【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。更多还原

【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 down-sampled 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.更多还原