Atrial fibrillation detection using reservoir computing and anomaly detection method from automated blood pressure measurements

Abstract

We propose a method for detecting abnormal cardiac rhythms—such as those associated with atrial fibrillation (AF)—from pulse waves measured by an automated blood pressure monitor. The method is based on reservoir computing and detects anomalies by evaluating the prediction error of a model trained solely on normal (sinus rhythm, SR) data. Hyperparameters were tuned using a validation set containing both SR and AF samples. Our method achieved a sensitivity of 100.0%, specificity of 98.6%, and accuracy of 99.2% on an independent test set, outperforming previously reported results. Our results suggest its potential as a cost-effective and accurate embedded screening device for identifying abnormal cardiac rhythms in daily health monitoring.