A Novel Segmentation-based Adaptive Feature Extraction Methodology for Discriminating Heart Sounds

To utilize heart sound features that may vary according to their suitability for segmentation, automatic adaptive feature extraction methodology is proposed to discriminate heart sounds. The innovation of this methodology is primarily reflected in the automatic segmentation and extraction of the first complex heart sound $(CS_{1})$ and second complex heart sound $(CS_{2})$ or each cardiac sound (CS), and automatic extraction of the segmentation-based frequency feature $FF_{1}$ or $FF_{2}$ , determination of the diagnostic features $[\gamma_{11},\ \gamma_{12}]$ and $[\gamma_{21},\ \gamma_{22}, \gamma_{23}]$ . Two stages corresponding to the implementation of the novel methodology are summarized as follows. In stage 1, the time intervals between two sequential peaks are automatically calculated and statistically analyzed, and the result is used to determine whether a given heart sound can be segmented. Stage 2 involves automatic extraction of segmentation-based adaptive features for adapting the heart sound to the frequency domain. The performance evaluation was validated using the scatter diagrams of the features extracted from the heart sounds from online databases and clinical databases.