Key result
The proposed ECG feature extraction scheme using 5-s ECG segments and tenfold cross validation achieved superior performance in discriminating ventricular arrhythmias versus existing methods.
Why the study?
Automated analysis of ECG signals is crucial for the early recognition of life-threatening arrhythmias.
Does the proposed ECG feature description model improve the discrimination of life-threatening arrhythmias compared to existing methods?
Does the proposed ECG feature description model improve the discrimination of life-threatening arrhythmias compared to existing methods?
A novel ECG feature extraction scheme using SVD, harmonic phase distribution, and DTW demonstrates superior performance in discriminating life-threatening ventricular arrhythmias.
Should not yet change clinical arrhythmia monitoring; leaves open prospective validation of the approach.
The automated analysis of electrocardiogram (ECG) signal is crucial for early recognition of life-threatening arrhythmias. This work presents an efficient ECG feature description model for diagnosis of ventricular arrhythmias (VAs); ventricular fibrillation (VF) and ventricular tachycardia (VT). The concept of a new feature set by applying singular value decomposition (SVD) and harmonic phase distribution is introduced to capture the morphological variation of the ECG signal. The phase characterization and singular value features are extracted from discrete Fourier transform and Empirical mode decomposition of ECG signal, respectively. In addition to that, dynamic time warping (DTW) procedure is applied to measure the similarity/dissimilarity pattern of ECG signals for classifying VAs. The firstfold classification involves the discrimination of VAs and non-VA patterns. Following this, secondfold classification discriminates VF versus VT and normal rhythms from other arrhythmic conditions. Experimental results are validated using the benchmark Physionet ECG database. The proposed method achieved the best performances using 5-s ECG segment with tenfold cross validation strategy. A comparative study with existing methods also shows superior performances of the proposed feature extraction scheme.
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Sinha et al. (2020) studied Ventricular arrhythmias (ventricular fibrillation and ventricular tachycardia). ECG feature description model (SVD, harmonic phase distribution, and DTW) vs. Existing methods was evaluated on Discrimination of ventricular arrhythmias and non-VA patterns, including VF versus VT. The proposed ECG feature extraction scheme using 5-s ECG segments and tenfold cross validation achieved superior performance in discriminating ventricular arrhythmias versus existing methods.
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