Real-Time Diagnostic Integrity Meets Efficiency: A Novel Platform-Agnostic Architecture for Physiological Signal Compression
CoRR(2023)
Abstract
Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable
systems will play a pivotal role in clinical diagnosis, monitoring, and
treatment of important brain disorder diseases.
However, the real-time transmission of the significant corpus physiological
signals over extended periods consumes substantial power and time, limiting the
viability of battery-dependent physiological monitoring wearables.
This paper presents a novel deep-learning framework employing a variational
autoencoder (VAE) for physiological signal compression to reduce wearables'
computational complexity and energy consumption.
Our approach achieves an impressive compression ratio of 1:293 specifically
for spectrogram data, surpassing state-of-the-art compression techniques such
as JPEG2000, H.264, Direct Cosine Transform (DCT), and Huffman Encoding, which
do not excel in handling physiological signals.
We validate the efficacy of the compressed algorithms using collected
physiological signals from real patients in the Hospital and deploy the
solution on commonly used embedded AI chips (i.e., ARM Cortex V8 and Jetson
Nano). The proposed framework achieves a 91
XGBoost, confirming the approach's reliability, practicality, and scalability.
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