ECG Recordings as Predictors of Very Early Autism Likelihood: A Machine Learning Approach

Author:

Tilwani Deepa1234ORCID,Bradshaw Jessica345,Sheth Amit12ORCID,O’Reilly Christian1234ORCID

Affiliation:

1. Artificial Intelligence Institute, University of South Carolina, Columbia, SC 29208, USA

2. Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA

3. Carolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC 29208, USA

4. Institute for Mind and Brain, University of South Carolina, Columbia, SC 29208, USA

5. Department of Psychology, University of South Carolina, Columbia, SC 29208, USA

Abstract

In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1–2 years of age, but ASD diagnoses are not typically made until ages 2–5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker in 3–6-month-old infants. We recorded the heart activity of infants at typical and elevated familial likelihood for ASD during naturalistic interactions with objects and caregivers. After obtaining the ECG signals, features such as heart rate variability (HRV) and sympathetic and parasympathetic activities were extracted. Then we evaluated the effectiveness of multiple machine learning classifiers for classifying ASD likelihood. Our findings support our hypothesis that infant ECG signals contain important information about ASD familial likelihood. Amongthe various machine learning algorithms tested, KNN performed best according to sensitivity (0.70 ± 0.117), F1-score (0.689 ± 0.124), precision (0.717 ± 0.128), accuracy (0.70 ± 0.117, p-value = 0.02), and ROC (0.686 ± 0.122, p-value = 0.06). These results suggest that ECG signals contain relevant information about the likelihood of an infant developing ASD. Future studies should consider the potential of information contained in ECG, and other indices of autonomic control, for the development of biomarkers of ASD in infancy.

Funder

Carolina Autism & Neurodevelopment Center at the University of South Carolina

National Institute of Mental Health

Publisher

MDPI AG

Subject

Bioengineering

Reference61 articles.

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