Abstract
A novel intelligent diagnostic system is proposed to diagnose heart sounds (HSs). The innovations of this system are primarily reflected in the automatic segmentation and extraction of the first complex sound ({ CS }_{1}) and second complex sound ({ CS }_{2}); the automatic extraction of the secondary envelope-based diagnostic features gamma _{_1}, gamma _{_2}, and gamma _{_3} from { CS }_{1} and { CS }_{2}; and the adjustable classifier models that correspond to the confidence bounds of the Chi-square (chi ^{2}) distribution and are adjusted by the given confidence levels (denoted as beta). The three stages of the proposed system are summarized as follows. In stage 1, the short time modified Hilbert transform (STMHT)-based curve is used to segment and extract { CS }_{1} and { CS }_{2}. In stage 2, the envelopes { CS _{1}}_{mathrm{F_{E}}} and { CS _{2}}_{mathrm{F_{E}}} for periods { CS }_{1} and { CS }_{2} are obtained via a novel method, and the frequency features are automatically extracted from { CS _{1}}_{mathrm{F_{E}}} and { CS _{2}}_{mathrm{F_{E}}} by setting different threshold value (Thv) lines. Finally, the first three principal components determined based on principal component analysis (PCA) are used as the diagnostic features. In stage 3, a Gaussian mixture model (GMM)-based component objective function f_{ et }(mathbf{x }) is generated. Then, the chi ^{2} distribution for component k is determined by calculating the Mahalanobis distance from {mathbf{x }} to the class mean mu _{_k} for component k, and the confidence region of component k is determined by adjusting the optimal confidence level beta _{k} and used as the criterion to diagnose HSs. The performance evaluation was validated by sounds from online HS databases and clinical heart databases. The accuracy of the proposed method was compared to the accuracies of other state-of-the-art methods, and the highest classification accuracies of 99.43%, 98.93%, 99.13%, 99.85%, 98.62%, 99.67% and 99.91% in the detection of MR, MS, ASD, NM, AS, AR and VSD sounds were achieved by setting beta _{k}(k=1, 2, ldots , 7) to 0.87,0.65,0.67,0.65,0.67,0.79 and 0.87, respectively.
Highlights
As an efficient method, using heart sound (HS) analysis is often used to evaluate heart function; this approach has been widely used to diagnose heart disease and evaluate heart functions, such as congenital heart disease classification[1], ventricular septal defect detection[2], blood pressure estimation[3] and congenital heart disease s creening[4], for children and adults
This study proposed a heart sound classification method based on improved MFCC features and convolutional recurrent 2021 neural networks, which achieved classification accuracy of 98% in the 2016 PhysioNeT/CinC Challenge database with dropout rate of 0.5
A novel intelligent system was proposed for diagnosing heart diseases with high CA
Summary
As an efficient method, using heart sound (HS) analysis is often used to evaluate heart function; this approach has been widely used to diagnose heart disease and evaluate heart functions, such as congenital heart disease classification[1], ventricular septal defect detection[2], blood pressure estimation[3] and congenital heart disease s creening[4], for children and adults. To improve the classification accuracy for diagnosing different types of heart disease and simplify the complexity of the diagnostic method, the smooth envelopes for CS1 and CS2 extraction in the frequency domain must be considered; more frequency widths corresponding to different Thv values should be used, and dimensionality reduction should be employed to reduce the number of features considered. Such a classification method could be applied in the efficient extraction of features for diagnosing heart diseases.
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