A SURVEY OF AUDIO PROCESSING ALGORITHMS FOR DIGITAL STETHOSCOPES <s> Aortic Pulmonary Signal Decomposition <s> The objective of the present paper is to provide a detailed review of the most recent developments in instrumentation and signal processing of digital phonocardiography and heart auscultation. After a short introduction, the paper presents a brief history of heart auscultation and phonocardiography, which is followed by a summary of the basic theories and controversies regarding the genesis of the heart sounds. The application of spectral analysis and the potential of new time-frequency representations and cardiac acoustic mapping to resolve the controversies and better understand the genesis and transmission of heart sounds and murmurs within the heart-thorax acoustic system are reviewed. The most recent developments in the application of linear predictive coding, spectral analysis, time-frequency representation techniques, and pattern recognition for the detection and follow-up of native and prosthetic valve degeneration and dysfunction are also presented in detail. New areas of research and clinical applications and areas of potential future developments are then highlighted. The final section is a discussion about a multidegree of freedom theory on the origin of the heart sounds and murmurs, which is completed by the authors' conclusion. <s> BIB001 </s> A SURVEY OF AUDIO PROCESSING ALGORITHMS FOR DIGITAL STETHOSCOPES <s> Aortic Pulmonary Signal Decomposition <s> Describes a new approach based on the time-frequency representation of transient nonlinear chirp signals for modeling the aortic (A2) and the pulmonary (P2) components of the second heart sound (S2). It is demonstrated that each component is a narrow-band signal with decreasing instantaneous frequency defined by its instantaneous amplitude and its instantaneous phase. Each component Is also a polynomial phase signal, the instantaneous phase of which can be accurately represented by a polynomial having an order of thirty. A dechirping approach is used to obtain the instantaneous amplitude of each component while reducing the effect of the background noise. The analysis-synthesis procedure is applied to 32 isolated A2 and 32 isolated P2 components recorded in four pigs with pulmonary hypertension. The mean /spl plusmn/ standard deviation of the normalized root-mean-squared error (NRMSE) and the correlation coefficient (/spl rho/) between the original and the synthesized signal components were: NRMSE=2.1/spl plusmn/0.3% and /spl rho/=0.97/spl plusmn/0.02 for A2 and NRMSE=2.52/spl plusmn/0.5% and /spl rho/=0.96/spl plusmn/0.02 for P2. These results confirm that each component can be modeled as mono-component nonlinear chirp signals of short duration with energy distributions concentrated along its decreasing instantaneous frequency. <s> BIB002 </s> A SURVEY OF AUDIO PROCESSING ALGORITHMS FOR DIGITAL STETHOSCOPES <s> Aortic Pulmonary Signal Decomposition <s> The time interval between the aortic (A2) and pulmonary (P2) components of the second heart sound (S2) is an indicator of the presence and severity of several cardiac abnormalities. However, in many cases identification of the A2 and P2 components is difficult due to their temporal overlap and significant spectral similarity. In this work, we present a method to extract the A2 and P2 components from the S2 sound, by assuming their mutual statistical independence. Once extracted, the A2 and P2 components are identified by using a physiological reference signal. Results obtained from real data are encouraging, and show promise for utilizing the proposed method in a clinical setting to non-invasively tract the A2-P2 time interval. <s> BIB003
Besides the four main components of the cardiac cycle, there is a clinical interest in the analysis of some of its associated sub-components BIB002 . It has been recognized that S1 may be composed of up to four components produced during ventricular contraction BIB001 , although the complexity of this task has been a very difficult hurdle for the signal processing community. The S2 sound is more well known, being composed of an aortic component (A2), which is produced first during the closure and vibration of the aortic valve and surrounding tissues, followed by the pulmonary component (P2) produced by a similar process associated with the pulmonary valve BIB002 . BIB002 demonstrated that it is possible to model each component of S2 by a narrowband nonlinear chirp signal. Later  ) he adapted and validated this approach for the analysis and synthesis of overlapping A2 and P2 components of S2. To do so, the time-frequency representation of the signal is generated and then estimated and reconstructed using the instantaneous phase and amplitude of each component (A2 and P2). In this paper the accuracy evaluation was made by a simulated A2 and P2 components having different overlapping factors. The reported error was between 1% and 6%, proportional to the duration of the overlapping interval. Nigam BIB003 ) also presented a method for extracting A2 and P2 components by assuming them as statistically independent. To do so, four simultaneous auscultations are analyzed using blind source separation. The main advantage of this method is the lower dependence on the A2-P2 time interval, although it needs a non-conventional 4-sensor stethoscope. Leung (T. S.  ) also analyzed the splitting of S2 using time-frequency decomposition.