Classification of normal and abnormal heart sounds

Heart hemodynamic status and detection of a cardiovascular disease can be evaluated by analyzing and visualizing the heart waveform through graphs called the Phonocardiogram (PCG). The normal sounds of the heart generate signals that are in the audible frequency range of the human ear. Due to the significance of cardiac auscultation for recognizing pathological cardiac status, there has been special interest in automating the classification of heart sounds in the past years. The objective of this research is to present an automatic classification algorithm for anomaly (normal vs. abnormal heart status) of PCG recordings. For this purpose, distinctive time and frequency features are extracted out of heart sound signals to build a learning model using random forest. The accuracy of the proposed algorithm is about 12% better than state of the art.