Abstract:  Cardiovascular disease has become one of the major diseases threatening human life and health. Electrocardiogram （ECG）is a common clinical diagnosis of the important methods for arrhythmia and is widely used in health monitoring of patients with heart disease. As a result of the existing medical resources， the use of artificial intelligence method to analysis and diagnosis in order to overcome these limitations of increasingly urgent demand， the use of automatic detection and classification methods in clinical practice may help doctors make accurate and rapid diagnosis of diseases. In this paper， eight common arrhythmia types are classified， and a deep learning classification method of ECG signals based on adaptive antagonism domain is proposed， which solves and improves the problems of insufficient training sample labeling and data distribution differences caused by individual differences. This method consists of three modules: Multi-scale feature extraction module A， domain recognition module B and multi-classifier module C. Module A is composed of two groups of different parallel convolution blocks， which increases the width of feature extraction. Module B is composed of three convolution blocks and a fully connected layer to fully extract shallow features. In module C， time features and deep learning extracted features are connected in series on the fully connected layer to enhance feature diversity. The experimental results show that the accuracy， sensitivity and positive predictive value of this method can reach 98.8%， 97.9% and 98.1%， and the proposed model can help doctors accurately detect different types of arrhythmias in routine electrocardiogram.

Key words: ECG signal classification, deep learning, multi-scale, time characteristics, adversarial domain adaptation