Detection of atrial fibrillation from pulse waves using convolution neural networks and recurrence-based plots
We propose a classification method for distinguishing atrial fibrillation from sinus rhythm in pulse-wave measurements obtained with a blood pressure monitor. This method combines recurrence-based plots with convolutional neural networks. Moreover, we devised a novel plot, with which our classification achieved specificity of 97.5%, sensitivity of 98.4%, and accuracy of 98.6%. These criteria are higher than previously reported results for measurements obtained with blood pressure monitors and are almost equal to statistical measures for methods based on electrocardiographs and photoplethysmographs.
Authors
- Kentaro Takeda (ORCID: https://orcid.org/0000-0002-9071-9101)
- Kazuyuki Aihara (ORCID: https://orcid.org/0000-0002-4602-9816)
- Makoto Ishizawa
- Hiroyuki Kitajima (ORCID: https://orcid.org/0000-0003-2625-2508)
- Tetsuo Minamino (ORCID: https://orcid.org/0000-0002-5424-4081)
Institutions
- Kagawa University (JP)
- The University of Tokyo (JP)
Publication Details
- Journal
- Chaos An Interdisciplinary Journal of Nonlinear Science
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1063/5.0212068
- Citations
- 2
- Primary Topic
- Non-Invasive Vital Sign Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Japan Agency for Medical Research and Development
- Japan Society for the Promotion of Science London
- Japan Society for the Promotion of Science
- Moonshot Research and Development Program