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

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.

Artificial intelligence‐enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes

“…These results highlight the potential of ML in detecting subtle physiological variations characteristic of CAN in 12‐lead ECGs, demonstrating the usefulness and clinical relevance of this ML‐driven approach in CAN diagnostics. Recently, a study of ML in infants shows that ECGs contain important information about autism spectrum disorder familial likelihood through HRV and sympathetic and parasympathetic activities, 24 thus demonstrating the possible wide‐ranging utility of ML for ECG analysis beyond cardiac rhythm disorders.…”

Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes

“…These results highlight the potential of ML in detecting subtle physiological variations characteristic of CAN in 12-lead ECGs, demonstrating the usefulness and clinical relevance of this ML-driven approach in CAN diagnostics. Recently, a study of ML of infant shows that ECGs contain important information about autism spectrum disorder familial likelihood through HRV and sympathetic and parasympathetic activities [22]. Thus, demonstrating the possible wide-ranging utility of ML for ECG analysis beyond cardiac rhythm disorders.…”