Key result
Prony-based ECG feature extraction identifies five cardiac arrhythmia classes from MIT-BIH recordings.
Why the study?
A novel ECG feature extraction method based on complex resonance frequencies was developed to improve identification of different cardiac arrhythmias.
Prony-based ECG feature extraction for arrhythmias is feasible; leaves open clinical validation before any diagnostic use.
An electrocardiogram (ECG) feature extraction system based on the calculation of the complex resonance frequency employing Prony-s method is developed. Prony-s method is applied on five different classes of ECG signals- arrhythmia as a finite sum of exponentials depending on the signal-s poles and the resonant complex frequencies. Those poles and resonance frequencies of the ECG signals- arrhythmia are evaluated for a large number of each arrhythmia. The ECG signals of lead II (ML II) were taken from MIT-BIH database for five different types. These are the ventricular couplet (VC), ventricular tachycardia (VT), ventricular bigeminy (VB), and ventricular fibrillation (VF) and the normal (NR). This novel method can be extended to any number of arrhythmias. Different classification techniques were tried using neural networks (NN), K nearest neighbor (KNN), linear discriminant analysis (LDA) and multi-class support vector machine (MC-SVM).
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Bani-Hasan et al. (2010) studied Cardiac arrhythmias. Prony's method for complex resonance frequency feature extraction was evaluated. An ECG feature extraction system using Prony's method to calculate complex resonance frequencies was developed to identify five classes of cardiac arrhythmias from the MIT-BIH database.
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