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

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.