Neural Architecture Search-Based ECG Classification for Low-Power Edge Deployment in IoT Healthcare Systems

Abstract

Objective: To develop and validate a deployment-centric ECG arrhythmia classification framework for low-power wearable edge devices, with emphasis on efficient on-device screening and reduced communication burden in remote cardiac monitoring workflows, optimized for seamless integration into clinical remote monitoring workflows using resource-constrained wearable IoT edge devices.Methods and Procedures: We design a clinically-oriented wearable-to-phone workflow in which the edge device performs on-device ECG inference and transmits actionable anomaly summaries to a smartphone via Bluetooth Low Energy (BLE). This approach reduces latency, energy cost, and privacy risks compared to continuous raw-signal transmission. A NAS process tailored to arrhythmia detection automatically explores candidate architectures under diverse compute and memory constraints to identify feasible models for embedded platforms. Multiple deployment-ready variants are generated via quantization and pruning, and implemented on an ARM Cortex-M33-based system. System efficiency is quantified through end-to-end measurements of energy consumption and data throughput, ensuring its readiness for long-term ambulatory monitoring.Results: The deployed models achieve real-time inference on an ARM Cortex-M33 with as little as 21.7 kB RAM and 265k MACs. After optimization, energy consumption is reduced to 5.51 mJ per inference. Compared with the original model, RAM usage is reduced by 53.6%, with only a 0.5% absolute decrease in accuracy (97.9% to 97.4%). Hardware-in-the-loop power profiling confirms significant efficiency gains, validating the clinical feasibility of the proposed selective offloading strategy.Conclusion: A NAS-guided, deployment-centric pipeline-spanning architecture search, compression/mapping, and embedded power profiling enables accurate, real-time ECG classification within stringent wearable constraints. The selective-offloading strategy bridges the gap between laboratory algorithms and practical cardiac surveillance by providing a high-fidelity, low-power, and clinically-aware solution for continuous arrhythmia screening.