Key result
A random forest classifier utilizing 25 features from 1-minute single-lead ECG segments detected paroxysmal atrial fibrillation with 91.94% accuracy, outperforming a decision tree classifier.
Why the study?
Paroxysmal atrial fibrillation often progresses stealthily to the chronic stage due to the absence of noticeable symptoms, making timely identification necessary.
Does a machine learning algorithm using 1-minute single-lead ECG segments accurately detect paroxysmal atrial fibrillation?
Does a machine learning algorithm using 1-minute single-lead ECG segments accurately detect paroxysmal atrial fibrillation?
A random forest machine learning algorithm using 1-minute single-lead ECG segments can accurately detect paroxysmal atrial fibrillation with over 91% accuracy.
May aid brief ECG screening for paroxysmal AF; leaves open prospective validation before clinical adoption.
Paroxysmal atrial fibrillation ( PAF) i s t he initial phase of atrial fibrillation ( AF), often progressing stealthily to the chronic stage due to the absence of noticeable symptoms. Hence, timely identification of PAF is pretty necessary. This study proposes an automated machine learning-based PAF detection algorithm utilizing a single-lead electrocardiogram signal. A total of 25 features are extracted from 1-minute segments and the optimal feature set, selected by deploying the minimum redundancy maximum relevance algorithm, is used to train decision tree (DT) and random forest (RF) classifiers. The training and testing stages included 43 subjects, and subjectwise 10-fold cross-validation was performed. RF outperforms the DT classifier a chieving 91.94% accuracy, 91.75% sensitivity, and 91.47% F1 score. The higher accuracy using shorter ECG segments remarks the significance of the proposed model for AF monitoring.
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Sakib et al. (2023) studied Paroxysmal atrial fibrillation (n=43). Random forest (RF) classifier vs. Decision tree (DT) classifier was evaluated on Accuracy of PAF detection. A random forest classifier utilizing 25 features from 1-minute single-lead ECG segments detected paroxysmal atrial fibrillation with 91.94% accuracy, outperforming a decision tree classifier.
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