Arrhythmia Detection in Wearable ECG Monitors under Biomedical Signal Processing and Routing Diversity

Abstract

Cardiovascular diseases remain a leading cause of mortality globally, necessitating continuous and reliable monitoring of cardiac health. Wearable electrocardiogram monitors have emerged as a pivotal technology for the early detection of arrhythmia, transitioning clinical diagnostics to ambulatory and remote settings. However, the efficacy of these devices is frequently compromised by motion artifacts, environmental interference, and the inherent unreliability of wireless data transmission across dynamic body area networks. This paper provides a comprehensive examination of how advanced biomedical signal processing and routing diversity converge to facilitate robust arrhythmia detection in wearable ecosystems. By systematically analyzing the preprocessing, feature extraction, and classification of cardiac signals, we elucidate the mechanisms through which noise is mitigated and diagnostic features are isolated. Concurrently, we investigate the critical role of routing diversity in wireless body area networks, demonstrating how multipath data transmission protocols prevent packet loss and ensure the timely delivery of critical physiological data to processing hubs. A methodological framework integrating adaptive signal quality assessment with diversity-aware routing is proposed and evaluated. The analysis reveals that harmonizing signal processing algorithms with dynamic network topologies substantially enhances both the diagnostic accuracy of arrhythmia detection and the energy efficiency of the wearable network. Ultimately, this research bridges the gap between biomedical engineering and network communications, offering a holistic perspective on the design and implementation of next-generation continuous cardiac monitoring systems.