Remote atrial fibrillation burden estimation using deep recurrent neural network
The atrial fibrillation burden (AFB) is defined as the percentage of time spend in atrial fibrillation (AF) over a long enough monitoring period. Recent research has demonstrated the added prognosis value that becomes available by using the AFB as compared with the binary diagnosis. We evaluate, for...
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Zusammenfassung: | The atrial fibrillation burden (AFB) is defined as the percentage of time
spend in atrial fibrillation (AF) over a long enough monitoring period. Recent
research has demonstrated the added prognosis value that becomes available by
using the AFB as compared with the binary diagnosis. We evaluate, for the first
time, the ability to estimate the AFB over long-term continuous recordings,
using a deep recurrent neutral network (DRNN) approach. Methods: The models
were developed and evaluated on a large database of p=2,891 patients, totaling
t=68,800 hours of continuous electrocardiography (ECG) recordings acquired at
the University of Virginia heart station. Specifically, 24h beat-to-beat time
series were obtained from a single portable ECG channel. The network, denoted
ArNet, was benchmarked against a gradient boosting (XGB) model, trained on 21
features including the coefficient of sample entropy (CosEn) and AFEvidence.
Data were divided into training and test sets, while patients were stratified
by the presence and severity of AF. The generalizations of ArNet and XGB were
also evaluated on the independent test PhysioNet LTAF database. Results: the
absolute AF burden estimation error |E_AF|, median and interquartile, on the
test set, was 1.2 (0.1-6.7) for ArNet and 3.1 (0.0-11.7) for XGB for AF
individuals. Generalization results on LTAF were consistent with E_AF of 2.6
(1.1-14.7) for ArNet and 3.6 (1.0-16.7) for XGB. Conclusion: This research
demonstrates the feasibility of AFB estimation from 24h beat-to-beat interval
time series utilizing recent advances in DRNN. Significance: The novel
data-driven approach enables robust remote diagnosis and phenotyping of AF. |
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DOI: | 10.48550/arxiv.2008.02228 |