An empirical comparison of deep CNN architectures for heart sound classification from phonocardiogram signals

Heart disease continues to be the world's number one killer, making low-cost, early screening a priority. A phonocardiogram (PCG), a simple recording of sounds made by the heart, provides valuable clues to conditions such as murmurs and valve defects and can be recorded cheaply using a digital stethoscope. With less accessibility to specialists, the automatic analysis of heart sounds is preferred for screening and tele-health environments. We compare five deep convolutional neural networks in this work, namely LeNet5, AlexNet, VGG16, ResNet-50 and a hybrid LSTM-CNN, on the task of separating normal from abnormal heart sounds. Before entering the network, each recording is converted to a log-Mel spectrogram, and each model is trained at five different learning rates (1e-2, 1e-3, 5e-4, 1e-4 and 5e-5). The PhysioNet/CinC Challenge 2016 database is common to all experiments and, after duplicate removal, provides 3, 240 recordings. A leakage-free protocol is enforced throughout: exact duplicates are detected by audio content hashing and the split is patient-independent, which on this dataset is sufficient to prevent several networks from reaching a misleading 100% accuracy. We report accuracy, AUC, sensitivity, specificity, F1 score and the Matthews correlation coefficient. Under five-fold cross-validation VGG16 attains the highest mean accuracy (91.94% ± 1.59, AUC 0.968, MCC 0.745), marginally ahead of ResNet-50 (90.93% ± 1.09); McNemar's test confirms that the difference between the leading architectures is not statistically significant (p = 0.31), so the two are reported as jointly strongest. An ablation study shows that spectrogram augmentation, rather than ImageNet pre-training, is the decisive design choice, and every model fails at a learning rate of 1e-2. External validation on the CirCor DigiScope 2022 database shows that zero-shot cross-dataset transfer performs at chance level, while the retrained pipeline exceeds the majority baseline, underlining how strongly single-dataset results can overstate real-world generalisation.