Key result
Entropy of approximation coefficients outperforms energetic ratio for discriminating cardiac severity in PCG signals.
Why the study?
The intensity or energy of PCG signals does not fully capture cardiac severity information, necessitating evaluation of other parameters for severity discrimination.
Does the entropy of approximation coefficients (EAC) from DWT analysis improve the discrimination of cardiac severity levels in PCG signals compared to energetic ratio (ER)?
Does the entropy of approximation coefficients (EAC) from DWT analysis improve the discrimination of cardiac severity levels in PCG signals compared to energetic ratio (ER)?
Entropy of approximation coefficients from discrete wavelet transform analysis provides better discrimination of cardiac severity levels in phonocardiogram signals than traditional energetic ratio methods.
May support entropy features for phonocardiogram analysis; leaves open clinical utility for severity monitoring pending validation.
The phonocardiogram (PCG) signal is sometimes affected by added parameters that reflect the presence of a specific pathology. The intensity or the energy of the signal is one of the most reliable parameters when studying cardiac severity. Yet, in a pathological electrophysiological and audio signal, the severity information does not fully remain in the intensity or energy, but in other variables. In this paper, we will discuss the ability of a time-frequency parameter to discriminate, separate, and monitor the pathological cardiac severity levels. We studied 14 PCG signal from eight pathologies, six of them contain clicks (reduce murmurs), and eight murmur PCG signals with four different cardiac severity levels. We then calculated the entropy of approximation coefficients (EAC) from a discrete wavelet transform (DWT) analysis, to differentiate the PCG signals with clicks from those with murmurs and to assess the cardiac severity evolution. Since the entropy EAC is also related to the signal’s intensity (energy), we compared it to the energetic ratio (ER) evolution, a parameter widely used for PCG signals discrimination and classification, which revealed that the EAC provied better results for the paper' purposes.
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Imane et al. (2023) studied Cardiac pathology (n=14). Entropy of approximation coefficients (EAC) vs. Energetic ratio (ER) was evaluated on Discrimination and classification of PCG signals and cardiac severity evolution. Entropy of approximation coefficients provided better results than energetic ratio for discriminating and monitoring cardiac severity levels in phonocardiogram signals.
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