[1]张悦,陈真诚,梁永波,等.基于光电容积脉搏波的心房颤动识别方法[J].中国医学物理学杂志,2020,37(11):1416-1420.[doi:DOI:10.3969/j.issn.1005-202X.2020.11.014]
　ZHANG Yue,CHEN Zhencheng,LIANG Yongbo,et al.Atrial fibrillation identification based on photoplethysmography[J].Chinese Journal of Medical Physics,2020,37(11):1416-1420.[doi:DOI:10.3969/j.issn.1005-202X.2020.11.014]
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基于光电容积脉搏波的心房颤动识别方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第11期
页码:
1416-1420
栏目:
医学信号处理与医学仪器
出版日期:
2020-12-02

文章信息/Info

Title:
Atrial fibrillation identification based on photoplethysmography
文章编号:
1005-202X（2020）11-1416-05
作者:
张悦1; 陈真诚1; 梁永波2; 朱健铭2
1.桂林电子科技大学电子工程与自动化学院， 广西 桂林 541004； 2.桂林电子科技大学生命与环境科学学院， 广西 桂林 541004
Author(s):
ZHANG Yue1;  CHEN Zhencheng1;  LIANG Yongbo2;  ZHU Jianming2
1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China 2. School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
关键词:
心房颤动; 光电容积脉搏波; BP神经网络; 随机森林; 支持向量机
Keywords:
Keywords: atrial fibrillation photoplethysmography BP neural network random forest support vector machine
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2020.11.014
文献标志码:
A
摘要:
为了能够简便快捷地检测心房颤动，提出一种基于光电容积脉搏波描记法（PPG）对心房颤动进行识别的方法。首先，将已确诊为心房颤动状态脉搏波与健康状态脉搏波数据进行对比分析；其次，基于分析结果，从脉搏波数据中提取与心房颤动相关的6类特征参数作为分类器的输入；最后，使用支持向量机（SVM）、BP神经网络、随机森林算法3种分类器建立心房颤动识别模型，其识别心房颤动的准确率分别可达89.1%、92.3%、95.2%。实验结果表明，基于PPG的心房颤动识别方法具有很高的识别准确率，尤其在使用随机森林算法作为分类器时，识别准确率达到最优。同时该检测方法简便快捷，是一种可以替代传统心电图检测识别心房颤动的方法，对心房颤动患者的长期观察监测具有临床价值。
Abstract:
Abstract: A photoplethysmography (PPG)-based method for identifying atrial fibrillation is proposed to detect atrial fibrillation easily and quickly. Firstly, the pulse waves of atrial fibrillation and those in health status are compared and analyzed. Secondly, based on the analysis results, 6 types of characteristic parameters related to atrial fibrillation are extracted from PPG data as the input of classifiers. Finally, 3 classifiers, namely support vector machine, BP neural network and random forest algorithm, are used to establish the model for atrial fibrillation identification. The identification accuracies of the 3 models are 89.1%, 92.3% and 95.2%, respectively. The experimental results show that the atrial fibrillation identification method based on PPG has a high accuracy, especially when using random forest algorithm as the classifier. Meanwhile, the proposed detection method which is more?onvenient?nd?apid can replace the traditional ECG detection to identify atrial fibrillation, having clinical value for the long-term observation and monitoring of patients with atrial fibrillation.

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备注/Memo

备注/Memo:
【收稿日期】2020-04-27 【基金项目】国家基金委重大仪器研制项目（61627807）；国家自然科学基金（81873913）；广西创新研究团队项目（2017GXNSFGA198005） 【作者简介】张悦，硕士，研究方向：生物传感与仪器，E-mail: 3066952- 98@qq.com
更新日期/Last Update: 2020-12-02