Key result
A novel phonocardiogram segmentation methodology using nonlinear dynamic analysis and high-frequency decomposition achieved 100% inter-beat and 97.7% intra-beat segmentation accuracy.
Why the study?
Does the proposed methodology using nonlinear dynamic analysis and high-frequency decomposition accurately segment phonocardiographic signals?
Does the proposed methodology using nonlinear dynamic analysis and high-frequency decomposition accurately segment phonocardiographic signals?
The proposed methodology using nonlinear dynamic analysis and high-frequency decomposition achieves high accuracy in segmenting phonocardiographic signals, even in the presence of cardiac murmurs.
May enhance phonocardiogram analysis amid murmurs; leaves open prospective clinical validation before diagnostic adoption.
In this study, an effective methodology for segmenting the temporal trace of phonocardiographic signals (PCG) is presented. Initially, inter-beat segmentation is carried out using the DII lead of the ECG recording for locating the occurrence of the first heart sound (S1). Next, the intra-beat segmentation is achieved by using recurrence time statistics (RTS), which is sensitive to changes of the reconstructed attractor in a state space derived from nonlinear dynamic analysis. If the segmentation using RTS fails, an alternative segmentation is proposed using thresholding over the Shannon envelogram extracted from the high-frequency decomposition. The database of PCG records which was used belongs to the National University of Colombia. Inter-beat segmentation accuracy was 100% over all PCG recordings. Taking into account 360 PCG beats, where a set of 180 beats were strongly disturbed by different types of cardiac murmurs, intra-beat segmentation yielded an accuracy result of 97.7%.
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Quiceno et al. (2008) studied Cardiac murmurs (n=360). Phonocardiogram segmentation using nonlinear dynamic analysis and high-frequency decomposition was evaluated on Intra-beat segmentation accuracy. A novel phonocardiogram segmentation methodology using nonlinear dynamic analysis and high-frequency decomposition achieved 100% inter-beat and 97.7% intra-beat segmentation accuracy.
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