Application of the matching pursuit method for structural decomposition and averaging of phonocardiographic signals

The paper evaluates the performance of an automatic adaptive time-frequency method to detect each cardiac cycle of a phonocardiogram (PCG) and extract average heart sounds and PCG cycles. The proposed method combines a global search of the PCG, in terms of the energy distribution of the most important components, with a local search relating to the specific events found within a cardiac cycle. The method is applied to 100 PCG recordings from 50 patients with an aortic bioprosthetic valve. The performance of the proposed method is compared with a commonly used semi-automatic method that is based on the combined analysis of an electrocardiogram (ECG) and the PCG signal. Results show that the proposed method clearly outperforms the semiautomatic method, especially in the case of patients with malfunctioning bioprostheses. By eliminating the need to record an ECG as the time-reference signal, this method reduces hardware overheads when analysis of PCG signals is the primary aim. It is also independent of subjective human judgment for selection of reference templates and threshold levels. Furthermore, the method is robust to artefacts, background noise and other kinds of signal interferences. With minor modifications, the procedure described could be applied to other types of biomedical signal in order to extract coherent transient components and identify specific events