Blockchain aided wearable piezoelectric technology for cardiac measurements using deep learning model

Dependable, contemporaneous sensor systems that can relyably operate despite motion artifacts, noise, and limited power resources are necessary for continuous heart health monitoring. We describe a multimodal technique to monitoring cardiovascular data using earable sensor devices in this paper. To enable self-powered detection of heart vibrations (PCG/PPG-derived waveforms), the suggested wearable employs piezoelectric materials to collect biomechanical energy from human motion. By enhancing high-fidelity cardiac waveforms using a Generative Adversarial Network (GAN) while maintaining morphological properties, medical datasets that are sparse or unbalanced may be addressed. A prediction model based on Kalman Filters is used to monitor cardiac states such heart rate variability (HRV), systolic timing intervals (STI), and probable arrhythmic patterns. This allows for real-time state estimates and noise suppression. The introduction of a lightweight consortium blockchain infrastructure guarantees safe multi-node access, data integrity, and provenance. Remote cardiac telemedicine is a good fit for this paradigm because it offers tamper-resistant medical records, patient identity security, decentralized verification, and visible audit trails. The combined GAN-Kalman architecture decreases noise by 30–45%, increases cardiac anomaly classification accuracy by 12–18%, and shortens real-time prediction latency, according to experimental simulations.