Evaluation of the performance of a machine learning based atrial fibrillation screening algorithm using an oscillometric blood pressure monitor.

Blood pressure monitors (BPMs) with atrial fibrillation (AFib) detection function can be used to detect AFib early. However, conventional algorithms require multiple BP measurements. Here, the feasibility of a machine-learning-based approach for AFib detection through single BP measurement was evaluated. First, a custom AdaBoost-based software, which analyzes the pulse-to-pulse interval (PPI) pattern and classifies it based on AFib detection, was created. Then, its classification performance was validated. For the validation study, PPI and standard 12-lead electrocardiogram (ECG) datasets were collected from 79 and 92 Japanese participants with and without AFib, respectively. PPI data were obtained using two different BPMs. All ECG results were interpreted by cardiologists. The custom software output for the PPI dataset and ECG interpreted results was compared, and the sensitivity and specificity were calculated. A sensitivity and specificity for PPI from main device were 97.5% (95% confidence interval [CI] 91.2-99.3%) and 98.9 (95% CI 94.1-99.8), respectively. No significant differences in sensitivity and specificity were observed in the subgroup analysis between different devices, age groups, and arm size groups. These results reflect the high accuracy and robustness of this AFib algorithm using a single BP measurement and supports its use for widespread AFib screening.