2015/06/06 by Benyuan Liu, Hongqi Fan, Liu, Benyuan +5
Computer Science · Engineering · Mathematics · Medicine · #Analog and Mixed-Signal Circuit Design #ECG Monitoring and Analysis #FOS: Computer and information sciences #Information Theory (cs.IT) #Wireless Body Area Networks #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1506.02154
arxiv created 2015/06/06 · openalex publication_date 2015/06/06 · arxiv updated 2015/06/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.