Morphological determination of pathological PCG signals by time and frequency domain analysis

With the development of diagnostic devices over the past few decades, the algorithmic classification of heart sound recordings has become possible. Although this field has been under research for a relatively long time, the classification of such recordings is not yet straightforward. We were given a large manually classified database of heart sounds with the challenge [1]. We worked together with an experienced cardiologist to find the aspects affecting the classifications. To algorithmically classify a heart sound recording as normal or abnormal, it is necessary in most cases to accurately locate both the fundamental heart sounds and the systolic and diastolic regions. For this purpose we used the method provided in the example entry [2][3]. Minor modifications were made, such as tuning some of the parameters to match the database parameters. In the classification of the heart sounds, we were looking for the morphological features of the abnormal signals, for example, mitral stenosis, mitral insufficiency, aortic stenosis, aortic insufficiency, tricuspid stenosis and tricuspid insufficiency. We extracted several features from both time and frequency domains, for example, the frequency properties of systolic and diastolic segments and resampled wavelet envelope features. The extracted features were classified by the help of a support vector machine. In order to train the classifier, we used a reduced, sorted dataset with a more balanced ratio of abnormal and normal signals. During the official phase, our best scores on a random subset were 77.2% sensitivity, 85.2% specificity and 81.2% final modified accuracy (MAcc). Our scores for the entire test dataset are 83.77% sensitivity, 76.8% specificity and 80.28% MAcc. Our scores for the entire training dataset are 93.08% sensitivity, 84.70% specificity and 88.70% MAcc.