###### Abstract  
A trial fibrillation (AF) is an arrhythmia that may cause blood clots and increase the risk of stroke and heart failure. Traditional 12-lead electrocardiogram (ECG) acquisition equipment is complex and difficult to carry. Short single-lead ECG recordings based on wearable devices can remedy these shortcomings. However, reliable and accurate atrial fibrillation detection is still an issue because of the limited information on the short single-lead ECG recordings. In this paper, we propose a novel multi-branch convolutional neural network and bidirectional long short-term memory network (MCNN-BLSTM) to deal with the reliability and accuracy of AF detection in short single-lead ECG recordings. Firstly, to fuller extract the feature information of short single-lead ECG recordings, the MCNN module is designed to dynamically set several corresponding branches according to the number of slices of short single-lead ECG recordings. Then, the BLSTM module is designed to further enhance the feature information learned from each branch. We validated the model on the PhysioNet/Cinc Challenge 2017 (Cinc2017) database and verified the generalization of the model on the China Physiological Signal Challenge 2018 (CPSC2018) database. The results show that the accuracy of the model on the CinC 2017 database reaches 87.57%, and the average F1 score reaches 84.56%. The accuracy of the model on the CPSC 2018 database reaches 87.50%, and the average F1 score reaches 82.01%. Compared with other advanced methods, our model shows better performance and can meet the daily needs of atrial fibrillation detection with short ECG wearable devices. deep learning; electrocardiogram; convolutional neural network; atrial fibrillation; long short-term memory +
Footnote †: journal: _arxiv_  
Wearable devices provide a cheaper and more convenient solution for AF detection [5]. For short single-lead ECG recordings, signal processing methods are particularly important due to different signal lengths. [PERSON] et al. [6] used the clipping and filling method to process ECG recordings of various sizes into fixed-length sequences. However, this method may lead to the loss of information in the input samples to the extent that the samples cannot be fully utilized. [PERSON] et al. [7] applied the consistent-sized electrocardiogram (CS-ECM) method to processing short-term ECG signals, avoiding the segmentation of short single-lead ECG recordings. However, such a method had information redundancy for short single-lead ECG recordings, and the redundant information did not provide more feature information for the model. [PERSON] et al. [8] extracted the time-frequency spectrum and Poincare plot of ECG signals, respectively. It avoided the segmentation processing of short single-lead ECG recordings. However, such a signal pretreatment operation was too complicated, reducing the model's real-time performance. Although there are many processing methods for short single-lead ECG recordings, choosing an appropriate method that can fully use the limited dynamic ECG information is still a problem. The main features of the AF signal are the irregular R-R intervals (when atrioventricular conduction is not impaired), the absence of distinct repeating P waves, and irregular atrial activations [1; 9]. In the early studies, most of the machine learning (ML) methods were used to achieve AF detection. [PERSON] et al. [10] proposed a hybrid Taguchi-genetic algorithm to decompose ECG signals and obtain p-wave amplitude features for AF detection. Compared with p-wave features, the QRS complex has a higher amplitude and is easier to locate. Therefore, the method based on RR interval features is widely used in AF detection. [PERSON] et al. [11] used the time domain, frequency domain, and distribution feature of RR interval to achieve AF classification of short single-lead ECG recordings. [PERSON] et al. [12] detected RR features of AF by measuring signal entropy.