Key result
A hybrid algorithm using EMD, Hilbert transform, and Gaussian function achieved an overall positive predictivity of 98.98% and 98.78% for normal S1 and S2, and 98.78% and 98.37% for abnormal S1 and S2.
Why the study?
Phonocardiography use has been restricted by interpretation challenges and noise vulnerability, prompting efforts to independently detect heart sounds without matching to an electrocardiogram.
The proposed hybrid algorithm demonstrates high positive predictivity for segmenting normal and abnormal S1 and S2 heart sounds from phonocardiograms.
Should not yet change auscultation practice; hypothesis-generating for automated S1/S2 detection.
For many years, heart function has been measured by the electrocardiogram (ECG) signal, while sounds produced in the heart can also contain information indicating normal or abnormal heart function. What has caused to restrict the use of the phonocardiography (PCG) signal was the lack of mastery of experts in the interpretation of these sounds, as well as its high potential for noise pollution. PCG is a non-invasive signal for monitoring physiological parameters of cardiac, which can make heart disease diagnostics more efficient. In recent years, attempts have been made to use PCG to detect heart disease independently without a need to match with the ECG. We propose a hybrid algorithm including empirical mode decomposition (EMD), Hilbert transform and Gaussian function for detecting heart sounds to distinguish first (S1) and second (S2) cardiac sounds by eliminating the effect of cardiac murmurs. In this article, 250 normal and 250 abnormal sound signals were examined. The overall positive predictivity of normal and abnormal S1 and S2 is 98.98%, 98.78, 98.78 and 98.37, respectively. Our results showed that the proposed method has a high potential for heart sounds determination, while maintains its simplicity and has a reasonable computational time.
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Soroor Behbahani (2019) studied Heart sounds (n=500). Hybrid algorithm including empirical mode decomposition (EMD), Hilbert transform and Gaussian function was evaluated on Overall positive predictivity of normal and abnormal S1 and S2. A hybrid algorithm using EMD, Hilbert transform, and Gaussian function achieved an overall positive predictivity of 98.98% and 98.78% for normal S1 and S2, and 98.78% and 98.37% for abnormal S1 and S2.
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