Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN - Nadikatla Chandrasekhar; Sujatha Canavoy Narahari; Sreedhar Kollem; Samineni Peddakrishna; Archana Penchala; Babji Prasad Chapa (Results in Engineering)
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%.