… Thus, the detection and classification of arrhythmias is a pertinent … (2) Method: This paper
proposes a hybrid deep learning-based approach to automate the detection and classification …

… arrhythmia classification. To begin with, Khazaee and Zadeh, 2014 proposed an arrhythmia
classification … advantage of swarm intelligence and the machine learning approach [17]. The …

… hybrid model to enhance the learning method, features extraction, and analysing ECG signal
classification. … and the dilated convolution-based hybrid subsystem can extract meaningful …

… classify data more accurately. This paper has introduced a hybrid approach for deep learning
… of the classification of the EC GS signal data from the Arrhythmia Database, we proposed a …

… Basic Theory In this paper, three deep-learning approaches are utilized to form the
classification model. Residual network (ResNet) and Bi-LSTM network are applied in the …

… It is used for transient signals like ECG, often necessary for machine learning procedures
with excessive computer assets. For better classification accuracy, machine learning methods …

… An early and accurate detection of arrhythmias is essential reduce the mortality rate due to …
This article proposes a deep learning approach for automated detection of cardiac arrhythmia …

… [21] have tried to automatically identify a developing SCD in patients using machine learning
approaches on the arrhythmic risk markers using the intensively clinically demonstrated …

… Several researchers employed machine learning and deep learning methods, particularly
Convolutional Neural Networks (CNN) to accomplish this task. Despite the significant …

… This work presents a technique for classification among lethal CVDs like atrial fibrillation (Afib), …
Automatic classification of arrhythmia beats based on machine learning methods was …