Sleep Pattern Analysis Using Machine Learning in Children with Neurodevelopmental Disorders
DOI:
https://doi.org/10.22399/ijcesen.5124Keywords:
Sleep Pattern Analysis, Neurodevelopmental Disorders, Machine Learning, Pediatric, Sleep Monitoring, Wearable SensorsAbstract
The problem of sleep disorders is extremely widespread in children with neurodevelopmental disorders (NDDs), including Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD). It has a significant impact on cognitive, behavioral, and emotional development. This research suggests a machine-learning-based model of examining sleep patterns among children with NDDs with the aid of multimodal data that have been acquired through wearables, polysomnography, and other behavioral outcomes. The framework uses data preprocessing, feature engineering, and predictive modelling, including conventional machine learning methods and deep learning methods, to recognize abnormal sleep patterns and assess the severity of the disorder. The main characteristics of sleep, including sleep duration, latency, fragmentation, and circadian rhythm consistency, are obtained and examined. Another implementation of explainable AI is also incorporated into the proposed approach to promote interpretability and clinical relevance. The model has been shown to accurately diagnose sleep abnormalities and provide actionable information, facilitating early diagnosis, patient-specific intervention, and better management of neurodevelopmental disorders in children through experimental evaluation.
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