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

arXiv: https://arxiv.org/abs/2504.15178 · HTML: https://mlsanity.com/paper/2504.15178/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2025.

## Built on by (most cited first, 1 of 1)

- [Saturation-Aware Predictive Quantization for Low-Power ECG Acquisition: A Benchmark of Taylor, Adaptive-Order, Kalman, and LSTM Predictors](https://mlsanity.com/paper/2608.00992/) (2026)

## Abstract (arXiv)

This project addresses the need for efficient, real-time analysis of biomedical signals such as electrocardiograms (ECG) and electroencephalograms (EEG) for continuous health monitoring. Traditional methods rely on long-duration data recording followed by offline analysis, which is power-intensive and delays responses to critical symptoms such as arrhythmia. To overcome these limitations, a time-domain ECG analysis model based on a novel dynamically-biased Long Short-Term Memory (DB-LSTM) neural network is proposed. This model supports simultaneous ECG forecasting and classification with high performance-achieving over 98% accuracy and a normalized mean square error below 1e-3 for forecasting, and over 97% accuracy with faster convergence and fewer training parameters for classification. To enable edge deployment, the model is hardware-optimized by quantizing weights to INT4 or INT3 formats, resulting in only a 2% and 6% drop in classification accuracy during training and inference, respectively, while maintaining full accuracy for forecasting. Extensive simulations using multiple ECG datasets confirm the model's robustness. Future work includes implementing the algorithm on FPGA and CMOS circuits for practical cardiac monitoring, as well as developing a digital hardware platform that supports flexible neural network configurations and on-chip online training for personalized healthcare applications.