Compressed Sensing Implementation in Cardiac Signals
Compressed Sensing Implementation in Cardiac Signals, Proc International Workshop on Intelligent Data Acquisition and Advanced Computing Systems, Rende, Italy, Vol. I, pp. 96 - 101, September, 2009.
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This work presents the study of a compressed sensing implementation in a system which acquires three cardiac signals, ballistocardiogram, electrocardiogram and photoplethysmogram. In order to accurately estimate heart rate and its variability, cardiac signals must be acquired at frequencies of about 1kHz, but since these signals have a sparse representation in some transformation basis, namely wavelet domain, compressed sensing paradigm states that they can be recovered from a small number of projections in another basis incoherent with the first. The signals’ compressibility was assessed, then TwIST algorithm was applied and reconstruction quality was measured for a number of different signal-to-noise ratios, compression rates, and sparsity basis. The analysis was completed by evaluating the algorithm’s computation time and heart rate deviation of the reconstructed signals.