Key result
Automatic detection using KNN, MLP, and Bayes classifiers correctly diagnosed paroxysmal atrial fibrillation in >90% of cases and predicted its onset in >70% of cases.
Why the study?
Does an automatic detection system using ECG signal features and classifiers (KNN, MLP, Bayes) accurately detect and predict paroxysmal atrial fibrillation?
Does an automatic detection system using ECG signal features and classifiers (KNN, MLP, Bayes) accurately detect and predict paroxysmal atrial fibrillation?
Machine learning classifiers using ECG signal features can accurately detect paroxysmal atrial fibrillation and predict its onset.
May support automated paroxysmal AF screening; hypothesis-generating and requires prospective clinical validation.
Paroxysmal atrial fibrillation, a really life threatening disease, is the result of irregular and repeated depolarization of the atria. In this paper, an automatic detection and prediction of this critical disease is performed by the use of three groups of features extracted from different parts of ECG signals and classified by KNN, MLP and Bayes optimal classifiers. Finally, the health status of more than 90% of cases are diagnosed correctly and also it is possible to detect an ECG record far from the PAF onset from the one which is immediately before PAF onset in more than 70% cases.
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Pourbabaee et al. (2008) studied Paroxysmal atrial fibrillation. ECG signal feature classification (KNN, MLP, Bayes) was evaluated on Correct diagnosis of health status and prediction of PAF onset. Automatic detection using KNN, MLP, and Bayes classifiers correctly diagnosed paroxysmal atrial fibrillation in >90% of cases and predicted its onset in >70% of cases.
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