Identifying patients with paroxysmal atrial fibrillation from sinus rhythm ECG using random forests. (1st March 2023)

• Record Type: Journal Article
• Authors: Myrovali, Evangelia; Hristu-Varsakelis, Dimitrios; Tachmatzidis, Dimitrios; Antoniadis, Antonios; Vassilikos, Vassilios
• Is Part Of: Expert systems with applications. Volume 213:Part A(2023)
• Publication Date: 2023-03-01
• Subjects: Machine learning -- PAF -- Sinus rhythm -- P wave -- Random forest -- Classification; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118948
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia which is often challenging to diagnose because patients may be asymptomatic, and episodes are usually intermittent. In this paper we describe a method for identifying patients with a history of PAF using electrocardiogram (ECG) recordings during sinus rhythm. We analyzed, on a beat-to-beat basis, the P-waves in the sinus rhythm ECGs of 69 patients with a history of PAF and 59 healthy individuals. From each subject's P-waves, we calculated key electrocardiographic metrics, including some which are proposed here for the first time. Using means testing and feature selection methods, we discerned the features which were most useful for classification, and trained a Random Forest which identified PAF patients. Our approach achieved a 93.45% accuracy, sensitivity of 95.21%, and specificity of 91.40% using P-wave integral and novel amplitude and slope-based features which ranked highest in importance compared to other metrics from the literature. In particular, descriptive statistics of P-wave amplitudes, slopes, and integrals, were effective for identifying subjects with PAF history vs. healthy individuals. Our method has a high sensitivity and discrimination ability, with an AUC=0.9669 which is superior to others', and can thus be potentially valuable for the early identification of patients who are prone to episodes of PAF, even as part of the standard cardiological checkup that most adults undergo periodically. Highlights: Early identification of PAF during sinus rhythm with high accuracy. P-wave signal metrics on a beat-to-beat basis are proposed. P-wave amplitude, integral and slope features were ranked as the most important. A RF classifier distinguishes PAF from healthy subjects with a state-of-the-art AUC.