Computationally-efficient compressive sampling for low-power pulseoximeter system

Abstract

This paper presents a computationally-efficient compressive sampling system for photoplethysmogram (PPG) signals. The approach relies on the exploration of the Discrete Cosine Transform (DCT) as sparsifying basis for reconstruction of randomly sampled signals, along with an overlapped window reconstruction algorithm which improves reconstruction accuracy of shorter windows, without sacrificing reconstruction accuracy. Simulation results demonstrate a reduction in CPU execution time by a factor of 2.4 without degradation of reconstruction accuracy compared to a traditional longer window reconstruction approach. This facilitates computationally-efficient, low-latency signal reconstruction.