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引用本文:朱宇翔,童基均,夏淑东,朱海航.基于连续小波变换和残差神经网络的房颤预测研究[J].软件工程,2024,27(9):62-66.【点击复制】
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基于连续小波变换和残差神经网络的房颤预测研究
朱宇翔1, 童基均1, 夏淑东2, 朱海航1
(1.浙江理工大学信息科学与工程学院, 浙江 杭州 310018;
2.浙江大学医学院附属第四医院, 浙江 义乌 322000)
zhyxang7@163.com; jijuntong@zstu.edu.cn; shystone@126.com; haihangzhu_zstu@163.com
摘 要: 心房颤动(AF)是一种最常见的心律失常类型,为了提高房颤预测的准确率和可靠性,提出了一种基于连续小波变换和残差神经网络的房颤预测方法。首先,采用软阈值小波去噪方法去除心电图信号的噪声干扰;其次,通过连续小波变换生成二维时频图;最后,使用带下采样的残差神经网络进行房颤预测。为了全面评估所提方法的性能,新建立了一个包含2 160条心电图(ECG)记录的综合数据集,并在此数据集上进行了实验。实验结果表明,该方法在新数据集和公开数据集(AFPDB)上分别得到92.4%和96.1%的精确度,相较于当前的深度学习方法,实现了显著提升。
关键词: 房颤;心电图;连续小波变换;残差网络
中图分类号: TP391    文献标识码: A
基金项目: 浙江省自然科学基金项目(LQ22F010006; LTGY23H170004)
Research on Atrial Fibrillation Prediction Based on Continuous Wavelet Transform and Residual Neural Networks
ZHU Yuxiang1, TONG Jijun1, XIA Shudong2, ZHU Haihang1
(1.School of Inf ormation Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China;
2.The Fourth Af f iliated Hospital Zhejiang University School of Medicine, Yiwu 322000, China)
zhyxang7@163.com; jijuntong@zstu.edu.cn; shystone@126.com; haihangzhu_zstu@163.com
Abstract: Atrial Fibrillation (AF) is the most common type of arrhythmia. To improve the accuracy and reliability of AF prediction, a method based on continuous wavelet transform and residual neural network is proposed. Firstly, a soft-threshold wavelet denoising method is used to remove noise interference from the Electrocardiogram (ECG) signal. Secondly, a two-dimensional time-frequency map is generated through continuous wavelet transform. Finally, a downsampled residual neural network is used for AF prediction. To comprehensively evaluate the performance of the proposed method, a new comprehensive dataset containing 2 160 ECG records has been established, and experiments have been conducted on this dataset. Experimental results show that the method achieves accuracy of 92.4% on the new dataset and 96.1% on the publicly available dataset (AFPDB), respectively, realizing significant improvements compared to current deep learning methods.
Keywords: AF; Electrocardiogram; continuous wavelet transform; residual network


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