Comparison of variational autoencoders for anomaly detection of electrocardiogram

Electrocardiogram is widely used as a tool for detecting and diagnosing heart disease. Previous research revealed that deep learning models based on supervised learning have limited performance due to imbalance between normal and anomaly. To overcome this problem, anomaly detection models using variational autoencoder (VAE) have been proposed. After training VAE model using normal data, a threshold to distinguish normal and anomaly is set by using validation data, then test data is applied. VAE model has encoder, a latent variable layer, and decoder. and deep learning models are embedded in encoder and decoder. In this paper, VAE models embedding MLP, RNN, and LSTM are compared under various settings which are the type of data (original data or the first-order differential data) and the assumption for the variance of decoder. As results of analysis, VAE model based on LSTM is performed best when original data is used and a deep learning model for the variance of decoder is embedded. Additionally, the performance of VAE models using original data were better than models using first-order differential data.