## 1 Introduction  
Myocardial infarction is a cardiovascular disease caused by myocardial insufficient blood supply or even myocardial necrosis due to coronary artery occlusion. According to statistics from the American Health Association, nearly 720,000 Americans suffer from myocardial infarction each year [1]. In the early stage of this disease, patients with myocardial infarction usually show symptoms such as chest pain and chest tightness, but some patients still have no obvious symptoms, which makes it difficult to treat in time, thus threatening life [2]. Therefore, how to achieve the early diagnosis of myocardial infarction has a significant clinical value, and has become a research topic of many scholars. Electrocardiogram (ECG) is one of the routine examination methods for myocardial infarction [2]. In the field of ECG signal processing, many traditional studies have focused on the feature extraction of myocardial infarction ECG signals including time domain, frequency domain, wavelet transform, and other characteristics. [PERSON] et al. extracted ST segments and combined support vector machine (SVM) and multi-instance learning to complete myocardial infarction ECG classification [3]. [PERSON] et al. started with the three time-domain features of T wave amplitude, Q wave amplitude, and ST segment offset level, and used the K-nearest neighbor (KNN) algorithm to achieve the detection and location of myocardial infarction [4]. [PERSON] et al. obtained the frequency domain features of ECG such as sample entropy, and applied SVM and the KNN algorithm to classify different types of myocardial infarction ECG [5]. Similar to [PERSON], [PERSON] et al. extracted T wave characteristics from ECG signals, and employed pattern recognition methods for myocardial infarction classification [6]. Although the above algorithms can achieve good results, as ECG signals are very weak and susceptible to noise interference, feature point recognition cannot be guaranteed, which has become a limitation of such methods. In recent years, with the development of deep learning, convolutional neural network (CNN), and recurrent neural network (RNN) have achieved great success in image classification, object detection, and speech recognition. Deep learning methods such as CNN automatically learn and extract features through deep neural networks, independent of the acquisition of handcrafted features and expert knowledge [7; 8; 9]. In the area of ECG signal processing, compared with the traditional methods, the deep learning method avoids ECG handcrafted feature extraction to simplify the implementation process to a certain extent, and has been applied by scholars. [PERSON] et al. completed the classification of atrial fibrillation ECG through a 16-layer CNN [10]. Due to the temporal characteristics of ECG signals, the long-short term memory (LSTM) in RNN also performs well. [PERSON] et al. used LSTM to complete the classification of arrhythmia ECG [11]; in the classification of myocardial infarction ECG, [PERSON] et al. extracted ECG signals from II, III, and AVF leads, and performed preprocessing and classification through shallow CNN [12]; moreover, [PERSON] et al. established a deep CNN to classify the noisy and denoised myocardial infarction ECG [13]. With the popularization of handheld electrocardiographs, smart bands, and smart watches, access to a single-lead ECG is possible in personal and home detection. Thus, it is important to detect and prevent myocardial infarction through single-lead ECG. Since there have only been a few studies on single-lead myocardial infarction ECG, there is still a very large space for exploration. Therefore, this paper proposed a deep learning method combining CNN and RNN, established a multi-channel CNN-LSTM network structure, segmented the pre-processed ECG signal, extracted spatial features in the multi-channel convolution network, and acquired the temporal characteristics through LSTM.