TY - JOUR
T1 - Remote Atrial Fibrillation Burden Estimation Using Deep Recurrent Neural Network
AU - Chocron, Armand
AU - Oster, Julien
AU - Biton, Shany
AU - Mandel, Franck
AU - Elbaz, Meyer
AU - Zeevi, Yehoshua Y.
AU - Behar, Joachim A.
N1 - Publisher Copyright:
© 1964-2012 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - Objective: The atrial fibrillation burden (AFB) is defined as the percentage of time spent in atrial fibrillation (AF) over a long enough monitoring period. Recent research has suggested the added prognostic value of using the AFB compared to a 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 from the University of Virginia. 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 that is derived from the number of irregular points revealed by the Lorenz plot. The generalizations of ArNet and XGB were also evaluated on the independent PhysioNet LTAF test database. Results: the absolute AF burden estimation error | EAF(%)} |, median and interquartile, on the test set, was 1.2 (0.1-6.7) for ArNet and 2.8 (0.9-11.7) for XGB for AF individuals. Generalization results on LTAF were consistent with | EAF(% | of 2.7 (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 DRNNs. Significance: The novel data-driven approach enables robust remote diagnosis and phenotyping of AF.
AB - Objective: The atrial fibrillation burden (AFB) is defined as the percentage of time spent in atrial fibrillation (AF) over a long enough monitoring period. Recent research has suggested the added prognostic value of using the AFB compared to a 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 from the University of Virginia. 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 that is derived from the number of irregular points revealed by the Lorenz plot. The generalizations of ArNet and XGB were also evaluated on the independent PhysioNet LTAF test database. Results: the absolute AF burden estimation error | EAF(%)} |, median and interquartile, on the test set, was 1.2 (0.1-6.7) for ArNet and 2.8 (0.9-11.7) for XGB for AF individuals. Generalization results on LTAF were consistent with | EAF(% | of 2.7 (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 DRNNs. Significance: The novel data-driven approach enables robust remote diagnosis and phenotyping of AF.
KW - Atrial fibrillation burden
KW - recurrent neural network
KW - remote health monitoring and digital health
KW - Neural Networks, Computer
KW - Humans
KW - Electrocardiography
KW - Entropy
KW - Atrial Fibrillation/diagnosis
KW - Databases, Factual
UR - http://www.scopus.com/inward/record.url?scp=85097961015&partnerID=8YFLogxK
U2 - 10.1109/TBME.2020.3042646
DO - 10.1109/TBME.2020.3042646
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
C2 - 33275575
AN - SCOPUS:85097961015
SN - 0018-9294
VL - 68
SP - 2447
EP - 2455
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 8
M1 - 9281068
ER -