… In this study, a novel hybrid approach of deep neural network combined with linear and
nonlinear features extracted from ECG and heart rate variability (HRV) is proposed for ECG multi-…

… A deep learning approach is presented in this study to automatically learning and classifying
the 10 class of ECG heartbeats, which is important for the diagnosis of cardiac arrhythmia. …

… normal and abnormal ECG signals. A multi-channel multi-scale deep neural network (DNN) …
ECG signals without any feature extraction. Convolutional layers are used to extract primary …

… that a review in studies of deep learning method applied in ECG diagnosis is necessary. In
… and their characteristics are highlighted. Meanwhile, an overview of deep learning on ECG …

… In this introduced system, the MOWPT gives a comprehensive time scale paving pattern and
possesses time invariance features that are employed for decomposing the real ECG signal …

… -art machine and deep learning based CAAC expert systems for shockable ECG signal
recognition, … In this work, 23 nonlinear features were extracted from ECG signals and fed to SVM …

… In any case, the main goal is to present and use some machine learning algorithm without
any feature engineered procedure and competing classification accuracy in comparison to …

… system using a new comprehensive ECG database inter-patient paradigm … any features
extraction. We investigated four supervised machine learning models: support vector machine (…

… Automated ECG interpretation is the use of machine learning and … diagnosis of ECG [6] to
improve the correct interpretation of ECG [7]. … this group will use deep learning for both feature …

… into another deep learning model for classification. First, a hybrid model for multiple ECG …
We experimented with a robust hybrid deep learning model for the ECG classification tasks, …