Software-based detection of atrial fibrillation in long-term ECGs

Haeberlin, Andreas; Roten, Laurent; Schilling, Manuel; Scarcia, Flavio; Niederhauser, Thomas; Vogel, Rolf; Fuhrer, Juerg; Tanner, Hildegard (2014). Software-based detection of atrial fibrillation in long-term ECGs. Heart rhythm, 11(6), pp. 933-938. Elsevier 10.1016/j.hrthm.2014.03.014

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Background

Atrial fibrillation (AF) is common and may have severe consequences. Continuous long-term electrocardiogram (ECG) is widely used for AF screening. Recently, commercial ECG analysis software was launched, which automatically detects AF in long-term ECGs. It has been claimed that such tools offer reliable AF screening and save time for ECG analysis. However, this has not been investigated in a real-life patient cohort.

Objective

To investigate the performance of automatic software-based screening for AF in long-term ECGs.

Methods

Two independent physicians manually screened 22,601 hours of continuous long-term ECGs from 150 patients for AF. Presence, number, and duration of AF episodes were registered. Subsequently, the recordings were screened for AF by an established ECG analysis software (Pathfinder SL), and its performance was validated against the thorough manual analysis (gold standard).

Results

Sensitivity and specificity for AF detection was 98.5% (95% confidence interval 91.72%–99.96%) and 80.21% (95% confidence interval 70.83%–87.64%), respectively. Software-based AF detection was inferior to manual analysis by physicians (P < .0001). Median AF duration was underestimated (19.4 hours vs 22.1 hours; P < .001) and median number of AF episodes was overestimated (32 episodes vs 2 episodes; P < .001) by the software. In comparison to extensive quantitative manual ECG analysis, software-based analysis saved time (2 minutes vs 19 minutes; P < .001).

Conclusion

Owing to its high sensitivity and ability to save time, software-based ECG analysis may be used as a screening tool for AF. An additional manual confirmatory analysis may be required to reduce the number of false-positive findings.

Item Type:

 Journal Article (Original Article)

Division/Institute:

 10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Cardiovascular Engineering (CVE)
04 Faculty of Medicine > Department of Cardiovascular Disorders (DHGE) > Clinic of Cardiology
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Kardiologie
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Kardiologie
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

UniBE Contributor:

 Häberlin, Andreas David Heinrich, Roten, Laurent, Niederhauser, Thomas, Vogel, Rolf, Tanner, Hildegard

Subjects:

 600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISSN:

 1547-5271

Publisher:

 Elsevier

Language:

 English

Submitter:

 Andreas Häberlin

Date Deposited:

 15 Sep 2014 17:40

Last Modified:

 05 Dec 2022 14:32

Publisher DOI:

 10.1016/j.hrthm.2014.03.014

PubMed ID:

 24632179

Uncontrolled Keywords:

 Atrial fibrillation, Software, Software-based analysis, Atrial fibrillation detection, Long-term ECG

BORIS DOI:

 10.7892/boris.49494

URI:

 https://boris.unibe.ch/id/eprint/49494

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