Bayesian De-quantization and Data Compression for Low-Energy Physiological Signal Telemonitoring

TL;DR

This paper introduces a two-stage data compression method using quantized compressed sensing and Bayesian de-quantization for low-energy physiological signal telemonitoring, achieving accurate heart rate estimation with minimal data transmission.

Contribution

It proposes a novel combination of quantized compressed sensing and Bayesian de-quantization tailored for approximately sparse physiological signals in telemonitoring.

Findings

Achieved an average of 2.596 BPM error in heart rate estimation.

Reduced transmission data by 50% with 2-bit quantization.

Validated on real-world dataset during physical activity.

Abstract

We address the issue of applying quantized compressed sensing (CS) on low-energy telemonitoring. So far, few works studied this problem in applications where signals were only approximately sparse. We propose a two-stage data compressor based on quantized CS, where signals are compressed by compressed sensing and then the compressed measurements are quantized with only 2 bits per measurement. This compressor can greatly reduce the transmission bit-budget. To recover signals from underdetermined, quantized measurements, we develop a Bayesian De-quantization algorithm. It can exploit both the model of quantization errors and the correlated structure of physiological signals to improve the quality of recovery. The proposed data compressor and the recovery algorithm are validated on a dataset recorded on 12 subjects during fast running. Experiment results showed that an averaged 2.596 beat per minute (BPM) estimation error was achieved by jointly using compressed sensing with 50% compression ratio and a 2-bit quantizer. The results imply that we can effectively transmit n bits instead of n samples, which is a substantial improvement for low-energy wireless telemonitoring.