  Cardiac auscultation is an essential point-of-care method used for the early
diagnosis of heart diseases. Automatic analysis of heart sounds for abnormality
detection is faced with the challenges of additive noise and sensor-dependent
degradation. This paper aims to develop methods to address the cardiac
abnormality detection problem when both types of distortions are present in the
cardiac auscultation sound. We first mathematically analyze the effect of
additive and convolutional noise on short-term filterbank-based features and a
Convolutional Neural Network (CNN) layer. Based on the analysis, we propose a
combination of linear and logarithmic spectrogram-image features. These 2D
features are provided as input to a residual CNN network (ResNet) for heart
sound abnormality detection. Experimental validation is performed on an
open-access heart sound abnormality detection dataset involving noisy
recordings obtained from multiple stethoscope sensors. The proposed method
achieves significantly improved results compared to the conventional
approaches, with an area under the ROC (receiver operating characteristics)
curve (AUC) of 91.36%, F-1 score of 84.09%, and Macc (mean of sensitivity and
specificity) of 85.08%. We also show that the proposed method shows the best
mean accuracy across different source domains including stethoscope and noise
variability, demonstrating its effectiveness in different recording conditions.
The proposed combination of linear and logarithmic features along with the
ResNet classifier effectively minimizes the impact of background noise and
sensor variability for classifying phonocardiogram (PCG) signals. The proposed
method paves the way towards developing computer-aided cardiac auscultation
systems in noisy environments using low-cost stethoscopes.
