Building normal ECG models to detect any arrhythmias using deep learning

Abstract

In the detection of arrhythmia using deep learning, most methods train neural networks to categorize input electrocardiogram (ECG) signals into typical arrhythmia signals using annotated training ECG data. Such methods can neither detect unknown arrhythmias nor explain why the signals are considered as arrhythmic. To detect arrhythmia automatically, this study proposes a method that learns normal ECG signals and can explain the assessment for the signals clearly. Our method builds a model of normal ECG signals using a convolutional neural network and long short-term memory. The model inputs normal ECG signals and performs training to predict the succeeding normal ECG signals. If an abnormal ECG signal is provided, the model predicts the succeeding ECG signal far from the actual ECG signal and can assess whether the input is abnormal. This means that our method can judge any arrhythmia because it requires no prior knowledge regarding the annotations. Experimental results confirm that the proposed method can detect arrhythmias appropriately without learning them. Furthermore, we propose a GUI that can display the location where abnormality is suspected to the user.