Abstract: For the growing demand of ECG data analysis,a new ECG classification algorithm is proposed.Firstly,the original data are truncated by fixed length,sample equilibrium is obtained,and the pre-processing operations such as instantaneous frequency and spectral entropy of the signal are obtained.After the data is preprocessed,the model can better extract features from the data for learning.In training progress,a two-way LSTM network stacking model is adopted.The stacked two-way LSTM model is an improved cyclic neural network model.Compared with convolutional neural networks,the cyclic neural network is more sui-table for processing sequence data such as electrocardiogram.The experiment is conducted using MATLAB2018b under Windows for training and testing.The CUDA version is 9.0.The classification accuracy rate is used as an indicator to measure the performance of the model.The model is tested on two data sets,one is the data of the 2017 Physiological Signal Challenge(hereinafter referred to as the 2017 dataset),the final classification accuracy rate is 97.4%;the other is the data of the 2018 Physiological Signal Challenge (hereinafter referred to as the 2018 dataset),and the final classification accuracy rate is 77.6% on this dataset.The MATLAB group to which it belongs has achieved the third place.This algorithm improves the accuracy of 5.6% in the 2017 dataset and 7.6% in the 2018 dataset compared to the results of the traditional LSTM network.Compared to the results of a single-layer bidirectional LSTM network,in the 2017 data set,the accuracy rate improves 4.2%,and the accuracy rate improves 5.7％ in the 2018 data set,which fully verifies the feasibility and advantages of the algorithm.

Key words: Arrhythmia, Deep learning, Electrocardiogram classification, Stacked bidirectional LSTM network