Feature selection algorithms highlight the importance of the systolic segment for normal/murmur PCG beat classification

Abstract

This paper proposes a method using statistical local and global features for classifying healthy and murmur heart sound recordings from phonocardiogram signals. Classification requires features extraction step that converts each signal into a sequence of feature vectors composed of static and dynamic energy coefficients computed from overlapped analysis windows. Firstly, we propose, for each heartbeat, to extract local features from the local consecutive regions (1st Sound, Systole, 2nd Sound, Diastole) and global ones from the global region. For each region, the features are the statistical features (mean and standard deviation) computed on the feature vector sequence plus the duration. Secondly, we propose to select the relevant features using filter approach based on mutual information criteria. The extraction and selection methods are validated using K nearest neighbor and Gaussian Mixture Models as classifiers. The classification system were evaluated on a sub-dataset of the public PASCAL heart sounds classifying challenge. Results showed that 12 features selected using the Max-Relevance Min-Redundancy selection strategy were sufficient to explain the two classes with 94.97% classification rate higher than 92.74% state-of-the-art rate. We also showed this selection strategy helped the system to be robust to the testing phase when using automatic segmentation rather than manual segmentation. This work demonstrates that local systolic segment features are the most relevant for murmur/normal classification, regardless of segmentation methods. It also shows that feature selection algorithms have potential to highlight certain relevant regions in signals, which is useful for aided diagnostic systems and basic research.