A Deep Learning Framework for Early Detection of Potential Cardiac Anomalies via Murmur Pattern Analysis in Phonocardiograms
Year: 2025
Type: conference-paper
Abstract: Heart murmurs, resulting from turbulent blood flow within the cardiac structure, represent some of the initial acoustic manifestations of potential underlying cardiovascular anomalies, such as arrhythmias. This research presents a deep learning framework aimed at the early detection of potential cardiac anomalies through the analysis of murmur patterns in phonocardiogram (PCG) signals. Our methodology employs a spectro-temporal feature fusion technique that integrates Mel spectrograms, Mel Frequency Cepstral Coefficients (MFCCs), Root Mean Square (RMS) energy, and Power Spectral Density (PSD) representations. The features are derived from segmented 5-second phonocardiogram (PCG) windows and subsequently input into a two-dimensional convolutional neural network (CNN) for the purpose of classification. In order to mitigate class imbalance and enhance generalization, We employ data augmentation techniques, including pitch moving and noise injection. The model under consideration has undergone training and evaluation utilizing a carefully selected subset of the CirCor DigiScope dataset. The experimental findings indicate a robust performance, with a classification accuracy recorded at 92.40% and a cross-entropy loss measured at 0.2242. The results indicate that an analysis of PCG signals informed by murmurs may function as an effective non-invasive method for the early screening of conditions that may include arrhythmias, particularly in clinical environments with limited resources.
Authors: Aymane Edder, Fatima-Ezzahraa Ben-Bouazza, Oumaima Manchadi, Youssef Ait Bigane, Djeneba Sangare, Bassma Jioudi
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W7103884463