Both were divided by 200 to calculate the corresponding lead value. Press, O. et al. Training the network using two time-frequency-moment features for each signal significantly improves the classification performance and also decreases the training time. Kingma, D. P. et al. 54, No. Vol. GitHub is where people build software. We build up two layers of bidirectional long short-term memory (BiLSTM) networks12, which has the advantage of selectively retaining the history information and current information. However, most of these methods require large amounts of labeled data for training the model, which is an empirical problem that still needs to be solved. Kampouraki, A., Manis, G. & Nikou, C. Heartbeat time series classification with support vector machines. The autoencoder and variational autoencoder (VAE) are generative models proposed before GAN. The data consists of a set of ECG signals sampled at 300 Hz and divided by a group of experts into four different classes: Normal (N), AFib (A), Other Rhythm (O), and Noisy Recording (~). The length \(||d||\) of this sequence is computed by: where d represents the Euclidean distance. Journal of medical systems 36, 883892, https://doi.org/10.1007/s10916-010-9551-7 (2012). License. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals".