Detection of inferior myocardial infarction based on multi branch hybrid network

Background and Objectives: Early and accurate detection of inferior myocardial infarction (IMI) is important for reducing the risk of mortality from a heart attack. Although previous work has demonstrated IMI detection, the differences among patients have been ignored. Most models display excellent performance in the intra-patient scheme, but the inter-patient test results are not ideal. The present paper proposes a model based on densely connected convolutional and gated recursive unit (GRU) networks to enhance the generalization ability of the model.Methods: Firstly, the data of multi-lead beat in series is used with GRU, to obtain more inter-lead and intra-lead time correlation information. This correlation information is scientific and significant for IMI detection. Sec-ondly, the proposed model retains both deep and shallow features of ECG through DenseNet, which include more detailed information of IMI. Finally, through the feature connection, the multi-dimensional features enrich the description of ECG signals, and help the network learn more essential characteristics of IMI, so as to enhance the generalization ability of the model.Results: The proposed method was verified by the PTB diagnostic database of the German National Metrology Institute. The generalization ability of the model was tested by intra-patient and inter-patient schemes. After 5 -fold cross-validation, the average accuracy, sensitivity and specificity were 99.95%, 99.94% and 99.96% in the intra-patient scheme respectively. Furthermore, these parameters were 88.68%, 90.33% and 87.04% in the inter -patient scheme.Conclusions: The experimental results show that the model displays good generalization ability, which has important clinical significance.