In this paper, we propose a novel approach based on deep belief networks for features
learning of ECG arrhythmias. The method consists of four steps: ECG signals preprocessing …

In this paper, a novel approach based on deep belief networks (DBN) for electrocardiograph
(ECG) arrhythmias classification is proposed. The construction process of ECG classification …

An electrocardiogram (ECG) is a biomedical signal type that determines the normality and
abnormality of heart beats using the electrical activity of the heart and has a great …

This paper introduces an electrocardiogram beat classification method based on deep belief
networks. This method includes two parts: feature extraction and classification. In the feature …

Recently, with the obvious increasing number of cardiovascular disease, the automatic
classification research of Electrocardiogram signals (ECG) has been playing a significantly …

Feature extraction plays an important role in arrhythmia classification, and successful
arrhythmia classification generally depends on ECG feature extraction. This paper proposed …

This paper introduces a novel deep learning-based algorithm that integrates a long short-
term memory (LSTM)-based auto-encoder (AE) network with support vector machine (SVM) …

Due to the recent advances in the area of deep learning, it has been demonstrated that a
deep neural network, trained on a huge amount of data, can recognize cardiac arrhythmias …

Heart arrhythmia is a fairly common medical condition, in which abnormal electrical activity
occurs in the heart. However, it can be life threatening if left untreated or undiagnosed. This …

In this paper, we propose a novel patient-specific electrocardiogram (ECG) classification
algorithm based on the recurrent neural networks (RNN) and density based clustering …