A hybrid CNN and LSTM deep learning system using phonocardiogram signals achieved 99.87% accuracy and 0.9985 AUC for classifying five cardiac valvular conditions with an augmented dataset.
Does a hybrid CNN-LSTM deep learning system improve the classification of cardiac valvular diseases from phonocardiogram signals compared to previous models?
A hybrid CNN-LSTM deep learning model demonstrated high accuracy (>98%) in classifying five cardiac valvular conditions using phonocardiogram signals, outperforming previous models.
Cardiovascular diseases (CVDs) are a prominent cause of death globally. The introduction of medical big data and Artificial Intelligence (AI) technology encouraged the effort to develop and deploy deep learning models for distinguishing heart sound abnormalities. These systems employ phonocardiogram (PCG) signals because of their lack of sophistication and cost-effectiveness. Automated and early diagnosis of cardiovascular diseases (CVDs) helps alleviate deadly complications. In this research, a cardiac diagnostic system that combined CNN and LSTM components was developed, it uses phonocardiogram (PCG) signals, and utilizes either augmented or non-augmented datasets. The proposed model discriminates five heart valvular conditions, namely normal, Aortic Stenosis (AS), Mitral Regurgitation (MR), Mitral Stenosis (MS), and Mitral Valve Prolapse (MVP). The findings demonstrate that the suggested end-to-end architecture yields outstanding performance concerning all important evaluation metrics. For the five classes problem using the open heart sound dataset, accuracy was 98.5%, F1-score was 98.501%, and Area Under the Curve (AUC) was 0.9978 for the non-augmented dataset and accuracy was 99.87%, F1-score was 99.87%, and AUC was 0.9985 for the augmented dataset. Model performance was further evaluated using the PhysioNet/Computing in Cardiology 2016 challenge dataset, for the two classes problem, accuracy was 93.76%, F1-score was 85.59%, and AUC was 0.9505. The achieved results show that the proposed system outperforms all previous works that use the same audio signal databases. In the future, the findings will help build a multimodal structure that uses both PCG and ECG signals.
Al-Issa et al. (Mon,) conducted a other in Cardiac valvular disease. Hybrid CNN and LSTM deep learning system was evaluated on Classification accuracy, F1-score, and AUC for heart valvular conditions. A hybrid CNN and LSTM deep learning system using phonocardiogram signals achieved 99.87% accuracy and 0.9985 AUC for classifying five cardiac valvular conditions with an augmented dataset.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: