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% E 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.

Questo articolo esplora i giri e le loro implicazioni.

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