Key result
Novel heart sound segmentation algorithm achieves ~96% accuracy using only average heart rate.
Why the study?
Efficient heart sound segmentation algorithms are needed to accurately identify the first and second heart sounds for detecting heart diseases without requiring complex ECG setups.
A novel heart sound segmentation algorithm using only average heart rate achieved 95.51% accuracy, offering a simpler alternative to ECG-dependent methods.
May simplify heart sound segmentation without ECG input; leaves open clinical validation before diagnostic use.
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.
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Ari et al. (2007) studied Heart diseases (n=34). Heart sound segmentation algorithm was evaluated on Overall accuracy of heart sound segmentation. A novel heart sound segmentation algorithm requiring only average heart rate achieved an overall accuracy of 95.51% across 34 pathological cases and normal heart sounds.
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