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

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.