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

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.