###### Abstract
However, the method based on RR interval features mainly relies on the local localization of the QRS complex to extract the RR interval, and the classification models primarily relied on the QRS complex localization algorithm. Hence, the two parts produce a great degree of coupling. In addition, ML methods are mainly based on manual feature extraction and feature selection, requiring multiple steps to complete the final classification [13], and the process is relatively complex. Deep learning (DL) has the ability of feature learning and classification [14], so various DL methods have been widely applied in AF detection, including convolutional neural networks (CNN) [7; 8; 15; 16; 17] and recurrent neural networks (RNN) [18]. [PERSON] et al. [7] proposed a beat-interval-texture convolutional neural network (BIT-CNN) model based on CNN to extract AF signal features and achieve AF detection through simultaneous one-dimension and two-dimensional convolution of two-dimensional image CS-ECM. [PERSON] et al. [19] proposed a specific-scale stationary wavelet transform processing method to analyze the time-frequency features of ECG signals and used the scale-independent convolutional neural network to extract the time-domain and frequent-domain features of ECG signals, and finally realized better AF detection on the MIT-BIH Atrial Fibrillation database. [PERSON] et al. [17] proposed a 13-layer deep CNN network model for detecting AF in short single-lead ECG recordings. [PERSON] et al. [20] designed a multi-scale fusion convolutional neural network to solve the problem of AF detection of short single-lead ECG recordings. In this case, two CNNs with different sizes of convolution kernels were set up to extract ECG recordings features of different scales. [PERSON] et al. [18] proposed a data enhancement strategy based on ECG, and on this basis, realized the automatic detection of AF using the double-layer LSTM model. [PERSON] et al. [21] obtained seismcardiography and gyrocardiography signals separately by smartphone, and used BLSTM to learn the time series features of the signal, and squeeze-and-excitation (SE) blocks learned the interdependence between channels. [PERSON] et al. [22] proposed a network model that combined CNN and LSTM. The model realized AF detection by directly extracting the RR interval sequence of the ECG signal as input, and verified it on multiple databases. The results showed that the model also had a stable performance on other databases. [PERSON] et al. [8] proposed a dual-channel neural network based on the visual geometry group (VGG) network,in which features were extracted from the time spectrum and Poincare diagram of ECG signals simultaneously, thus achieving effective detection of AF. [PERSON] et al. [23] proposed a multichannel parallel neural network model (MLCNN-BiLSTM). MLCNN was used for feature extraction in the model with multi-lead ECG signals, and BiLSTM was used for timing feature extraction in the second lead. Finally, the two features were weighted and integrated to classify arrhythmias better. Today, the output of deep learning models is comparable with human experts and sometimes achieves even better results [24]. Although ML and DL have achieved good results in detecting atrial fibrillation, the critical problem of real-time atrial fibrillation detection is still not solved. Since most AF detection methods are based on static ECG signals, the applicability of the dynamic ECG signal is limited. In particular, in limited short single-lead ECG recordings, reliable and accurate AF detection remains challenging. To solve the above problems and improve the ability of short single-lead ECG recordings in atrial fibrillation detection, we propose a novel short single-lead ECG recordings AF detection model (MCNN-BLSTM), which is mainly composed of the MCNN module and BLSTM module. First, the MCNN module can set the corresponding branch according to the number of fragments obtained by the overlapping segmentation scheme for feature extraction.