Key result
The Fuzzy clustering Neural Network (FCNN) classifier outperformed Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN) classifiers for ECG arrhythmia classification.
A novel fuzzy clustering neural network approach using symbolic dynamics improves automatic ECG arrhythmia classification compared to SVM and RBFNN.
This paper presents automatic ECG arrhythmia classification method using symbolic dynamics through hybrid classifier. The proposed method consists of four steps: pre-processing, data extraction, symbolic time series construction and classification. In the proposed method, initially ECG signals are pre-processed to remove noise. Further, QRS complex is extracted followed by R peak detection. From R peak value, symbolic time series representation is formed. Finally, the symbolic time series is classified using Fuzzy clustering Neural Network (FCNN). To evaluate the proposed method we conducted the experiments on MIT-BIH dataset and compared the results with Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN) classifiers. The experimental results reveal that the FCNN classifier outperforms other two classifiers.
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Roopa et al. (2018) studied ECG arrhythmia. Fuzzy clustering Neural Network (FCNN) using symbolic dynamics vs. Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN) was evaluated on ECG arrhythmia classification performance. The Fuzzy clustering Neural Network (FCNN) classifier outperformed Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN) classifiers for ECG arrhythmia classification.
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