Interpersonal Motor Coordination in Children with Autism and the Establishment of Machine Learning Models to Objectively Classify Children with Autism and Typical Development

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2024-06-06 DOI:10.1016/j.irbm.2024.100838
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引用次数: 0

Abstract

Background

The global prevalence of autism spectrum disorder (ASD) is around 1%. Yet the current diagnosis of ASD mainly depends on clinicians' experience and caregivers' report, which are subjective, time consuming, and labor demanding. An objective and efficient way to diagnose ASD is urgently needed. The objective of this study was to quantify an omnipresent yet least studied behavioral characteristic in children with ASD – interpersonal motor coordination (IMC), and to investigate the feasibility of using IMC related features to identify ASD by implementing machine learning algorithms.

Methods

Twenty children with ASD and twenty-three children with typical development (TD) were filmed in a conversation with an interviewer. Motion energy analysis was implemented to obtain the movement time series, and cross wavelet analysis (CWA) quantified the level of IMC at different movement frequencies. Machine learning algorithms were utilized to examine whether these two groups of children could be accurately classified using features of IMC.

Results

Statistical analysis revealed reduced IMC in the ASD group at relatively high movement frequencies. The establishment of machine learning (ML) models showed that the maximum classification accuracy was 85.37% (specificity = 95.24%, sensitivity = 75.00%) using five original coherence values computed with CWA. In addition, the classification accuracy could be improved to 92.68% (specificity = 95.24%, sensitivity = 90.00%) with three novel features created by taking the sum of statistically significant features.

Conclusions

Children with ASD demonstrated an atypical profile of IMC, and IMC could be used to objectively classify children with ASD and TD. In addition, our analyses showed that creating novel features based on statistically significant features could help improve classification performance. It is proposed that such economic, contactless, and calibration-free approach to data collection might well serve both ASD research and practice, particularly early objective identification. However, this study could be improved with respect to larger sample size with balanced gender ratio and different severity.

Abstract Image

自闭症儿童的人际运动协调能力以及建立机器学习模型对自闭症儿童和典型发育儿童进行客观分类
背景自闭症谱系障碍(ASD)的全球患病率约为 1%。然而,目前对自闭症谱系障碍的诊断主要依赖于临床医生的经验和护理人员的报告,这些都是主观的、耗时耗力的。因此亟需一种客观有效的方法来诊断 ASD。本研究旨在量化 ASD 儿童无处不在但研究最少的行为特征--人际运动协调(IMC),并通过机器学习算法研究使用 IMC 相关特征识别 ASD 的可行性。通过运动能量分析获得运动时间序列,并通过交叉小波分析(CWA)量化不同运动频率下的 IMC 水平。结果统计分析显示,在相对较高的运动频率下,ASD 组儿童的 IMC 水平较低。机器学习(ML)模型的建立表明,使用 CWA 计算出的五个原始一致性值,分类准确率最高可达 85.37%(特异性 = 95.24%,灵敏度 = 75.00%)。结论 ASD 儿童的 IMC 表现出非典型特征,IMC 可用于对 ASD 和 TD 儿童进行客观分类。此外,我们的分析表明,在具有统计学意义的特征基础上创建新特征有助于提高分类性能。我们建议,这种经济、无接触、无需校准的数据收集方法可以很好地服务于 ASD 研究和实践,尤其是早期客观识别。不过,这项研究还可以在扩大样本量、平衡性别比例和不同严重程度方面加以改进。
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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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