Abstract
An algorithm for the classification of the phonocardiogram (PCG) waveform is developed. Four main stages that are effective to detect the diagnostic features from PCG, are involved in this classification procedure. The segmentation of the primary heart sounds (PHS) is the first stage. By analysis of the dominant frequency peaks (DFPs), the PHSs are recognized accurately in the succeeding stage. A dynamic time warping (DTW) algorithm is used in the third stage to find the diagnostic features of the systole and diastole. In the last stage, click detection is performed by time series prediction. Based on the diagnostic features extracted by the above stages, the PCG can be easily interpreted by the tree-decision method. Ten PCGs are tested and it is shown that the classification method developed has a good performance.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
