A robust heart sound segmentation algorithm for commonly occurring heart valve diseases.

The first step towards detection of valvular heart diseases from heart sound signal (phonocardiogram) is segmentation. A segmentation algorithm provides the location of the first and second heart sounds which in turn helps to locate and analyse the murmur. Established phonocardiogram based segmentation methods use an electrocardiographic (ECG) signal as a continuous auxiliary input in a complex instrumentation setup. This paper proposes an automatic segmentation method that does not require any such auxiliary signal. Compared to other approaches without auxiliary signal, this work extensively utilizes biomedical domain features for reduction of time and computational complexities and is more accurate. The performance of the algorithm is evaluated for nine commonly occurring pathological cases and normal heart sound for various sampling frequencies, recording environments and age group of subjects. The proposed algorithm yields an overall accuracy of 97.47% and is compared with two competing techniques. In addition, the robustness of the algorithm is shown against additive white Gaussian noise contamination at various SNR levels.