Compressed sensing of ECG bio-signals using one-bit measurement matrices

Abstract

Compressed sensing (CS) is an emerging signal processing technique that enables sub-Nyquist sampling of sparse signals such as electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) bio-signals. Future CS signal processing systems will exploit significant time- and/or frequency-domain sparsity to achieve ultra-low-power bio-signal acquisition in the analog, digital, or mixed-signal domains. A measurement matrix of random values is key to one form of CS computation. It has been shown for ECG and EMG signals that signal-to-quantization noise ratios (SQNR) > 60 dB with compression factors up to 16X are achievable using uniform or Gaussian 6-bit random coefficients. In this paper, 1-bit random coefficients are shown also to give compression factors up to 16X with similar SQNR performance. This approach reduces hardware and saves energy concomitant with 1-bit versus 6-bit signal processing.