P-wave beat-to-beat morphology analysis outperforms conventional P-wave indices in detecting patients with paroxysmal atrial fibrillation. (24th May 2021)

• Record Type: Journal Article
• Authors: Tachmatzidis, D; Filos, D; Tsarouchas, A; Mouselimis, D; Antoniadis, A; Bakogiannis, C; Chouvarda, I; Lazaridis, C; Triantafyllou, C; Fragkakis, N; Maglaveras, N; Vassilikos, V
• Is Part Of: Europace. Volume 23:Supplement 3(2021)
• Publication Date: 2021-05-24
• Subjects: Arrhythmia -- Treatment -- Periodicals; Cardiac pacing -- Periodicals; Catheter ablation -- Periodicals; Heart -- Physiology -- Periodicals; Electrophysiology -- Periodicals; 617.4120645
• DOI: 10.1093/europace/euab116.167
• Languages: English
• ISSNs: 1099-5129

Abstract

Abstract: Funding Acknowledgements: Type of funding sources: None. Background: Atrial fibrillation (AF) - the most common sustained cardiac arrhythmia - while not a life-threatening condition itself, leads to an increased risk of stroke and high rates of mortality. Early detection and diagnosis of AF is a critical issue for all health stakeholders. Purpose: The aim of this study is to compare the performance of standard P-wave indices with beat-to-beat P-wave morphological variability parameters in identifying patients with history of Paroxysmal Atrial Fibrillation (PAF). Methods: Three-dimensional 1000Hz ECG digital recordings of 10 minutes duration were obtained from a total of 39 PAF patients and 60 healthy individuals. Following artifacts and ectopic beats removal, P‑wave morphology analysis was performed based on the dynamic application of the k‑means clustering process and main and secondary P-wave morphologies were identified. The percentage of P-waves following the main or the secondary morphology in each lead was calculated, as well as established indices such as Advanced Interatrial Block, P-wave duration, axis and area, P-wave Terminal Force in lead V1 and Orthogonal Leads Type 1, 2 or 3. Results: 9 out of 24 parameters studied, were found to be significantly different between the two groups. 7 of these indices were derived from morphology analysis and 2 from P-wave area. Logistic regression revealed that the percentage of P-waves allocated to main morphology in X axis performed better than all other indices in identifying patients with PAF history from healthy volunteers in terms of total accuracy and F1 measure. Conclusion: P-wave beat-to-beat morphology analysis can identify PAF patients during normal sinus rhythm more efficiently than standard P-wave indices.