Heart rate study using the cardiac sounds

The purpose of this paper is to present a straightforward framework for Heart Rate (HR) estimation from a Phonocardiogram (PCG) record and study the association between murmur severity levels and HR. The system focuses primarily on data processing procedure, which is based on signal preprocessing using Maximal Overlap Discrete Wavelet Transform (MODWT) to delineate murmurs from heart sounds. We exploit the characteristics of Logistic function to derive an enhanced PCG envelope that serves as prerequisite for HR algorithm detection. In fact, the PCG envelope presents a cyclostationarity that can be easily detected throughout a cross-covariance autocorrelation function to calculate the Heart Rate (HR). In addition, the effect of minor and pronounced murmurs is gauged by the Energetic Ratio (ER) that provides a comprehensive idea about the superimposed murmur energy on first and second Heart sounds. The study was conducted on PASCAL dataset with 335 real clinical records. Results shows that mild murmurs with low ER are not associated with HR (p-value = 0.846). However, moderate-severe murmurs indicate a significant association with HR (p-value = 0.0002). Furthermore, HR decreases as Energetic Ratio increases. These findings could be valuable to medical professionals operating in the emergency departments.