Classification of myocardial infarction based on ECG signals and multi-network stacking model

Myocardial infarction is one of the common cardiovascular diseases, and it also is a major cause of death and disability worldwide every year. Therefore, early detection of myocardial infarction is vital for patients. Deep learning algorithms have been used successfully for the recognition of myocardial infarction in recent years, but most of them are based on the segmentation of heartbeat signals and the single deep learning model. In this paper, we proposed a new myocardial infarction classification method based on an improved ECG feature extraction scheme and the multi-network stacking model. Firstly, the mean amplitude spectrum (MAS) is used in feature extraction to elaborate the frequency distribution difference of each wave among ECG signals. Secondly, due to the lack of time-domain features in the MAS map, we proposed a novel time-frequency feature fusion method. Finally, the time-frequency feature is fed to the multi-network stacking model for the classification of myocardial infarction. To improve the diversity of feature learning, this paper researches to stack different networks for learning the characteristic of the time-frequency feature. Meanwhile, we used the improved training methods to solve the problem of data imbalance. Experiments using the PTB database show that this feature extraction scheme and multi-network stacking model are effective for the classification of myocardial infarction.