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

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