A classification methodology using Choi-Williams distribution and support vector regression achieved 97.85% accuracy in distinguishing normal from pathologic phonocardiographic recordings.
A nonlinear approach using support vector regression of time-frequency representations accurately classifies normal and pathologic phonocardiographic recordings with 97.85% accuracy.
Absolute Event Rate: 97.85% vs 96.78%
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%.
Jaramillo et al. (Fri,) conducted a other in Cardiac murmurs (n=22). Choi-Williams distribution (CWD) with support vector regression vs. Short Time Fourier Transform (STFT) was evaluated on Classification accuracy. A classification methodology using Choi-Williams distribution and support vector regression achieved 97.85% accuracy in distinguishing normal from pathologic phonocardiographic recordings.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: