Classification of Arrhythmia Based on the Fusion of MobileNet and BiLSTM Models

ECG signal analysis plays a crucial role in the diagnosis of cardiovascular diseases. Aiming at the problem of ECG signal classification and diagnosis, we propose a classification method based on model fusion of MobileNetV3 and BiLSTM_Attention. Firstly, considering the high solving efficiency of lightweight neural network, MobileNetV3 is used to extract local features in the signal. Secondly, on the basis of extracting local features, the Bi-directional long and short term memory network (BiLSTM) is used to further extract the time sequence relationship of ECG signals. Finally, the attention mechanism is introduced to extract different weights for different features to enhance the classification ability of the model. Example analysis shows that, the classification accuracy of this algorithm is 98.46%, and each epoch takes 21.07 seconds on average, which has excellent comprehensive performance and potential clinical application value.