Arrhythmia Classification Based on CNN and Bidirectional LSTM

Abstract

Arrhythmia is one of the common diseases in cardiovascular diseases. Automatic classification of arrhythmia is of great significance to the diagnosis and treatment of cardiovascular diseases. The arrhythmia classification method based on one-dimensional ECG signals takes several beats as input and extracts features from the model for classification. Aiming at the problems of high preprocessing cost and failure to classify according to AAMI (Association for the Advancement of Medical Instrumentation) recommended standards, a method for automatic classification of arrhythmias based on original one-dimensional ECG signals and according to AAMI recommended standards is proposed. This method first uses the convolutional neural network (CNN) to learn the morphological characteristics of the ECG signals, then obtains the context dependence of the characteristics through the bidirectional long short-term memory (BLSTM) network, and finally uses the softmax function to complete the classification task. The mish function is used as the activation function to make the model more stable in training. The five-folds cross-validation is carried out on MIT-BIH, and the evaluation results achieve an average accuracy of 99.11%, indicating that the model can effectively extract the characteristics of the ECG signal and is suitable for the diagnosis of arrhythmia in the monitoring system.

Keywords

• ecg;
• arrhythmia;
• convolutional neural network (cnn);
• bidirectional long short-term memory (blstm) network