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Integrated fusion approach for multi-class heart disease classification through ECG and PCG signals with deep hybrid
Shivalila Hangaragi1, N Neelima2, Katarina Jegdic3
1Department of Electronics and Communication, Amrita School of Engineering-Bangalore, Amrita Vishwa Vidyapeetham, Bangalore, India.
Insights
This study introduces a novel method for detecting cardiovascular diseases by fusing electrocardiogram (ECG) and phonocardiogram (PCG) signals. The advanced model accurately classifies six heart conditions with 97% accuracy, improving early diagnosis.
Area of Science:
- Cardiology and Biomedical Signal Processing.
Background:
- Cardiovascular disease detection often uses single-modality signals (ECG or PCG), leading to high false positive rates.
- Reliance on ECG alone misses cardiac murmurs, while PCG alone overlooks electrical activity changes, resulting in incomplete evaluations.
Purpose of the Study:
- To develop and implement a multi-class heart disease classification model using a fusion of both ECG and PCG signals.
- To enhance the accuracy and comprehensiveness of cardiovascular disease detection and classification.
Main Methods:
- Signal preprocessing and fusion, followed by waveform detection (Pan-Tompkins Algorithm) and signal localization (Algebraic Integer-quantized Stationary Wavelet Transform).
- Clustering using Low-rank Kernelized Density-Based Spatial Clustering of Applications with noise, feature selection via Heming Wayed Polar Bear Optimization, and classification with a C squared Pool Sign BI-power-activated Deep Convolutional Neural Network.
Main Results:
- Achieved a high classification accuracy of 97% with a low error rate of 0.03.
- Successfully identified and classified six distinct heart conditions: Aortic stenosis Valvular disorder, Tricuspid Valvular disorder, Mitralstenosis Valvular disorder, Pulmonary Valvular disorder, Atrial Fibrillation, and Ischemic heart disorder.
Conclusions:
- The proposed multi-modal approach significantly improves cardiovascular disease classification accuracy.
- This integrated ECG and PCG model offers a more robust and reliable method for early diagnosis and prediction of heart conditions.
Abstract:
Detection and classification of cardiovascular diseases are crucial for early diagnosis and prediction of heart-related conditions. Existing methods rely on either electrocardiogram or phonocardiogram signals, resulting in higher false positive rates. Solely ECG misses the murmurs associated with the narrowing of the blood vessels caused by abnormalities in the heart. Similarly, considering only PCG will miss the subtle changes in the electrical activity of the heart that leads to incomplete evaluation. The implementation of a multi-class heart disease classification model utilizing both ECG and PCG signals is the objective of the proposed study. The approach involves preprocessing, fusion, waveform detection utilizing the Pan-Tompkins Algorithm, and signal localization using Algebraic Integer-quantized Stationary Wavelet Transform. Low-rank Kernelized Density-Based Spatial Clustering of Applications with noise is employed to cluster signals into normal and abnormal categories. Feature selection is performed with Heming Wayed Polar Bear Optimization, and classification is done using C squared Pool Sign BI-power-activated Deep Convolutional Neural Network. The proposed model achieves a classification accuracy of 97% with 0.03 error rate. The multi-class classifier effectively identifies and classifies the heart diseases into Aortic stenosis Valvular disorder, Tricuspid Valvular disorder, Mitralstenosis Valvular disorder, Pulmonary Valvular disorder, Atrial Fibrillation, and Ischemic heart disorder.
