Key result
Novel S-transform signal processing effectively segments heart sounds and detects systolic murmurs.
Why the study?
Cardiac auscultation is a noninvasive, low-cost, and accurate method to diagnose some heart diseases, but improved signal processing tools for heart sound analysis are needed.
Answers are grounded in this paper and cite their sources.
This paper present several signal processing tools for the analysis of heart sounds. Cardiac auscultation is noninvasive, low-cost and accurate to diagnose some heart diseases. A new module for the segmentation of heart sounds based on S-Transform is presented. The heart sound segmentation process divides the Phono Cardio Gram (PCG) signal into four parts: S1 (first heart sound), systole, S2 (second heart sound) and diastole. The segmentation can be considered one of the most important phases in the auto-analysis of PCG signals. A segmentation method based on the Shannon energy of the local spectrum calculated by the S-transform is proposed. Then, the energy concentration of the S-transform is optimized to accurately detect the boundaries of the localized sounds. New features based on the energy concentration of the S-transform are Original Research Article proposed to classify S1 and S2 and other features based on the complexity measu Frequency (TF) domain are proposed to detect systolic murmurs.
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Moukadem et al. (2014) studied Heart diseases / Systolic murmurs. Signal processing tools based on S-Transform was evaluated on Heart sound segmentation (S1, systole, S2, diastole) and systolic murmur detection. A novel signal processing module based on the S-Transform and Shannon energy of the local spectrum was proposed to effectively segment heart sounds and detect systolic murmurs.
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