On a robust algorithm for heart sound segmentation

The detection of heart diseases from heart sound signals needs an efficient segmentation algorithm to properly identify the location of the first and second heart sounds. This in turn helps in characterizing murmurs present in the cardiac cycles and the pathological condition by providing an appropriate time reference. The work presented here needs only the average heart rate as discrete auxiliary information that can be easily provided, unlike most of the methods which require the electrocardiography ( ECG) signal as a continuous auxiliary signal in a complex setup. The algorithm was tested on 34 pathological cases and normal heart sound for a variety of sampling frequencies, recording environments, and age groups of subjects. It was found to give an overall accuracy of 95.51%. The robustness of the algorithm against additive white Gaussian noise contamination is also presented, and the noise immunity of various diseases for correct segmentation is established through this study.