Key result
Feature extraction and classification using ANN, Bayes, and k-NN diagnosed about 93% of PAF patients among healthy cases and detected different ECG episodes with correct classification rates >90%.
Why the study?
Can feature extraction and classification algorithms (ANN, Bayes, k-NN) accurately diagnose Paroxysmal Atrial Fibrillation from ECG signals?
Can feature extraction and classification algorithms (ANN, Bayes, k-NN) accurately diagnose Paroxysmal Atrial Fibrillation from ECG signals?
Machine learning classifiers using statistical and morphological features from ECG signals can accurately diagnose paroxysmal atrial fibrillation and predict its onset.
ML classifiers show promise for PAF ECG diagnosis; leaves open prospective validation before clinical adoption.
Paroxysmal Atrial Fibrillation (PAF), a really life threatening disease, is the result of irregular and repeated depolarization of the atria. In this paper, patients with PAF disease and their different episodes can be detected by extracting statistical and morphological features from ECG signals and classifying them by applying artificial neural network (ANN), Bayes optimal classifier and K-nearest neighbor (k-NN) classifier. Consequently, we become successful to diagnose about 93% of PAF patients among healthy cases and also detect their ECG signal different episodes such as those far from the PAF episode and the ones which are immediately before PAF episode with the correct classification rates (CCR) of more than 90%.
No takes yet. Share an insight, caveat, or question.
Pourbabaee et al. (2010) studied Paroxysmal Atrial Fibrillation. Feature extraction and classification (ANN, Bayes optimal classifier, k-NN) was evaluated on Correct classification rate for diagnosing PAF and detecting different episodes. Feature extraction and classification using ANN, Bayes, and k-NN diagnosed about 93% of PAF patients among healthy cases and detected different ECG episodes with correct classification rates >90%.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: