Abstract
This paper proposes a robust and real-time capable algorithm for classification of the first and second heart sounds. The classification algorithm is based on the evaluation of the envelope curve of the phonocardiogram. For the evaluation, in contrast to other studies, measurements on 12 probands were conducted in different physiological conditions. Moreover, for each measurement the auscultation point, posture and physical stress were varied. The proposed envelope-based algorithm is tested with two different methods for envelope curve extraction: the Hilbert transform and the short-time Fourier transform. The performance of the classification of the first heart sounds is evaluated by using a reference electrocardiogram. Overall, by using the Hilbert transform, the algorithm has a better performance regarding the F1-score and computational effort. The proposed algorithm achieves for the S1 classification an F1-score up to 95.7% and in average 90.5%. The algorithm is robust against the age, BMI, posture, heart rate and auscultation point (except measurements on the back) of the subjects.
Highlights
Cardiovascular diseases are the leading cause of death worldwide [1,2]
The threshold parameters nnormal and nhigh were greater for the Hilbert transform (HT), since through the averaging effect of the short-time Fourier transform (STFT), its resulting envelope curve was smoother
This paper presents an enveloped-based and real-time capable algorithm for the detection and classification of the heart sounds S1 and S2 in phonocardiograms (PCG)
Summary
Cardiovascular diseases are the leading cause of death worldwide [1,2]. According to [3], this trend will continue and deteriorate in the future. The most successful approaches are the envelope-based, probabilistic-based and the feature-based methods [5]
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