Real-Time Diagnostic Integrity Meets Efficiency: A Novel Platform-Agnostic Architecture for Physiological Signal Compression

TL;DR

This paper introduces a deep-learning based signal compression framework using VAE that significantly reduces power consumption and data size in wearable physiological monitoring devices, enabling real-time clinical applications.

Contribution

It presents a novel VAE-based compression method for physiological signals that outperforms existing techniques in compression ratio and is validated on real patient data and embedded AI hardware.

Findings

Achieves a 1:293 compression ratio for spectrogram data.

Maintains 91% seizure detection accuracy after compression.

Validated on real patient data and embedded AI chips.

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% seizure detection accuracy using XGBoost, confirming the approach's reliability, practicality, and scalability.