Remote Atrial Fibrillation Burden Estimation Using Deep Recurrent Neural Network.

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 [Formula: see text], 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 [Formula: see text] 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.

Machine learning for nocturnal mass diagnosis of atrial fibrillation in a population at risk of sleep-disordered breathing.

OBJECTIVE: In this research, we introduce a new methodology for atrial fibrillation (AF) diagnosis during sleep in a large population sample at risk of sleep-disordered breathing. APPROACH: The approach leverages digital biomarkers and recent advances in machine learning (ML) for mass AF diagnosis from overnight-hours of single-channel electrocardiogram (ECG) recording. Four databases, totaling n = 3088 patients and p = 26 913 h of continuous single-channel electrocardiogram raw data were used. Three of the databases (n = 125, p = 2513) were used for training a ML model in recognizing AF events from beat-to-beat time series. Visit 1 of the sleep heart health study database (SHHS1, n = 2963, p = 24 400) was used as the test set to evaluate the feasibility of identifying prominent AF from polysomnographic recordings. By combining AF diagnosis history and a cardiologist's visual inspection of individuals suspected of having AF (n = 118), a total of 70 patients were diagnosed with prominent AF in SHHS1. MAIN RESULTS: Model prediction on SHHS1 showed an overall [Formula: see text]and [Formula: see text] in classifying individuals with or without prominent AF. [Formula: see text] was non-inferior (p = 0.03) for individuals with an apnea-hypopnea index (AHI) ≥15 versus AHI < [Formula: see text]. Over 22% of correctly identified prominent AF rhythm cases were not previously documented as AF in SHHS1. SIGNIFICANCE: Individuals with prominent AF can be automatically diagnosed from an overnight single-channel ECG recording, with an accuracy unaffected by the presence of moderate-to-severe obstructive sleep apnea. This approach enables identifying a large proportion of AF individuals that were otherwise missed by regular care.

Single-channel oximetry monitor versus in-lab polysomnography oximetry analysis: does it make a difference?

OBJECTIVE: Portable oximetry has been shown to be a promising candidate for large-scale obstructive sleep apnea screening. In polysomnography (PSG), the gold standard OSA diagnosis test, the oxygen desaturation index (ODI) is usually computed from desaturation events occurring during sleep periods only, i.e. overnight desaturations occurring during or overlapping with a wake state are excluded. However, for unattended home oximetry, all desaturations are taken into account since no reference electroencephalogram is available for sleep staging. We aim to evaluate the hypothesis that the predictive power of oximetry for OSA screening is not impaired when reference sleep stages are not available. APPROACH: We used a PSG clinical database of 887 individuals from a representative São Paulo (Brazil) population sample. Using features derived from the oxygen saturation time series and demographic information, OxyDOSA, a published machine learning model, was trained to distinguish between non-OSA and OSA individuals using the ODI computed while including versus excluding overnight desaturations overlapping with a wake period, thus mimicking portable and PSG oximetry analyses, respectively. MAIN RESULTS: When excluding wake desaturations, the OxyDOSA model had an AUROC = 94.9 ± 1.6, Se = 85.9 ± 2.8, Sp = 90.1 ± 2.6 and F1 = 86.4 ± 2.7. When considering wake desaturations, the OxyDOSA model had an AUROC = 94.4 ± 1.6, Se = 88.0 ± 2.0, Sp = 87.7 ± 2.9 and F1 = 86.2 ± 2.4. Non-inferiority was demonstrated (p = 0.049) at a tolerance level of 3%. In addition, analysis of the desaturations excluded by PSG oximetry analysis suggests that up to 21% of the total number of desaturations might actually be related to apneas or hypopneas. SIGNIFICANCE: This analysis of a large representative population sample provided strong evidence that the predictive power of oximetry for OSA screening using the OxyDOSA model is not impaired when reference sleep stages are not available. This finding motivates the usage of portable oximetry for OSA screening.