Heart Murmur Classification for Diagnostic Applications.

This paper presents an innovative methodology for applying signal processing techniques and machine learning to process recently available data for heart murmur detection and classification. The scope of this research is to improve upon current heart murmur detection research by investigating the classification of murmur characteristics, such as pitch, duration, configuration, and quality, which are necessary for diagnosis. Feature extraction was performed on each cardiac cycle for a total of 53 features. The machine learning models tested included 5 supervised models, with 3 of those being boosting models, and 13 voting classifier combinations. The efficiency of the feature extraction methodology and the machine learning algorithms was evaluated with a confusion matrix. The murmur detection algorithm achieved an F-score of 0.87.