Cybersecure End-to-End FPGA-Accelerated ECG Monitoring for Precision Diagnosis With Personalized CWT and Adversarial Defense

Tiancheng Cao, Wei Soon Ng, Chen Shen, Hongtao Li, Rong Tan, Dawei Wang, Hen-Wei Huang

Abstract: Advancements in wearable technology and edge computing have transformed cardiovascular monitoring, driving the demand for private, secure, and real-time diagnostic solutions. This paper presents an edge-wearable ECG monitoring system that integrates personalized continuous wavelet transform (CWT) preprocessing, a DeepFool–FGSM adversarial defense, and an optimized parallel PoolFormer architecture for resource-constrained FPGA deployment. The personalized CWT captures individual-specific ECG features and mitigates model-inversion privacy risks. The defense approach balances robustness and computational efficiency and reduces hardware complexity and energy via quantization-aware training (QAT). Evaluations on field programmable gate array (FPGA) confirm high diagnostic accuracy (98.93%), real-time inference (latency <1.7 ms), and improved robustness against adversarial perturbations, with 0.055 W FPGA-core power. Together, the system delivers confidentiality, integrity, and availability for cybersecure, personalized ECG monitoring at the edge.