A novel deep learning approach combining ECG and PCG signals demonstrates high accuracy and computational efficiency for heart disease classification.
Heart Disease (HD) is a leading cause of mortality worldwide. HD causes more number of deaths per year. Hence, the early detection of HD is needed to increase the survival rate. Many existing research works are presented for the detection of HD. However, existing approaches for HD diagnosis suffered from low accuracy and external noise, and most relied on either Electrocardiogram (ECG) or Phonocardiogram (PCG) signals. Different outputs might sometimes be obtained from each signal, creating misclassified outcomes. Hence, this study proposes a new HD classification accuracy prediction approach using the Polynomial Jacobian Matrix-based Deep Jordan Recurrent Neural Network (PJM-DJRNN). The proposed method involves noise removal from ECG and PCG signals separately using the Brownian Functional-based BesseL Filter (BrF-BLF) and Frequency Ratio-based Butterworth Filter (FR-BWF), decomposition of the signals using Hamming-based Ensemble Empirical Mode Decomposition (HEEMD), and clustering of the signals as normal and abnormal using Root Farthest First Clustering (RFFC). Then, the rule is generated for the obtained clustering outcome. Then, from the abnormal signal, the features are extracted. Then, the important features are selected using Poisson Distribution Function - Snow Leopard Optimization (PDF-SLO), and the PJM-DJRNN is used to classify the types of disease. The proposed method is more effective than existing research methodologies as it uses both ECG and PCG signals, achieves better input signals, and accurately predicts HD classification. The proposed model's classification efficiency has been authenticated through experimental analysis, which yielded an accuracy of 97.33%. • The final method demonstrated strong performance with a 98% specificity and 97% sensitivity, aligning with the best methods reported in the literature. • The combined BrF-BLF and FR-BWF filters to remove noise from ECG and PCG signals, improving input quality for the PJM-DJRNN model. • The HEEMD for signal decomposition, RFFC for clustering, and PDF-SLO for feature extraction, creating a robust feature set for classification. • The PJM-DJRNN achieved 18,016 ms computation time, outperforming DJRNN by 9,998 ms, DNN by 39,990 ms, and ENN by 59,988 ms. • These results confirm that PJM-DJRNN outperforms existing methods in terms of computation time.
Chandrasekhar et al. (Sat,) studied this question.