Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Hadi Grailu
Volume 15, Issue 3 , December 2021, , Pages 247-262
Abstract
Today, auscultation is one of the most effective methods in monitoring heart disease. With the advancement of technology and the facilitation of telecare on the one hand, and the increasing need for high quality and long-term recording of cardiac audio signals on the other hand, the amount of data generated ...
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Today, auscultation is one of the most effective methods in monitoring heart disease. With the advancement of technology and the facilitation of telecare on the one hand, and the increasing need for high quality and long-term recording of cardiac audio signals on the other hand, the amount of data generated has increased and therefore, the storage and transmission of these signals has become a challenge. This, in turn, demonstrates the importance and necessity of using efficient methods for compression of these types of signals. In this paper, a lossy compression method is proposed for PCG signals recorded at a relatively high sampling rate so that it can control the quality of the compressed signal. This method is based on two techniques: "two-stage downsampling" and "pattern matching". The proposed two-stage downsampling technique increases the amount of compression ratio and at the same time reduces the computational complexity. The pattern matching technique is able to reduce the inter-period redundancy and therefore, increase the compression ratio. The simulation results of the proposed method on the two databases of the University of Michigan and the University of Washington showed that the two-stage downsampling and pattern matching techniques have a large contribution in increasing the compression ratio. The performance of the proposed method was evaluated according to the PRD and CR criteria and compared with that of some existing methods. In this evaluation, for the PRD range of 5%, the CR value was between 2500 and 3900 for the University of Michigan database and between 2500 and 4125 for the University of Washington database. Also, the results of applying the proposed method on the Pascal database showed that the efficiency of the proposed method depends to a large extent on the quality and regularity of the input PCG signals.
