An Efficacy of Spectral Features with Boosted Decision Tree Algorithm for Automatic Heart Sound Classification

This research work aims to classify the audio signals received from heart into normal/abnormal. The heart sound perceived has been referred as phonocardiogram (PCG) signals. An attempt has been made to identify a set of features that provide more accurate results for classifying PCG under designated categories using a variant of decision tree algorithm. After applying 6th order butter worth band-pass filter on PCG signals, the new features, viz. Tonnetz, Spectral contrast, and Chroma have been extracted. Further, XGBOOST, a variant of the decision tree has been used for classifying unsegmented PCG signals. The benchmark datasets, PhysioNet 2016, and PASCAL 2011 have been taken for validating the proposed methodology presented here. PhysioNet 2016 is comprised of sub-datasets, viz. A–F which contain a total of 3,240 PCG recordings, whereas the PASCAL 2011 contains 415 heart sound signals. The proposed approach considers a new feature set in conjunction with the existing ones; and it has resulted in mean accuracy, sensitivity, and specificity scores as 95.2, 94.22 and 96.18 respectively.