Kitajima Hiroyuki, Takeda Kentaro, Ishizawa Makoto, Aihara Kazuyuki, Minamino Tetsuo
Faculty of Engineering and Design, Kagawa University, 2217-20, Hayashi, Takamatsu, Kagawa 761-0396, Japan.
Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa 761-0793, Japan.
Chaos. 2025 Mar 1;35(3). doi: 10.1063/5.0212068.
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.
我们提出了一种在通过血压监测仪获得的脉搏波测量中区分心房颤动和窦性心律的分类方法。该方法将基于递归的图与卷积神经网络相结合。此外，我们设计了一种新颖的图，通过它我们的分类实现了97.5%的特异性、98.4%的灵敏度和98.6%的准确率。这些标准高于先前报道的使用血压监测仪获得的测量结果，并且几乎与基于心电图和光电容积脉搏波描记法的方法的统计指标相当。
