Compressed Sensing Σ-Δ Modulators and Recovery Algorithm for Multi-Channel Bio-Signal Acquisition

Abstract

Compressed sensing (CS) is a sampling scheme that exploits signal sparsity to reduce the digitizing rate and thus improve analog-to-digital converter (ADC) power efficiency. By decoupling the analog signal frequency and digitizing rate, the ADC sampling rate is determined by the information rate rather than the maximum signal frequency. Herein we propose a compressed sensing scheme for multi-channel bio-signal recording based on sigma-delta modulation (SDM). The dot products between the signal and measuring vectors are realized by the inherent integration of the SDM with relieved saturation concern. Compared to other CS scheme, front-end based on CS SDM scales better with continuing advances in CMOS technology. A sparse sensing matrix and modified recovery algorithm that exploits similar sparse signatures across multiple channels improve both chip area efficiency and signal recovery accuracy. Detailed analyses are validated by extensive simulation results.