Towards heart sound classification without segmentation via autocorrelation feature and diffusion maps

• A novel framework for heart sound classification without segmentation.
• Extracting the autocorrelation features of the normalized average Shannon energy envelopes at different wavelet sub-bands.
• Fusing the autocorrelation features into the uniform features by using diffusion maps and classifying them with the SVM classifier.
• Evaluating the proposed method on two public datasets published in the PASCAL Classifying Heart Sounds Challenge.

Heart sound classification, used for the automatic heart sound auscultation and cardiac monitoring, plays an important role in primary health center and home care. However, one of the most difficult problems for the task of heart sound classification is the heart sound segmentation, especially for classifying a wide range of heart sounds accompanied with murmurs and other artificial noise in the real world. In this study, we present a novel framework for heart sound classification without segmentation based on the autocorrelation feature and diffusion maps, which can provide a primary diagnosis in the primary health center and home care. In the proposed framework, the autocorrelation features are first extracted from the sub-band envelopes calculated from the sub-band coefficients of the heart signal with the discrete wavelet decomposition (DWT). Then, the autocorrelation features are fused to obtain the unified feature representation with diffusion maps. Finally, the unified feature is input into the Support Vector Machines (SVM) classifier to perform the task of heart sound classification. Moreover, the proposed framework is evaluated on two public datasets published in the PASCAL Classifying Heart Sounds Challenge. The experimental results show outstanding performance of the proposed method, compared with the baselines.