Time-Frequency Representations for second heart sound analysis.
Several researches have tried to provide a means to analyze the second heart sound (S2) in an attempt to understand the functional mechanisms in its genesis and for diagnosis purposes. In this work we tested Time-Frequency Representation (TFR) for simulated S2 selecting and applying classical and modern TFRs such as the Spectrogram, the Wigner-Ville Distribution, the Time Varying Autoregressive (TVAR) model, the Scalogram, and the Hilbert-Huang Spectrum (HHS) by Empirical Mode Decomposition. Two performance measures are proposed, the first one based on local 2D correlations (rho) between the ideal and the estimated TFRs images, while the second one based on time moments of the TFR images to provide the normalized root-mean-square error (NRMSE). Under no noise conditions, the TFRs by HHS and the TVAR modeling, by the Burg algorithm, resulted in a rho(average) of 0.788 and 0.812, and NRMSE of 0.172 and 0.195, respectively. Therefore, based on the lowest NRMSE, HHS was considered the TFR with the best performance. Afterward, HHS was applied to real S2 acquired at the aortic and pulmonary focal points.
Reyes
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BA
Universidad Autónoma Metropolitana, Mexico City, Mexico. bersalex@hotmail.com
Charleston-Villalobos
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S
Gonzalez-Camarena
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Aljama-Corrales
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T
D000465:Algorithms; D001011:Aorta / Q000473:pathology; D003198:Computer Simulation; D006321:Heart / Q000502:physiology*; D006347:Heart Sounds; D006351:Heart Valves / Q000502:physiology; D006801:Humans; D008955:Models, Cardiovascular; D015233:Models, Statistical; D008962:Models, Theoretical; D010701:Phonocardiography; D011664:Pulmonary Valve / Q000473:pathology; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013997:Time Factors