Atrial fibrillation (AF) is a common abnormal heart rhythm disease. Therefore, the development of an AF detection system is of great significance to detect critical illnesses. In this paper, we proposed an automatic recognition method named CNN-LSTM to automatically detect the AF heartbeats based on deep learning. The model combines convolutional neural networks (CNN) to extract local correlation features and uses long short-term memory networks (LSTM) to capture the front-to-back dependencies of electrocardiogram (ECG) sequence data. The CNN-LSTM is feeded by processed data to automatically detect AF signals. Our study uses the MIT-BIH Atrial Fibrillation Database to verify the validity of the model. We achieved a high classification accuracy for the heartbeat data of the test set, with an overall classification accuracy rate of 97.21%, sensitivity of 97.34%, and specificity of 97.08%. The experimental results show that our model can robustly detect the onset of AF through ECG signals and achieve stable classification performance, thereby providing a suitable candidate for the automatic classification of AF.

中文翻译：

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使用CNN-LSTM模型的房颤自动系统

心房颤动（AF）是一种常见的异常心律疾病。因此，AF检测系统的开发对检测重大疾病具有重要意义。在本文中，我们提出了一种名为CNN-LSTM的自动识别方法，该方法可基于深度学习自动检测AF心跳。该模型结合了卷积神经网络（CNN）来提取局部相关特征，并使用长短期记忆网络（LSTM）来捕获心电图（ECG）序列数据的前后依存关系。CNN-LSTM由处理后的数据提供以自动检测AF信号。我们的研究使用MIT-BIH心房颤动数据库来验证该模型的有效性。我们对测试集的心跳数据实现了较高的分类精度，总体分类准确率为97.21％，敏感性为97.34％，特异性为97.08％。实验结果表明，我们的模型能够通过ECG信号可靠地检测AF的发作，并实现稳定的分类性能，从而为AF的自动分类提供合适的候选对象。