Key result
An automatic configuration using support vector machines and a neural fuzzy inference network achieved 96.4% accuracy in classifying normal sinus rhythm and four arrhythmia types from ECG signals.
Why the study?
Does an automatic configuration using SVM and SoNFIN accurately identify arrhythmias from two-lead ECG signals?
Does an automatic configuration using SVM and SoNFIN accurately identify arrhythmias from two-lead ECG signals?
An automated machine learning algorithm using SVM and neural fuzzy inference networks achieved 96.4% accuracy in classifying arrhythmias from two-lead ECGs, showing promise for portable home monitoring systems.
May support portable arrhythmia monitoring development; leaves open prospective clinical validation before practice adoption.
An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.
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Liu et al. (2013) studied Arrhythmia. Automatic configuration using SVM and SoNFIN was evaluated on Classification accuracy for normal sinus rhythm and four arrhythmia types. An automatic configuration using support vector machines and a neural fuzzy inference network achieved 96.4% accuracy in classifying normal sinus rhythm and four arrhythmia types from ECG signals.
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