A novel methodology for evaluation of S wide split via estimated parameters.

Background And Objective: Aimed at the shortcomings of using time interval ( [Formula: see text] ) between the sounds produced by the aortic valve closure (A) and the pulmonary valve closure (P) to detect the wide splitting of the second heart sound (S), which are the [Formula: see text] easily influenced by the heartbeat and not easily distinguished from the fixed splitting of S without considering the entire respiratory phase, and from the third heart sound (S), this study proposes a novel methodology to detect the wide splitting of S using an estimated split coefficient of S ( [Formula: see text] ) combined with an adaptive number (N) of S.

Methodology: The methodology is orderly summarized as follows: Stage 1 describes the segmentation-based S automatic location and extraction. A Gaussian mixture model (GMM)-based regression model for S is proposed to estimate the positions of A and P, then an overlapping rate (OLR)-based [Formula: see text] and the [Formula: see text] are estimated, and finally, a N-S is automatically determined to calculate the statistics of [Formula: see text] and [Formula: see text] . In stage 3, based on the combination of estimated features, the detection of wide splitting of S is determined.

Results: The performance is evaluated using a total of 3350-period heart sounds from 72 patients, with an overall accuracy of 100%, F=1 and a Cohen's kappa value (κ) of 1.

Discussion: The significant contributions are highlighted: A novel GMM-based efficient methodology is proposed for estimating the characteristics of A and P. A novel OLR-based [Formula: see text] is defined to replace the current state-of-the-art criterion for evaluating the split degree of S. Considering respiration phases combined with CR are proposed for the high-precision diagnosis of S wide split.