Long short-term memory. Unpaired image-to-image translation using cycle-consistent adversarial networks. At each stage, the value of the loss function of the GAN was always much smaller than the losses of the other models obviously. Chen, X. et al. Figure1 illustrates the architecture of GAN. The procedure uses oversampling to avoid the classification bias that occurs when one tries to detect abnormal conditions in populations composed mainly of healthy patients. A dropout layer is combined with a fully connected layer. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals". In contrast to the encoder, the output and hidden state of the decoder at the current time depend on the output at the current time and the hidden state of the decoder at the previous time as well ason the latent code d. The goal of RNN-AE is to make the raw data and output for the decoder as similar as possible. Table3 demonstrated that the ECGs obtained using our model were very similar to the standard ECGs in terms of their morphology. Manual review of the discordances revealed that the DNN misclassifications overall appear very reasonable. Computerized extraction of electrocardiograms from continuous 12 lead holter recordings reduces measurement variability in a thorough QT study. A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification. https://doi.org/10.1038/s41598-019-42516-z, DOI: https://doi.org/10.1038/s41598-019-42516-z. As an effective method, Electrocardiogram (ECG) tests, which provide a diagnostic technique for recording the electrophysiological activity of the heart over time through the chest cavity via electrodes placed on the skin2, have been used to help doctors diagnose heart diseases. Labels is a categorical array that holds the corresponding ground-truth labels of the signals. An overall view of the algorithm is shown in Fig. Finally, we used the models obtained after training to generate ECGs by employing the GAN with the CNN, MLP, LSTM, and GRU as discriminators. Scientific Reports (Sci Rep) With pairs of convolution-pooling operations, we get the output size as 5*10*1. In classification problems, confusion matrices are used to visualize the performance of a classifier on a set of data for which the true values are known. 14th International Workshop on Content-Based Multimedia Indexing (CBMI). An initial attempt to train the LSTM network using raw data gives substandard results. puallee/Online-dictionary-learning 5: where N is the number of points, which is 3120 points for each sequencein our study, and and represent the set of parameters.