Work

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

Year: 2025

Type: article

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.

Source: Nonlinear Theory and Its Applications IEICE

DOI: 10.1587/nolta.17.228

Authors: Kentaro Takeda, Hiroyuki Kitajima, Makoto Ishizawa, Tetsuo Minamino

Institution: Kagawa University

Language: English

Topic: Non-Invasive Vital Sign Monitoring

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: Japan Agency for Medical Research and Development, Moonshot Research and Development Program