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).
Table 1.
Baseline characteristics of each dataset.
Fig 2.
Visualization of (a) t-Stochastic Neighbor Embedding (t-SNE) and (b) Locally Linear Embedding (LLE) in two-dimensional space for normal sinus rhythms and arrhythmias. Normal sinus rhythms are shown as blue clusters while arrhythmias are shown as red clusters. It shows that feature vectors can represent the differences between normal and arrhythmic rhythms although the model was trained without any label. Abbreviations: SR: normal sinus rhythm; ST: sinus tachycardia; SVT: supraventricular tachycardia; AFIB: atrial fibrillation.
Fig 3.
Visualization of (a) t-SNE and (b) LLE in two-dimensional space for ECG from ICU and Healthy test. ECGs from ICU are shown as blue clusters while ECGs from medical-checkup are shown as red clusters. It shows that feature vectors can represent the differences between healthy and unhealthy people although the model was trained without any label.
Fig 4.
Reconstruction output and feature space exploration.
(a) Examples of the reconstruction output of a normal electrocardiogram (ECG) are shown. Original ECG data (blue) and reconstructed ECG data (yellow) are not significantly different. (b) Examples of latent space exploration. A single blue line represents the reconstruction output of the sampled origin feature vector in the first ECG. In the remaining three ECGs, the blue line represents the reconstruction output of the feature vector that subtracts 2 from the Nth feature variable. The yellow lines represent the reconstruction output of the feature vector that adds 2 to the Nth feature variable.
Table 2.
Anomaly detection in the Shaoxing and MIT-BIH datasets.
Fig 5.
Distribution of anomality score in 1,000 patients.
The distribution of the anomality score is skewed to the left, which is consistent with the premise that the number of normal ECGs is more than the number of abnormal ECGs. ECGs with an anomality score over 1.3 were due to noise or movement artifacts. The majority of ECGs with an anomality score between 0.45–1.3 were abnormal.
Table 3.
Transfer learning using CVAE features and anomality features.
Table 4.
Transfer learning vs. random initialization using the bootstrap validation method.