Real-Time Diagnostic Integrity Meets Efficiency: A Novel Platform-Agnostic Architecture for Physiological Signal Compression
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.
Keywords
Cite
@article{arxiv.2312.12587,
title = {Real-Time Diagnostic Integrity Meets Efficiency: A Novel Platform-Agnostic Architecture for Physiological Signal Compression},
author = {Neel R Vora and Amir Hajighasemi and Cody T. Reynolds and Amirmohammad Radmehr and Mohamed Mohamed and Jillur Rahman Saurav and Abdul Aziz and Jai Prakash Veerla and Mohammad S Nasr and Hayden Lotspeich and Partha Sai Guttikonda and Thuong Pham and Aarti Darji and Parisa Boodaghi Malidarreh and Helen H Shang and Jay Harvey and Kan Ding and Phuc Nguyen and Jacob M Luber},
journal= {arXiv preprint arXiv:2312.12587},
year = {2024}
}