Cardiovascular diseases(CVDs)are the leading cause of mortality worldwide,necessitating efficient diagnostic tools.This study develops and validates a deep learning framework for phonocardiogram(PCG)classification,foc...Cardiovascular diseases(CVDs)are the leading cause of mortality worldwide,necessitating efficient diagnostic tools.This study develops and validates a deep learning framework for phonocardiogram(PCG)classification,focusing on model generalizability and robustness.Initially,a ResNet-18 model was trained on the PhysioNet 2016 dataset,achieving high accuracy.To assess real-world viability,we conducted extensive external validation on the HLS-CMDS dataset.We performed four key experiments:(1)Fine-tuning the PhysioNet-trained model for binary(Normal/Abnormal)classification on HLS-CMDS,achieving 88%accuracy.(2)Fine-tuning the same model for multiclass classification(Normal,Murmur,Extra Sound,Rhythm Disorder),which yielded 86%accuracy.(3)Retraining a ResNet-18 model with ImageNet weights directly on the HLS-CMDS data,which improved multi-class accuracy to 89%,demonstrating the benefit of domain-specific feature learning on the target dataset.(4)A novel stress test evaluating the retrained model on computationally separated heart sounds from mixed heart-lung recordings,which revealed a significant performance drop to 41%accuracy.This highlights the model’s sensitivity to signal processing artifacts.Our findings underscore the importance of external validation and demonstrate that while deep learning models can generalize across datasets,their performance is heavily influenced by training strategy and their robustness to preprocessing artifacts remains a critical challenge for clinical deployment.展开更多
基金Deanship of Graduate Studies and Scientific Research at Qassim University through grant number(QU-APC-2025)。
文摘Cardiovascular diseases(CVDs)are the leading cause of mortality worldwide,necessitating efficient diagnostic tools.This study develops and validates a deep learning framework for phonocardiogram(PCG)classification,focusing on model generalizability and robustness.Initially,a ResNet-18 model was trained on the PhysioNet 2016 dataset,achieving high accuracy.To assess real-world viability,we conducted extensive external validation on the HLS-CMDS dataset.We performed four key experiments:(1)Fine-tuning the PhysioNet-trained model for binary(Normal/Abnormal)classification on HLS-CMDS,achieving 88%accuracy.(2)Fine-tuning the same model for multiclass classification(Normal,Murmur,Extra Sound,Rhythm Disorder),which yielded 86%accuracy.(3)Retraining a ResNet-18 model with ImageNet weights directly on the HLS-CMDS data,which improved multi-class accuracy to 89%,demonstrating the benefit of domain-specific feature learning on the target dataset.(4)A novel stress test evaluating the retrained model on computationally separated heart sounds from mixed heart-lung recordings,which revealed a significant performance drop to 41%accuracy.This highlights the model’s sensitivity to signal processing artifacts.Our findings underscore the importance of external validation and demonstrate that while deep learning models can generalize across datasets,their performance is heavily influenced by training strategy and their robustness to preprocessing artifacts remains a critical challenge for clinical deployment.
