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Published on: May 23, 2021
Electrocardiogram morphological arrhythmia classification using fuzzy entropy-based feature selection and optimal
Krishnakant Chaubey1, Seemanti Saha1
1Department of Electronics & Communication Engineering, National Institute of Technology Patna, Ashok Raj Path, Patna, 800005, Bihar, India.
Insights
This study introduces an efficient algorithm for classifying seven types of cardiac arrhythmias from ECG signals. The proposed method achieves high accuracy, outperforming existing techniques for reliable heart rhythm monitoring.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Cardiac arrhythmias contribute significantly to global mortality.
- Accurate detection of arrhythmias requires continuous ECG monitoring and advanced analysis.
- Computer-assisted algorithms are crucial for interpreting complex ECG data.
Purpose of the Study:
- To develop and validate a novel morphological arrhythmia classification algorithm for ECG signals.
- To identify and rank significant features for improved classification accuracy.
- To compare the performance of different machine learning classifiers for ECG beat categorization.
Main Methods:
- A novel feature set of 25 attributes was extracted from ECG beats.
- Fuzzy Entropy-based Feature Selection (FEBFS) was employed to rank and select features.
- Support Vector Machine with Radial Basis Function (SVM-RBF) and Weighted K-Nearest Neighbor (WKNN) classifiers were utilized.
- Performance was evaluated using 10-fold cross-validation on the MIT-BIH Arrhythmia Database.
Main Results:
- The WKNN classifier, with K=3 and cityblock distance, achieved the highest accuracy.
- Achieved Average Sensitivity = 94.89%, Positive Predictivity = 97.13%, Specificity = 99.72%, F1 Score = 95.95%, and Overall Accuracy = 99.15%.
- The proposed algorithm demonstrated superior performance compared to existing state-of-the-art methods.
Conclusions:
- The developed algorithm, utilizing a unique feature set and FEBFS, is efficient and reliable for morphological arrhythmia classification.
- The findings highlight the potential of advanced signal processing and machine learning for improving cardiac arrhythmia detection.
- This work offers a robust solution for beat-by-beat ECG analysis in clinical settings.
Abstract:
Electrocardiogram (ECG) signal analysis has become significant in recent years as cardiac arrhythmia shares a major portion of all mortality worldwide. To detect these arrhythmias, computer-assisted algorithms play a pivotal role as beat-by-beat monitoring of holter ECG signals is required. In this paper, a morphological arrhythmia classification algorithm has been proposed to classify seven different ECG beats, namely Normal Beat (N), Left Bundle Branch Block Beat (L), Right Bundle Branch Block Beat (R), Atrial Premature Contraction Beat (A), Premature Ventricular Contraction Beat (V), Fusion of Normal and Ventricle Beat (F) and Pace Beat (P). A novel feature set of 25 attributes has been extracted from each ECG beat and ranked using the Fuzzy Entropy-based feature selection (FEBFS) technique. In addition, two distinct classifiers, support vector machine with radial basis function as the kernel (SVM-RBF) and weighted K-nearest neighbor (WKNN), are used to categorize ECG beats, and their performances are also evaluated after adjusting vital parameters. The performance of classifiers is compared for four different ECG beat segmentation approaches and further analyzed using three similarity measurement techniques and two fuzzy entropy methods while feature selection. The classifier results are also cross-validated using a 10-fold cross-validation scheme, and the MIT-BIH Arrhythmia Database has been used to validate the proposed work. After selecting 21 highly ranked features, WKNN achieves the best results with the nearest neighbor value K = 3 and cityblock distance metrics, with Average Sensitivity (Sen) = 94.89%, Positive Predictivity (Ppre) = 97.13%, Specificity (Spe) = 99.72%, F1 Score = 95.95%, and Overall Accuracy (Acc) = 99.15%. The novelty of this work relies on formulating a unique feature set, including proposed symbolic features, followed by the FEBFS technique making this algorithm efficient and reliable for morphological arrhythmia classification. The above results demonstrate that the proposed algorithm performs better than many existing state-of-the-art works.
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