Combined ECG Analysis Approach Based on Detection of Deviation from the Personal or Population Norm Using Wearable Devices

This paper presents a strategy for developing a real-time electrocardiogram (ECG) monitoring and analysis system using wearable devices, emphasizing personalization and continuous cardiac health management. The proposed methodology introduces a two-stage approach: generating a personal ECG norm model and establishing a robust monitoring architecture. The process involves systematic ECG data collection, pre-processing to eliminate noise, training a personalized model, and periodically updating this model with new data to establish a baseline for individual cardiac activity. The conceptual architecture assures consistent ECG monitoring, immediate data processing, and comprehensive analysis to detect any abnormalities in the ECG data, thereby providing timely and reliable ECG assessments. Expected outcomes include accurate detection of cardiac events, immediate intervention capabilities, and continuous monitoring. The implementation presents several challenges such as maintaining data quality and model accuracy, ensuring user compliance, safeguarding data privacy, and navigating the technical limitations of wearable devices. Solutions like employing advanced and adaptive algorithms, providing user training, employing robust data encryption methods, and developing optimized algorithms are discussed to mitigate these challenges. The paper concludes by identifying future research and development directions, including improving model efficacy, enhancing user interaction, bolstering data security, and refining algorithms, positioning the personalized, real-time ECG monitoring system as a crucial element in individualized healthcare and management of cardiac health.