Heart sound classification based on temporal alignment techniques

The ability to accurately stratify patients at risk of adverse cardiovascular outcomes using heart sound recordings could result in earlier treatment and improved patient outcomes. However, there remain several challenges associated with risk stratifying patients based on the phono-cardiogram (PCG) alone. First, inter-patient differences can make it challenging to learn a model that generalizes well across patients. Second, heterogeneity introduced by the collection environment of the recordings can render a classifier trained on one population useless when applied to another To address these challenges we explore the use of temporal alignment techniques, in particular dynamic time warping (DTW). Using DTW we compare heart sounds within and across subjects/recordings. These DTW based features, coupled with widely used spectral MFCC coefficients, serve as input to a linear SVM. Applied to the held-out test set our classifier obtained a test score of 82.4%, suggesting that temporal alignment techniques can effectively reduce the effects of inter-patient variability and mitigate the differences introduced by heterogeneous data collection environments.