Atrial fibrillation detection with and without atrial activity analysis using lead-I mobile ECG technology. (April 2021)

• Record Type: Journal Article
• Authors: Tuboly, Gergely; Kozmann, György; Kiss, Orsolya; Merkely, Béla
• Is Part Of: Biomedical signal processing and control. Volume 66(2021)
• Publication Date: 2021-04
• Subjects: A1 our AF detection algorithm version relying only on heart rhythm -- A2 our AF detection algorithm version taking both heart rhythm and atrial activity into account heartrhythm evaluation -- AA atrial activity -- ACC accuracy -- AF atrial fibrillation -- HR heart rate -- LTAFD Long-Term Atrial Fibrillation Database -- MSA marked sinus arrhythmia -- MBAD MIT-BIH Arrhythmia Database -- MBAFD MIT-BIH Atrial Fibrillation Database -- MBNSRD MIT-BIH Normal Sinus Rhythm Database -- NSR normal sinus rhythm -- PPV positive predictive value -- PPG photoplethysmogram -- Se sensitivity -- Sp specificity -- SPB supraventricular premature beat -- VPB ventricular premature beat -- WCD WIWE clinical dataset; Atrial activity -- Atrial fibrillation detection -- Heart rhythm -- Single-channel mobile electrocardiography; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102462
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Both rhythm and atrial activity are analyzed by the atrial fibrillation detector. Atrial activity analysis increases specificity of atrial fibrillation detection. Atrial fibrillation detection methods should be tested on other arrhythmias as well. The atrial fibrillation detector proved to be efficient in the WIWE mobile ECG system. Abstract: Objective: This paper presents the performance of an atrial fibrillation (AF) detection algorithm in a lead-I mobile ECG setting. Our aim is to demonstrate that false positive cases occurring due to non-AF arrhythmias can be significantly reduced by taking atrial activity into account in addition to heart rhythm. Methods: AF detection was carried out in two ways: only by heart rhythm assessment based on Poincaré plot of RR intervals (A1), and by also performing P wave analysis on the average majority cycle (A2). Results: The algorithm was tested on the PhysioNet MIT-BIH Atrial Fibrillation Database, Long-Term AF Database, MIT-BIH Arrhythmia Database, and MIT-BIH Normal Sinus Rhythm Database. A1 produced an average sensitivity ( Se ) of 97.64 % and specificity ( Sp ) of 93.06 %. A2 resulted Se = 96.52 % and Sp = 99.12 %. Additionally, clinical data acquisition was performed by the Sanatmetal WIWE mobile ECG system. On the latter records, Se = 100 % and Sp = 95.31 % were obtained with A1, while A2 resulted Se = 100 % and Sp = 100 %. Conclusion: Atrial activity analysis significantly increased Sp in records with frequent premature beats (by almost 12 %) and marked sinus arrhythmia (by 7.5 %). Based on our results and data from the literature, we suggest that AF detection methods should be tested more carefully on non-AF arrhythmia cases. Applied in WIWE, the A2 version of our algorithm outperforms the AF detectors of current single-channel mobile ECG systems. Significance: The results contribute to the significant reduction of false positive AF detections in lead-I mobile ECG technology.