Heart disease diagnosis and categorization from ECG signals using hybrid Fuzzy-CNN machine optimized by meta-heuristic algorithms
AI Analysis: Heart disease diagnosis and categorization from ECG signals using hybrid Fuzzy-CNN machine optimized by meta-heuristic algorithms
This study presents an optimized hybrid Fuzzy-CNN model using meta-heuristic algorithms for classifying 2D ECG images. It achieves 99.71% accuracy, significantly improving detection of critical arrhythmias like VEB and S classes, outperforming classical and advanced models.
Executive Impact
Cardiovascular diseases (CVDs) remain the leading cause of global mortality, with accurate and timely diagnosis being paramount. Traditional machine learning methods often struggle with the complexity and nonlinearity of ECG signals. This research introduces a novel, optimized hybrid Fuzzy-CNN model that leverages meta-heuristic algorithms to enhance diagnostic precision and clinical utility.
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Precision
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Sensitivity (V)
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Sensitivity (S)
Deep Analysis & Enterprise Applications
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99.71%
Overall Accuracy
The proposed Fuzzy-CNN model achieves an outstanding overall accuracy of 99.71% on the MIT-BIH dataset for 7-class arrhythmia classification, outperforming standard CNN, SVM, and LSTM, as well as recent advanced models.
Enterprise Process Flow
Preprocessing ECG Signals
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WHO Algorithm for Optimal Period (T) Selection
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Circular Coordinate Transformation
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Generated 2D Image for CNN Input
| Metric | Standard CNN (%) | Fuzzy-CNN (%) | Improvement (%) |
|---|---|---|---|
| AVG-ACC | 93.95 | 99.71 | 6.45 |
| AVG-Sn | 87.03 | 98.61 | 11.58 |
| AVG-Sp | 96.28 | 99.56 | 3.2 |
| AVG-Pr | 89.22 | 98.68 | 9.46 |
| AVG-F1 | 87.85 | 98.65 | 10.8 |
98.95%
Sensitivity for Ventricular Ectopic Beats (V)
The model demonstrates a high sensitivity of 98.95% for ventricular (V) ectopic beats and 96.67% for supraventricular (S) ectopic beats, both critical classes associated with sudden cardiac death, significantly improving detection compared to previous studies.
Real-time Arrhythmia Monitoring
Challenge: Traditional ECG analysis and existing deep learning models often lack the robustness, interpretability, and low inference time required for real-time monitoring in wearable devices and telemedicine platforms, especially when dealing with data variability and rare, critical arrhythmia types.
Solution: The Fuzzy-CNN model integrates WHO-optimized 2D image transformation for patient-adaptive visual features, joint temporal-visual feature fusion, and POA-based global optimization. This hybrid approach ensures high accuracy and sensitivity for critical classes, combined with lightweight linear fuzzy operations for fast inference.
Outcome: With an inference time of only 9.473 ms for 576 signals and memory consumption of ~385 MB (reducible by 50%), the model is highly suitable for deployment on edge devices like Raspberry Pi or smartphones, enabling continuous, reliable, and interpretable real-time detection of life-threatening arrhythmias in clinical settings.
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Data Preprocessing & Adaptive Image Conversion
Implement noise removal (median filter), normalization, and resampling of ECG signals from MIT-BIH database. Utilize the WHO algorithm to adaptively determine optimal segmentation period (T) and convert ECG signals into 2D images using circular coordinate transformation, preserving morphological details and reducing noise effects.
Fuzzy-CNN Hybrid Model Development
Construct a 5-layer CNN for feature extraction from 2D images, integrating temporal-spectral features. Replace the final fully-connected layer with a Takagi-Sugeno fuzzy system. Employ the Puma Optimization Algorithm (POA) to simultaneously tune all CNN filter coefficients and fuzzy system parameters, ensuring global convergence and robustness against local minima.
Robust Training & Comprehensive Validation
Train the POA-optimized Fuzzy-CNN model on the MIT-BIH dataset using k-fold cross-validation and early stopping to prevent overfitting. Evaluate performance across seven AAMI EC57 standard arrhythmia classes (N, S, V, F, Q, LBBB, RBBB) using accuracy, precision, recall, F1-score, and ROC curves, with a focus on critical classes V and S.
Clinical Integration & Real-World Deployment
Integrate the validated model into a robust framework for real-time ECG signal classification. Focus on optimizing for inference speed and memory consumption for deployment on wearable devices and telemedicine platforms, ensuring clinical reliability and interpretability for early detection of life-threatening arrhythmias.
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