Abstract

Info

Title:: ECG signal reconstruction based on multi-layer feature fusion using neural network

Author(s):: YAO Yuanxing; WANG Fei; LIU Wenhan; HE Jin; WANG Hao; CHANG Sheng; HUANG Qijun; School of Physics and Technology, Wuhan University, Wuhan 430072, China

Keywords:: electrocardiogram electrocardiogram signal reconstruction convolutional neural network bidirectional long short-term memory

Abstract:: A novel neural network which can achieve multi-layer feature fusion is proposed for reconstructing the electrocardiogram (ECG) signals of other leads using the known ECG signals of leads I, II and V2. The features of multiple leads are extracted by convolutional neural network for linear combination, and an improved bidirectional long short-term memory network structure is used to obtain temporal sequence correlation which is then fused with the features obtained by convolutional neural network for realizing ECG signal reconstruction. The proposed method is verified with Physikalisch Technische Bundesanstalt database. The results show that the signal reconstruction method has a correlation coefficient of 0.944 4 and a low root-mean-square error of 0.320 3, which demonstrates the effectiveness of the novel neural network structure for ECG signal reconstruction.