Abstract
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.
Highlights
Heart disease is the leading cause of human death, and the number of deaths due to cardiovascular diseases accounts for a large proportion of the total number of deaths worldwide [1]
Electrocardiogram (ECG) detection technology forms an important basis for atrial fibrillation (AF) diagnosis [10,11,12]. erefore, the application of automatic detection technology for diagnosing AF is necessary
We preprocess the ECG signal to train and evaluate the automatic AF prediction method based on the convolutional neural networks (CNN)-long short-term memory networks (LSTM) model
Summary
Heart disease is the leading cause of human death, and the number of deaths due to cardiovascular diseases accounts for a large proportion of the total number of deaths worldwide [1]. Most cardiovascular diseases are often accompanied by arrhythmia. There are more than 10 million people suffering from AF. A series of complications related to AF, such as stroke, heart failure, and other diseases, lead to high morbidity and mortality [5,6,7]. Machine learning has significantly contributed to the development of real-time monitoring of AF, and timely intervention in the effective detection of AF can avoid serious consequences caused by an exacerbation of the disease [13]
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