Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN
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...
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025001203 |
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| author | Nadikatla Chandrasekhar Sujatha Canavoy Narahari Sreedhar Kollem Samineni Peddakrishna Archana Penchala Babji Prasad Chapa |
| author_facet | Nadikatla Chandrasekhar Sujatha Canavoy Narahari Sreedhar Kollem Samineni Peddakrishna Archana Penchala Babji Prasad Chapa |
| author_sort | Nadikatla Chandrasekhar |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-ce014542c5fd42799ebf8adacebff0f8 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-ce014542c5fd42799ebf8adacebff0f82025-01-19T06:26:35ZengElsevierResults in Engineering2590-12302025-03-0125104032Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNNNadikatla Chandrasekhar0Sujatha Canavoy Narahari1Sreedhar Kollem2Samineni Peddakrishna3Archana Penchala4Babji Prasad Chapa5Department of Electronics and Communication Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, 532127, IndiaDepartment of ECE, Sreenidhi Institute of Science and Technology, Ghatkesar, Hyderabad, Telangana, 501301, IndiaDepartment of ECE, School of Engineering, SR University, Warangal, Telangana, 506371, IndiaSchool of Electronics Engineering, VIT-AP University, Amaravati, Andhra Pradesh, 522241, India; Corresponding author.Department of ECE, Sreenidhi Institute of Science and Technology, Ghatkesar, Hyderabad, Telangana, 501301, IndiaDepartment of Electronics and Communication Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, 532127, IndiaHeart 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%.http://www.sciencedirect.com/science/article/pii/S2590123025001203Heart diseaseDeep learningRule generationECGPCGJordan recurrent neural network |
| spellingShingle | Nadikatla Chandrasekhar Sujatha Canavoy Narahari Sreedhar Kollem Samineni Peddakrishna Archana Penchala Babji Prasad Chapa Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN Results in Engineering Heart disease Deep learning Rule generation ECG PCG Jordan recurrent neural network |
| title | Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN |
| title_full | Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN |
| title_fullStr | Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN |
| title_full_unstemmed | Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN |
| title_short | Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN |
| title_sort | heart abnormality classification using ecg and pcg recordings with novel pjm djrnn |
| topic | Heart disease Deep learning Rule generation ECG PCG Jordan recurrent neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025001203 |
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