基于深度卷积残差网络的心电单导联房颤检测方法

ECG-based Atrial Fibrillation Detection Based on Deep Convolutional Residual Neural Network

在智能诊断需求日益增长的背景下,提出了一种基于残差网络形式构建的卷积神经网络,该模型作为心电信号房颤分类的方法,使用MIT-BIH的心房颤动公开数据集来验证所提方法的效果,以辅助房颤自动检测。针对心电信号二分类问题,首先,对数据集进行前期数据预处理,然后将处理后的数据输入到卷积神经网络,以构建深度学习模型,使其对房颤特征进行自动提取,最后利用深度学习模型进行房颤检测,通过五折交叉验证得到构建模型分类的敏感性为99.26%,特异性为99.42%,阳性预测值为99.61%,准确率为99.47%。将所提模型的性能与已有模型进行了比较,证实了所提模型用于房颤检测的可行性。由此得出结论,通过残差网络构建的房颤自动检测系统可以达到房颤的良好分类效果,有助于房颤自动检测。

In the context of increasing demand for intelligent diagnosis,a convolutional neural network model based on residual network is proposed for ECG(electrocardiogram)signal classification of atrial fibrillation.MIT-BIH atrial fibrillation data is used to verify the effectiveness of the method,and then assist the automatic detection of atrial fibrillation.Aiming at the problem of ECG signal dichotomy,firstly,the atrial fibrillation data set and previous data preprocessing work are introduced.Then,the processed data is input into the deep learning model constructed with convolutional neural network,to automatically extract features of atrial fibrillation from electrocardiogram signals.Finally,the designed deep learning model is used for atrial fibrillation detection.The validation of the method is proved with five cross-validation strategy.Performance of the classification is represented by the sensitivity,specificity,positive predictive value and accuracy,they are 99.26%,99.42%,99.61%and 99.47%,respectively.Then the performance of the proposed model and existing models are compared to confirm that the proposed model is feasible in atrial fibrillation detection.In conclusion,the automatic detection system for atrial fibrillation based on convolutional neural network with residual network can achieve a good classification performance of atrial fibrillation,which can be helpful in automatic atrial fibrillation detection.