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. Pediatrics, 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：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

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.

Citation： ZHAO Yunan, LAI Shixuan, LYU Wei, ZHAO Min, LI Shouhe, ZHANG Mengyi, QI Jinping. Effect of music therapy on brain function of autistic children based on power spectrum and sample entropy. Journal of Biomedical Engineering, 2025, 42(3): 537-543. doi: 10.7507/1001-5515.202401021 Copy

1.	陳淑玲. 《兒心量表2016版》在孤獨癥譜系障礙中的應用研究. 武漢: 華中科技大學, 2020.
2.	畢雪. 了解“來自星星的孩子”. 吉林日報, 2023-04-11(005).
3.	周正榮, 王晨光, 王謙倩, 等. 經顱直流電刺激改善孤獨癥譜系障礙兒童社交認知: 基于圖片命名的腦電圖研究. 中國康復醫學雜志, 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.	張勇. 音樂治療學. 武漢: 湖北科學技術出版社, 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, 彭曉玲, 黃丹. 腦電圖在孤獨癥譜系障礙中的應用. 中國康復, 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, 張文明, 等. 基于虛擬開車環境的自閉癥兒童腦電樣本熵. 物理學報, 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.	張夢怡. 古箏特殊音樂教育在自閉癥領域的研究與應用——以兒歌彈唱教學為例. 人民音樂, 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.	聞芳, 龐姣, 李小俚, 等. 經顱直流電刺激對孤獨癥譜系障礙兒童腦電的影響研究. 中國生物醫學工程學報, 2019, 38(5): 566-572.
24.	安占周. 孤獨癥腦功能狀態分析及腦調控效果研究. 秦皇島: 燕山大學, 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. 陳淑玲. 《兒心量表2016版》在孤獨癥譜系障礙中的應用研究. 武漢: 華中科技大學, 2020.
2. 畢雪. 了解“來自星星的孩子”. 吉林日報, 2023-04-11(005).
3. 周正榮, 王晨光, 王謙倩, 等. 經顱直流電刺激改善孤獨癥譜系障礙兒童社交認知: 基于圖片命名的腦電圖研究. 中國康復醫學雜志, 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. 張勇. 音樂治療學. 武漢: 湖北科學技術出版社, 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, 彭曉玲, 黃丹. 腦電圖在孤獨癥譜系障礙中的應用. 中國康復, 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, 張文明, 等. 基于虛擬開車環境的自閉癥兒童腦電樣本熵. 物理學報, 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. 張夢怡. 古箏特殊音樂教育在自閉癥領域的研究與應用——以兒歌彈唱教學為例. 人民音樂, 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. 聞芳, 龐姣, 李小俚, 等. 經顱直流電刺激對孤獨癥譜系障礙兒童腦電的影響研究. 中國生物醫學工程學報, 2019, 38(5): 566-572.
24. 安占周. 孤獨癥腦功能狀態分析及腦調控效果研究. 秦皇島: 燕山大學, 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

Effect of music therapy on brain function of autistic children based on power spectrum and sample entropy

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content