Development of an Intelligent Wearable ECG Sensor for Predicting Risk Situations of Premature Ventricular Contraction (PVC) With a Mobile Application

Wearable electrocardiogram (ECG) sensors have emerged as a promising technology for real-time heart monitoring, particularly in detecting premature ventricular contraction (PVC), a prevalent arrhythmia that can lead to severe cardiac complications. Despite advancements in wearable medical technology, there is a knowledge gap in integrating machine learning for real-time prediction and personalized risk assessment of PVC. This study presents the development of an intelligent wearable ECG sensor integrated with a mobile application that employs machine learning algorithms to predict PVC risk situations. The system processes ECG signals, detects abnormal heart rhythms, and provides real-time alerts to users and healthcare providers. Findings indicate that the proposed device enhances early detection accuracy, reduces false positives, and enables continuous remote monitoring. The results underscore the potential of AI-driven wearable ECG sensors in improving cardiac health outcomes and emergency response strategies.