Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder
Fig 1
Model architecture of encoder in convolutional variational autoencoder (CVAE) consists of nine layers of one-dimensional convolutional neural network (CNN).
The filter size is 19 until the sixth residual block, after which it is reduced to nine. At the end of encoder, the extracted data are flattened and converted into the values of mean and standard deviation with a size of 60. The latent variable is sampled from the values of mean and standard deviation. The hyperparameter of the decoder is set symmetrically so that the output size is the same as that of the encoder. Average pooling is applied to the outputs of the decoder. The mean values were used as the CVAE feature vector of electrocardiogram (ECG).