ABSTRACT
Discrimination of murmurs in heart sounds is accomplished by means of time-frequency representations (TFR) which help to deal with non-stationarity. Nevertheless, classification with TFR is not straightforward given their large dimension and redundancy. In this paper we compare several methodologies to apply Principal Component Analysis (PCA) to TFR as a dimensional reduction scheme, which differ in the form that features are represented. Besides, we propose a method which maximizes information among TFR preserving information within TFRs. Results show that the methodologies that represent TFRs as matrices improve discrimination of heart murmurs, and that the proposed methodology shrinks variability of the results.
Subject(s)
Diagnosis, Computer-Assisted/methods , Heart Auscultation/methods , Heart Murmurs/diagnosis , Pattern Recognition, Automated/methods , Sound Spectrography/methods , Artificial Intelligence , Discriminant Analysis , Humans , Principal Component Analysis , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
This paper presents a nonlinear approach for time-frequency representations (TFR) data analysis, based on a statistical learning methodology - support vector regression (SVR), that being a nonlinear framework, matches recent findings on the underlying dynamics of cardiac mechanic activity and phonocardiographic (PCG) recordings. The proposed methodology aims to model the estimated TFRs, and extract relevant features to perform classification between normal and pathologic PCG recordings (with murmur). Modeling of TFR is done by means of SVR, and the distance between regressions is calculated through dissimilarity measures based on dot product. Finally, a k-nn classifier is used for the classification stage, obtaining a validation performance of 97.85%.
