Electroencephalogram feature extraction and classification of autistic children based on recurrence quantification analysis_Journal of Biomedical Engineering

Authors：

ZHAO Jie ^1,2 , ZHANG Zhiming ¹ , WAN Lingyan ¹ , LI Xiaoli ³ ,  KANG Jiannan ¹

1. Institute of Electronic Information Engineering, Hebei University, Baoding, Hebei 071000, P.R.China;
2. Machine Vision Technology Innovation Center of Hebei Province, Baoding, Hebei 071000, 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; electroencephalogram; recurrence plot; recurrence quantification analysis

DOI：

10.7507/1001-5515.202010082

Video：

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Extraction and analysis of electroencephalogram (EEG) signal characteristics of patients with autism spectrum disorder (ASD) is of great significance for the diagnosis and treatment of the disease. Based on recurrence quantitative analysis (RQA)method, this study explored the differences in the nonlinear characteristics of EEG signals between ASD children and children with typical development (TD). In the experiment, RQA method was used to extract nonlinear features such as recurrence rate (RR), determinism (DET) and length of average diagonal line (LADL) of EEG signals in different brain regions of subjects, and support vector machine was combined to classify children with ASD and TD. The research results show that for the whole brain area (including parietal lobe, frontal lobe, occipital lobe and temporal lobe), when the three feature combinations of RR, DET and LADL are selected, the maximum classification accuracy rate is 84%, the sensitivity is 76%, the specificity is 92%, and the corresponding area under the curve (AUC) value is 0.875. For parietal lobe and frontal lobe (including parietal lobe, frontal lobe), when the three features of RR, DET and LADL are combined, the maximum classification accuracy rate is 82%, the sensitivity is 72%, and the specificity is 92%, which corresponds to an AUC value of 0.781. The research in this paper shows that the nonlinear characteristics of EEG signals extracted based on RQA method can become an objective indicator to distinguish children with ASD and TD, and combined with machine learning methods, the method can provide auxiliary evaluation indicators for clinical diagnosis. At the same time, the difference in the nonlinear characteristics of EEG signals between ASD children and TD children is statistically significant in the parietal-frontal lobe. This study analyzes the clinical characteristics of children with ASD based on the functions of the brain regions, and provides help for future diagnosis and treatment.

Citation： ZHAO Jie, ZHANG Zhiming, WAN Lingyan, LI Xiaoli, KANG Jiannan. Electroencephalogram feature extraction and classification of autistic children based on recurrence quantification analysis. Journal of Biomedical Engineering, 2021, 38(4): 663-670. doi: 10.7507/1001-5515.202010082 Copy

1.	Chez M G, Chang M, Krasne V, et al. Frequency of epileptiform EEG abnormalities in a sequential screening of autistic patients with no known clinical epilepsy from 1996 to 2005. Epilepsy & Behavior, 2006, 8(1): 267-271.
2.	Blumberg S J, Bramlett M D, Kogan M D, et al. Changes in prevalence of parent-reported autism spectrum disorder in school-aged U. S. children: 2007 to 2011-2012. Natl Health Stat Report, 2013, 65(65): 1-11.
3.	Tick B, Bolton P, Happé F, et al. Heritability of autism spectrum disorders: a meta-analysis of twin studies. J Child Psychol Psychiatry, 2016, 57(5): 585-595.
4.	周秉睿, 徐琼, 鲁萍, 等. 中文版《孤独症诊断观察量表》模块1信度和效度评价及临床应用研究. 中国循证儿科杂志, 2013, 8(4): 257-261.
5.	Stam C J. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field-science direct. Clinical Neurophysiology, 2005, 116(10): 2266-2301.
6.	Cukic M, Stokic M, Radenkovic S, et al. Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression. Int J Methods Psychiatr Res, 2020, 29(2): 254-268.
7.	李世丹. 递归定量分析在视听脑电信号中的应用研究. 太原: 太原理工大学, 2018.
8.	Eckmann J P, Kamphorst S O, Ruelle D. Recurrence plots of dynamical systems. Europhys Lett, 1987, 4(9): 973-977.
9.	Pitsik E, Frolov N, Kraemer K H, et al. Motor execution reduces EEG signals complexity: recurrence quantification analysis study. Chaos, 2020, 30(2): 023111.
10.	Timothy L T, Krishna B M, Nair U. Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis. Int J Psychophysiol, 2017, 120(3): 86-95.
11.	Vettori S, Dzhelyova M, Van der Donck S, et al. Combined frequency-tagging EEG and eye tracking reveal reduced social bias in boys with autism spectrum disorder. Cortex, 2020, 125(2): 135-148.
12.	Sarmukadam K, Bitsika V, Sharpley C F, et al. Comparing different EEG connectivity methods in young males with ASD. Behav Brain Res, 2020, 383(6): 112482.
13.	Distefano C, Dickinson A, Baker E, et al. EEG data collection in children with ASD: the role of state in data quality and spectral power. Res Autism Spectr Disord, 2019, 57(4): 132-144.
14.	Loomes R, Hull L, Mandy W P. What is the male-to-female ratio in autism spectrum disorder? A systematic review and meta-analysis. J Am Acad Child Adolesc Psychiatry, 2017, 56(6): 466-474.
15.	周佰成, 翟延慧, 李向前. Takens理论和小波分析在非线性预测中的应用. 吉林大学自然科学学报, 2001, 2001(3): 21-28.
16.	Zbilut J P, Webber C L. Embeddings and delays as derived from quantification of recurrence plots. North-Holland, 1992, 171(3/4): 199-203.
17.	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): S1.
18.	Bosl W, Tierney A, Tager-Flusberg H, et al. EEG complexity as a biomarker for autism spectrum disorder risk. BMC Med, 2011, 9(1): 18.
19.	Richey J A, Damiano C R, Sabatino A, et al. Neural mechanisms of emotion regulation in autism spectrum disorder. J Autism Dev Disord, 2015, 45(11): 3409-3423.
20.	Vogan V M, Francis K E, Morgan B R, et al. Load matters: neural correlates of verbal working memory in children with autism spectrum disorder. J Neurodev Disord, 2018, 10(1): 19.
21.	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.

1. Chez M G, Chang M, Krasne V, et al. Frequency of epileptiform EEG abnormalities in a sequential screening of autistic patients with no known clinical epilepsy from 1996 to 2005. Epilepsy & Behavior, 2006, 8(1): 267-271.
2. Blumberg S J, Bramlett M D, Kogan M D, et al. Changes in prevalence of parent-reported autism spectrum disorder in school-aged U. S. children: 2007 to 2011-2012. Natl Health Stat Report, 2013, 65(65): 1-11.
3. Tick B, Bolton P, Happé F, et al. Heritability of autism spectrum disorders: a meta-analysis of twin studies. J Child Psychol Psychiatry, 2016, 57(5): 585-595.
4. 周秉睿, 徐琼, 鲁萍, 等. 中文版《孤独症诊断观察量表》模块1信度和效度评价及临床应用研究. 中国循证儿科杂志, 2013, 8(4): 257-261.
5. Stam C J. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field-science direct. Clinical Neurophysiology, 2005, 116(10): 2266-2301.
6. Cukic M, Stokic M, Radenkovic S, et al. Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression. Int J Methods Psychiatr Res, 2020, 29(2): 254-268.
7. 李世丹. 递归定量分析在视听脑电信号中的应用研究. 太原: 太原理工大学, 2018.
8. Eckmann J P, Kamphorst S O, Ruelle D. Recurrence plots of dynamical systems. Europhys Lett, 1987, 4(9): 973-977.
9. Pitsik E, Frolov N, Kraemer K H, et al. Motor execution reduces EEG signals complexity: recurrence quantification analysis study. Chaos, 2020, 30(2): 023111.
10. Timothy L T, Krishna B M, Nair U. Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis. Int J Psychophysiol, 2017, 120(3): 86-95.
11. Vettori S, Dzhelyova M, Van der Donck S, et al. Combined frequency-tagging EEG and eye tracking reveal reduced social bias in boys with autism spectrum disorder. Cortex, 2020, 125(2): 135-148.
12. Sarmukadam K, Bitsika V, Sharpley C F, et al. Comparing different EEG connectivity methods in young males with ASD. Behav Brain Res, 2020, 383(6): 112482.
13. Distefano C, Dickinson A, Baker E, et al. EEG data collection in children with ASD: the role of state in data quality and spectral power. Res Autism Spectr Disord, 2019, 57(4): 132-144.
14. Loomes R, Hull L, Mandy W P. What is the male-to-female ratio in autism spectrum disorder? A systematic review and meta-analysis. J Am Acad Child Adolesc Psychiatry, 2017, 56(6): 466-474.
15. 周佰成, 翟延慧, 李向前. Takens理论和小波分析在非线性预测中的应用. 吉林大学自然科学学报, 2001, 2001(3): 21-28.
16. Zbilut J P, Webber C L. Embeddings and delays as derived from quantification of recurrence plots. North-Holland, 1992, 171(3/4): 199-203.
17. 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): S1.
18. Bosl W, Tierney A, Tager-Flusberg H, et al. EEG complexity as a biomarker for autism spectrum disorder risk. BMC Med, 2011, 9(1): 18.
19. Richey J A, Damiano C R, Sabatino A, et al. Neural mechanisms of emotion regulation in autism spectrum disorder. J Autism Dev Disord, 2015, 45(11): 3409-3423.
20. Vogan V M, Francis K E, Morgan B R, et al. Load matters: neural correlates of verbal working memory in children with autism spectrum disorder. J Neurodev Disord, 2018, 10(1): 19.
21. 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.

Journal of Biomedical Engineering

Electroencephalogram feature extraction and classification of autistic children based on recurrence quantification analysis

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