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Journal of Autism and Developmental Disorders

Gepubliceerd in:

11-07-2021 | Original Paper

Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms

Auteurs: Zhong Zhao, Zhipeng Zhu, Xiaobin Zhang, Haiming Tang, Jiayi Xing, Xinyao Hu, Jianping Lu, Xingda Qu

Gepubliceerd in: Journal of Autism and Developmental Disorders | Uitgave 7/2022

Abstract

Our study investigated the feasibility of using head movement features to identify individuals with autism spectrum disorder (ASD). Children with ASD and typical development (TD) were required to answer ten yes–no questions, and they were encouraged to nod/shake head while doing so. The head rotation range (RR) and the amount of rotation per minute (ARPM) in the pitch (head nodding direction), yaw (head shaking direction) and roll (lateral head inclination) directions were computed, and further fed into machine learning classifiers as the input features. The maximum classification accuracy of 92.11% was achieved with the decision tree classifier with two features (i.e., RR_Pitch and ARPM_Yaw). Our study suggests that head movement dynamics contain objective biomarkers that could identify ASD.

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American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing.CrossRef

Anzulewicz, A., Sobota, K., & Delafield-Butt, J. T. (2016). Toward the Autism Motor Signature: Gesture patterns during smart tablet gameplay identify children with autism. Scientific Reports. https://doi.org/10.1038/srep31107CrossRefPubMedPubMedCentral

Baltrušaitis, T., & Robinson, P. L. (2012). Morency 3D Constrained Local Model for rigid and non-rigid facial tracking. 2012 IEEE Conference on computer vision and pattern recognition (pp. 2610–2617). IEEE.CrossRef

Baltrusaitis, T., Zadeh, A., & Lim, Y. C. (2018). Morency OpenFace 2.0: Facial behavior analysis toolkit. 2018 13th IEEE International conference on automatic face & gesture recognition (FG 2018) (pp. 59–66). IEEE.CrossRef

Berrar, D. (2019). Cross-validation. Encyclopedia of bioinformatics and computational biology (pp. 1542–1545). Elsevier.

Bhat, A. N., Galloway, J. C., & Landa, R. J. (2012). Relation between early motor delay and later communication delay in infants at risk for autism. Infant Behavior and Development, 35(4), 838–846. https://doi.org/10.1016/j.infbeh.2012.07.019CrossRefPubMed

Bojanek, E. K., Wang, Z., White, S. P., & Mosconi, M. W. (2020). Postural control processes during standing and step initiation in autism spectrum disorder. Journal of Neurodevelopmental Disorders, 12(1), 1. https://doi.org/10.1186/s11689-019-9305-xCrossRefPubMedPubMedCentral

Campbell, K., Carpenter, K. L., Hashemi, J., Espinosa, S., Marsan, S., Borg, J. S., et al. (2018). Computer vision analysis captures atypical attention in toddlers with autism. Autism, 23, 613–628.

Capps, L., Kehres, J., & Sigman, M. (1998). Conversational abilities among children with autism and children with developmental delays. Autism, 2(4), 325–344. https://doi.org/10.1177/1362361398024002CrossRef

Cascia, M. L., Sclaroff, S., & Athitsos, V. (2000). Fast, reliable head tracking under varying illumination: An approach based on registration of texture-mapped 3D models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(4), 322–336. https://doi.org/10.1109/34.845375CrossRef

Chow, R., Chiu, N., Bruera, E., Krishnan, M., Chiu, L., Lam, H., et al. (2016). Inter-rater reliability in performance status assessment among health care professionals: A systematic review. Annals of Palliative Medicine, 5(2), 83–92. https://doi.org/10.21037/apm.2016.03.02CrossRefPubMed

Crippa, A., Salvatore, C., Perego, P., Forti, S., Nobile, M., Molteni, M., et al. (2015). Use of machine learning to identify children with autism and their motor abnormalities. Journal of Autism and Developmental Disorders, 45(7), 2146–2156. https://doi.org/10.1007/s10803-015-2379-8CrossRefPubMed

Daniels, A. M., & Mandell, D. S. (2014). Explaining differences in age at autism spectrum disorder diagnosis: A critical review. Autism, 18(5), 583.CrossRef

Dawson, G., Jones, E. J. H., Merkle, K., Venema, K., Lowy, R., Faja, S., et al. (2012). Early behavioral intervention is associated with normalized brain activity in young children with autism. Journal of the American Academy of Child & Adolescent Psychiatry, 51(11), 1150–1159. https://doi.org/10.1016/j.jaac.2012.08.018CrossRef

Duda, M., Kosmicki, J. A., & Wall, D. P. (2014). Testing the accuracy of an observation-based classifier for rapid detection of autism risk. Translational Psychiatry, 4, e424. https://doi.org/10.1038/tp.2014.65CrossRefPubMedPubMedCentral

Fournier, K. A., Hass, C. J., Naik, S. K., Lodha, N., & Cauraugh, J. H. (2010). Motor coordination in autism spectrum disorders: A synthesis and meta-analysis. Journal of Autism and Developmental Disorders, 40(10), 1227–1240.CrossRef

García-Pérez, R. M., Lee, A., & Hobson, R. P. (2007). On intersubjective engagement in autism: A controlled study of nonverbal aspects of conversation. Journal of Autism and Developmental Disorders, 37(7), 1310–1322.CrossRef

Goldman, S., Wang, C., Salgado, M. W., Greene, P. E., Kim, M., & Rapin, I. (2009). Motor stereotypies in children with autism and other developmental disorders. Developmental Medicine & Child Neurology, 51(1), 30–38. https://doi.org/10.1111/j.1469-8749.2008.03178.xCrossRef

Grossi, E., & Valbusa, G. M. (2020). Buscema detection of an autism EEG signature from only two EEG channels through features extraction and advanced machine learning analysis. Clinical EEG and Neuroscience. https://doi.org/10.1177/1550059420982424CrossRefPubMed

Hof, M., Tisseur, C., Berckelear-Onnes, I. V., Nieuwenhuyzen, A. V., & Ester, W. A. (2020). Age at autism spectrum disorder diagnosis: A systematic review and meta-analysis from 2012 to 2019. Autism, 2, 136236132097110.

Hutt, C., & Hutt, S. J. (1965). Effects of environmental complexity on stereotyped behaviours of children. Animal Behaviour, 13(1), 1–4. https://doi.org/10.1016/0003-3472(65)90064-3CrossRef

Jacob, S., Wolff, J. J., Steinbach, M. S., Doyle, C. B., Kumar, V., & Elison, J. T. (2019). Neurodevelopmental heterogeneity and computational approaches for understanding autism. Translational Psychiatry, 9(1), 63. https://doi.org/10.1038/s41398-019-0390-0CrossRefPubMedPubMedCentral

Jeni, L. A., Cohn, J. F., & Kanade, T. (2015). Dense 3D face alignment from 2D videos in real-time. 2015 11th IEEE International conference and workshops on automatic face and gesture recognition (FG) (pp. 1–8). IEEE.

Kang, J., Han, X., Song, J., Niu, Z., & Li, X. (2020). The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2020.103722CrossRefPubMed

Landa, R., & Garrett-Mayer, E. (2010). Development in infants with autism spectrum disorders: A prospective study. Journal of Child Psychology & Psychiatry, 47(6), 629–638.CrossRef

Li, B., Sharma, A., Meng, J., Purushwalkam, S., & Gowen, E. (2017). Applying machine learning to identify autistic adults using imitation: An exploratory study. PLoS ONE, 12(8), e0182652. https://doi.org/10.1371/journal.pone.0182652CrossRefPubMedPubMedCentral

Licari, M. K., Alvares, G. A., Varcin, K., Evans, K. L., Cleary, D., Reid, S. L., et al. (2020). Prevalence of motor difficulties in autism spectrum disorder: Analysis of a population-based cohort. Autism Research, 13(2), 298–306. https://doi.org/10.1002/aur.2230CrossRefPubMed

Liu, W., Li, M., & Yi, L. (2016). Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism Research, 9(8), 888–898. https://doi.org/10.1002/aur.1615CrossRefPubMed

Martin, K. B., Hammal, Z., Ren, G., Cohn, J. F., Cassell, J., Ogihara, M., et al. (2018). Objective measurement of head movement differences in children with and without autism spectrum disorder. Molecular Autism, 9(1), 14.CrossRef

Möricke, E., Buitelaar, J. K., & Rommelse, N. N. J. (2016). Do we need multiple informants when assessing autistic traits? The degree of report bias on offspring, self, and spouse ratings. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-015-2562-yCrossRefPubMed

Oi, M. (2010). Do Japanese children with high-functioning autism spectrum disorder respond differently to Wh-questions and Yes/No-questions? Clinical Linguistics & Phonetics, 24(9), 691–705. https://doi.org/10.3109/02699206.2010.488313CrossRef

Ozonoff, S., Young, G. S., Goldring, S., Greiss-Hess, L., Herrera, A. M., Steele, J., et al. (2008). Gross motor development, movement abnormalities, and early identification of autism. Journal of Autism and Developmental Disorders, 38(4), 644.CrossRef

Perochon, S., Di Martino, M., Aiello, R., Baker, J., Carpenter, K., Chang, Z., et al. (2021). A scalable computational approach to assessing response to name in toddlers with autism. Journal of Child Psychology and Psychiatry. https://doi.org/10.1111/jcpp.13381CrossRefPubMed

Plitt, M., Barnes, K. A., & Martin, A. (2015). Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NeuroImage: Clinical. https://doi.org/10.1016/j.nicl.2014.12.013CrossRef

Sorosky, A. D., Ornitz, E. M., Brown, M. B., & Ritvo, E. R. (1968). Systematic observations of autistic behavior. Archives of General Psychiatry, 18(4), 439–449.CrossRef

Tebartz van Elst, L., Pick, M., Biscaldi, M., Fangmeier, T., & Riedel, A. (2013). High-functioning autism spectrum disorder as a basic disorder in adult psychiatry and psychotherapy: Psychopathological presentation, clinical relevance and therapeutic concepts. European Archives of Psychiatry and Clinical Neuroscience, 263(2), 189–196. https://doi.org/10.1007/s00406-013-0459-3CrossRef

Wall, D. P., Dally, R., Luyster, R., Jung, J. Y., & Deluca, T. F. (2012a). Use of artificial intelligence to shorten the behavioral diagnosis of autism. PLoS ONE, 7(8), e43855. https://doi.org/10.1371/journal.pone.0043855CrossRefPubMedPubMedCentral

Wall, D. P., Kosmicki, J., DeLuca, T. F., Harstad, E., & Fusaro, V. A. (2012b). Use of machine learning to shorten observation-based screening and diagnosis of autism. Translational Psychiatry, 2, e100. https://doi.org/10.1038/tp.2012.10CrossRefPubMedPubMedCentral

Wan, G., Kong, X., Sun, B., Yu, S., Tu, Y., Park, J., et al. (2019). Applying eye tracking to identify autism spectrum disorder in children. Journal of Autism and Developmental Disorders, 49(1), 209–215. https://doi.org/10.1007/s10803-018-3690-yCrossRefPubMed

Wiggins, L. D., Baio, J., & Rice, C. (2006). Examination of the time between first evaluation and first autism spectrum diagnosis in a population-based sample. Journal of Developmental and Behavioral Pediatrics, 27(2 Suppl), S79-87.CrossRef

Yamagata, B., Itahashi, T., Fujino, J., Ohta, H., Nakamura, M., Kato, N., et al. (2018). Machine learning approach to identify a resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder. Brain Imaging and Behavior. https://doi.org/10.1007/s11682-018-9973-2CrossRef

Zhao, Z., Zhang, X., Li, W., Hu, X., Qu, X., Cao, X., et al. (2019). Applying machine learning to identify autism with restricted kinematic features. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2950030CrossRefPubMedPubMedCentral

Zhao, Z., Zhu, Z., Zhang, X., Tang, H., Xing, J., Hu, X., et al. (2021). Atypical head movement during face-to-face interaction in children with autism spectrum disorder. Autism Research: Official Journal of the International Society for Autism Research. https://doi.org/10.1002/aur.2478CrossRef

Titel: Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms
Auteurs: Zhong Zhao
Zhipeng Zhu
Xiaobin Zhang
Haiming Tang
Jiayi Xing
Xinyao Hu
Jianping Lu
Xingda Qu
Publicatiedatum: 11-07-2021
Uitgeverij: Springer US
Gepubliceerd in: Journal of Autism and Developmental Disorders / Uitgave 7/2022
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI: https://doi.org/10.1007/s10803-021-05179-2

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