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

Compressed sensing (CS) exploits signal sparsity in some domain to enable sub-Nyquist sampling which increases the energy efficiency of analog-to-digital conversion (ADC) and downstream data processing circuits–the sampling frequency is determined by the information rate, not the usual Nyquist rate. CS techniques for wireless multi-channel bio-signal recording applications based on sigma-delta modulation (SDM) are detailed and used to validate a multi-channel recovery algorithm. The SDM topology allows the required dot product calculations between the measurement and signal vectors to be performed in conjunction with its inherent integration using minimal additional circuitry. It eliminates opamp output saturation concerns and benefits directly from still ongoing Moore's Law CMOS technology scaling. Finally, a sparse sensing matrix and recovery algorithm are described that exploit similar sparse signatures across multiple channels to improve both signal recovery accuracy and chip area efficiency. Simulation results validate the concepts.