Short-term atrial fibrillation detection using electrocardiograms: A comparison of machine learning approaches.

BACKGROUND

Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias, which challenges the healthcare systems globally.Timely detection of AF can potentially reduce the mortality and morbidity rates as well as alleviate the economic burden caused by this.Digital solutions are shown to enhance the diagnosis of cardiac abnormalities.

OBJECTIVES

By the latest advancements in the field of medical informatics and tele-health monitoring, huge amount of electro-physiological signals, such as electrocardiograms (ECG), can be easily collected.One of the most common ways for physicians/cardiologists to analyse these signals is through visual inspection.However, it is not always easy and in most cases cumbersome to analyse these big amounts of ECG data.Therefore, it is of great interest to develop models that are capable of analyzing these data and help physicians making better decisions.This paper proposes and compares well-known machine learning (ML) algorithms to diagnose short episodes of AF. This also paves the way for real-time detection of AF in clinical settings.

METHODS

Different ML algorithms such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Stacking Classifier (SC), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) were applied to detect AF. These models were trained using extracted statistical features from ECG signals.

RESULTS

The proposed ML models were trained on a dataset with 23 ECG records of length approximately 10 h each using leave one group out cross validation (LOGO-CV) technique and achieved the best sensitivity (Se), specificity (Sp), positive predictive value (PPV), false positive rate (FPR), and F-score of 85.67%, 81.25%, 90.85%, 18.75% and 88.18%, respectively, to classify AF from normal sinus rhythms (NSR) in short ECG segments of 20 heartbeats.Additionally, the models were examined on three unseen datasets, namely the Long Term AF dataset, MIT-BIH Arrhythmia dataset, and MIT-BIH Normal Sinus Rhythm dataset, to assess their robustness and generalization.

CONCLUSION

The obtained results show high performance and flexibility of some of the applied ML models compared to other well-known algorithms. In general, the empirical results confirm that ensemble methods, such as AdaBoost, generalized well and perform better than other approaches.