Fast Compressive Sensing Based on Dominant Frequency Estimation

This work investigates the theoretical analysis to enable fast and accurate estimation of dominant frequencies from randomly sampled signals by compressive sensing (CS). We show that dominant frequencies can be discovered using partially computed Discrete Cosine Transform (DCT). We also propose a new system structure with an estimation unit that enables the signal reconstruction to be selectively bypassed for CS-based devices on signals with dominant frequencies, thus increasing the responsiveness and further reducing the power consumption. For verification, we design a photoplethysmagram (PPG) based heart rate monitor using the proposed algorithm. The accuracy is tested using MIMIC database. The detected heart rate is within 1 beat per minute from the reference over 99% of the data.