Robust AI-Driven Arrhythmia Detection via Compressive Sensing and Lightweight CNN

作者：Yuliang Ma, Jiayue Zong, Xiaoji Ren, Zhiyu Li, Kun Hao, Ye Yuan, Guoren Wang · 发表于：IEEE Internet of Things Journal · 年份：2025 · DOI：10.1109/jiot.2025.3587321 · 被引用次数：2 · 研究领域：ECG Monitoring and Analysis

Cardiac arrhythmia detection is critical for ensuring robust and reliable healthcare services in Symbiotic IoT networks, where dynamic resource constraints and real-time responsiveness coexist. While prior work focuses on improving waveform detection algorithms, challenges remain in jointly optimizing robustness, computational efficiency, and diagnostic accuracy under noisy and resource-limited environments. This article proposes a novel compressive sensing-enabled lightweight convolutional neural network architecture tailored for Symbiotic IoT networks, addressing these challenges through two key innovations: 1) a heart rate variability (HRV)-pattern branch that leverages R–R interval features to enhance morphological analysis robustness and 2) a heartbeat-driven compressive sensing branch that consolidates electrocardiography signals at cardiac cycle granularity to reduce computational overhead while maintaining diagnostic reliability. The unified framework achieves dual optimization of precision and efficiency, demonstrating robust performance under varying noise levels and resource constraints. Evaluations on the MIT-BIH dataset show that the HRV-enhanced branch achieves 99.57% accuracy without increased time overhead, while the compressive sensing pipeline accelerates processing by 40% through heartbeat-based signal integration. These results highlight the framework’s suitability for Symbiotic IoT networks, where AI services must balance real-time responsiveness, computa...