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Background The mobile and wearable technology and artificial technology are rapidly developing for atrial fibrillation (AF) detection. Nonetheless, how these smart technologies combine to improve AF prediction, rather than detection, remains unclear. Objective To investigate a machine-learning fusion approach beyond photoplethysmographic (PPG)-based AF detection for predicting AF onset. Methods There are 2,120,210 individuals who used PPG-based smart devices (Huawei Technologies Co., Ltd., Shenzhen, China) to monitor their pulse rhythm from pre-mAFA study (Huawei heart study) for AF screening across China between October 26, 2018 and March 26, 2021. Two machine-learning (ML) models, short-term model and long-term mode, have been developed based on PPG continuing monitoring signals to predict AF episode at 4h or 8h before AF onset respectively, with supervised ML (XGBoost and LightGBM) techniques, and further optimized with feature extension and hyperparameter optimization, respectively. The probabilities of AF onset calculated by both short-term and long-term models with timely PPG monitoring signals would be further input into Warning Decision Module, combined with prior predicting risk of AF onset, then the real risk ratio of predicting AF onset in advance would be output by the fusion module (Figure). The predictive ability of ML models, classified by time to last AF onset, has been validated among individuals involving in AF screening from pre-mAFA study between March 19 to March 26, compared to identified AF by PPG monitoring. Results There were 6294 (mean age ± standard deviation, 51.6±16.0 years old, 5439 male) individuals with identified AF, with total 142,518 identified AF episodes by PPG. The 107,864 identified AF were utilized to validate ML fusion approach for the prediction of AF, given effective PPG monitoring signals before identified AF episodes for ML models. There were 443,630 PPG monitoring signals before identified AF episodes and 563,309 non-AF PPG monitoring signals of ML models. The sensitivity of ML fusion approach for the prediction of AF onset was 94.04%, the specificity of 96.35%, and the recall of 94.04%, respectively (Table). When time to last AF onset over 24 hours, the sensitivity of ML fusion approach for AF onset at 8h in advance was 85.73%, specificity of 96.62%, and recall of 85.73%, respectively (Table). Conclusion The ML fusion approach based on PPG-smart devices provided the effective early 'alters' of AF onset in advance in this large cohort, which may help the early 'upstream' management of AF. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National HealthCare of China (20BJZ26)

Machine-learning fusion approach for the prediction of atrial fibrillation onset using photoplethysmographic-based smart device