Time-Series Analysis on Edge-AI Hardware for Healthcare Monitoring

Jinhai Hu

Progress Report for PhD Confirmation Exercise , 2022

This project presents a novel time-domain ECG signal analysis model using a dynamically-biased Long Short-Term Memory (DB-LSTM) neural network, capable of performing both ECG forecasting and classification with high accuracy and efficiency. The model achieved over 98% accuracy and a normalized mean square error below 10⁻³ for forecasting, while classification reached over 97% accuracy with fewer training parameters and fast convergence. Designed for real-time, low-power applications, the network uses low-bit quantization (INT4/INT3) with minimal loss in classification accuracy and no degradation in forecasting performance. The model's robustness was validated through simulations on multiple ECG datasets. Future work includes deploying the algorithm on FPGA and CMOS hardware for continuous cardiac monitoring, with plans to develop a flexible AI platform for neural network simulation and implement on-chip online training for healthcare applications.

Link to Paper