  Cardiovascular Disease (CVD) is considered as one of the principal causes of
death in the world. Over recent years, this field of study has attracted
researchers' attention to investigate heart sounds' patterns for disease
diagnostics. In this study, an approach is proposed for normal/abnormal heart
sound classification on the Physionet challenge 2016 dataset. For the first
time, a fixed-length feature vector; called i-vector; is extracted from each
heart sound using Mel Frequency Cepstral Coefficient (MFCC) features.
Afterwards, Principal Component Analysis (PCA) transform and Variational
Autoencoder (VAE) are applied on the i-vector to achieve dimension reduction.
Eventually, the reduced size vector is fed to Gaussian Mixture Models (GMMs)
and Support Vector Machine (SVM) for classification purpose. Experimental
results demonstrate the proposed method could achieve a performance improvement
of 16% based on Modified Accuracy (MAcc) compared with the baseline system on
the Physoinet dataset.
