Recognizing the warning signs early can help prevent a heart attack or stroke.Ī healthy human heart generates a unique murmur, any irregularity is reflected in this sound and can be easily picked up using a stethoscope. The common signs of a damaged heart valve are fatigue, palpitations, shortness of breath, weakness, fainting, and chest pain 6. Another consequence is “regurgitation” illustrated by the inability of the valve to prevent blood backflow 5. The main consequence of a heart valve defect is “stenosis” described as a narrowing of the heart valve preventing blood discharge. Heart Valvular Disease (HVD) is a type of cardiovascular disease that results from the blocking, hardening, or malfunctioning of the heart valves and this can be caused by aging, dysplasia, calcific disease, inflammatory disorders, and connective tissue disorders 4. The main function of the valves is to regulate the blood flow in the circulatory system. The heart is made up of four chambers, there are four heart valves, the aortic, and the mitral on the left heart, and the pulmonary and tricuspid on the right heart. Needless to say, misdiagnosing heart irregularities can be fatal since remedying risk factors can prevent 90% of heart disorders 3.Īs a result, early and quick detection of heart problems is critical in eliminating serious complications. The problem with classical methods is that they are subjective and various physicians might have different interpretations. A lot of information about overall heart health can be obtained using conventional methods that collect different heart sounds using a stethoscope. The heart is a vital body organ, a mechanical device, and any abnormality is reflected in the heart sound and propagates through the chest wall. They are caused primarily by high blood pressure, tobacco, diabetes, lack of exercise, and obesity 1. In the future, the findings will help build a multimodal structure that uses both PCG and ECG signals.Ĭardiovascular diseases (CVD) are a leading cause of death and they claimed the lives of 18 million people in 2015 worldwide 1, 2. The achieved results show that the proposed system outperforms all previous works that use the same audio signal databases.
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.
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. The findings demonstrate that the suggested end-to-end architecture yields outstanding performance concerning all important evaluation metrics. The proposed model discriminates five heart valvular conditions, namely normal, Aortic Stenosis (AS), Mitral Regurgitation (MR), Mitral Stenosis (MS), and Mitral Valve Prolapse (MVP). 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. Automated and early diagnosis of cardiovascular diseases (CVDs) helps alleviate deadly complications. These systems employ phonocardiogram (PCG) signals because of their lack of sophistication and cost-effectiveness. 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. Cardiovascular diseases (CVDs) are a prominent cause of death globally.
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