Early Detection System for Heartbeat Abnormalities in Autistic Children Using Support Vector Machine - Ahmad Ridwan (JCEIT: Journal of Computer Engineering and Information Technology, 2025)
This research aims to develop an early detection system for heart rate anomalies in autistic children based on Heart Rate Variability (HRV) to prevent tantrum behavior that can endanger the child's physical and psychological health. Based on previous research, children with autism spectrum disorder (ASD) show a significant increase in heart rate (HR), especially when experiencing stress or anxiety, with some cases reaching above 120 bpm. At the same time, control groups such as children with language disorders do not show a similar pattern. This leads to the hypothesis that physiological monitoring using non-invasive technologies, such as Photoplethysmography (PPG), can detect changes in HR before a tantrum occurs. The purpose of this study is to design a wearable device based on a pulse sensor and NodeMCU that can integrate HR in real-time, extract HRV features in the frequency domain (VLF, LF, HF, and LF/HF ratio), and classify normal and anomalous conditions using the Support Vector Machine (SVM) algorithm. The system is designed to notify parents or caregivers via a Telegram bot when HR exceeds 114 bpm. The research methodology was experimental, conducted on two subjects: a 7-year-old boy and a girl on the autism spectrum during learning, quiet, and tantrum activities. Results showed that HRV parameters increased significantly during the tantrum condition and even during learning, indicating activation of the sympathetic nervous system. The SVM classifier achieved 98.9% accuracy in the tantrum condition, 82% in the learning condition, but only 61.1% in the transition from quiet to tantrum. 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