge L E, et al. Resting state EEG abnormalities in Autism Spectrum Disorders. J Neurodev Disord, 2013, 5(1): 24.
26. Song Y D, Zhang J X. Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine. J Neurosci Methods, 2016, 257: 45-54.
27. Pincus S M. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U S A, 1991, 88(6): 2297-2301.

Journal of Biomedical Engineering

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