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Deep learning in ECG diagnosis: A review
X Liu, H Wang, Z Li, L Qin - Knowledge-Based Systems, 2021 - Elsevier
Cardiovascular disease (CVD) is a general term for a series of heart or blood vessels
abnormality that serves as a global leading reason for death. The earlier the abnormal heart …
abnormality that serves as a global leading reason for death. The earlier the abnormal heart …
[HTML][HTML] A review on deep learning methods for ECG arrhythmia classification
Deep Learning (DL) has recently become a topic of study in different applications including
healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a …
healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a …
Arrhythmia detection using deep convolutional neural network with long duration ECG signals
This article presents a new deep learning approach for cardiac arrhythmia (17 classes)
detection based on long-duration electrocardiography (ECG) signal analysis …
detection based on long-duration electrocardiography (ECG) signal analysis …
Deep learning for healthcare applications based on physiological signals: A review
Background and objective: We have cast the net into the ocean of knowledge to retrieve the
latest scientific research on deep learning methods for physiological signals. We found 53 …
latest scientific research on deep learning methods for physiological signals. We found 53 …
A deep learning approach for Parkinson's disease diagnosis from EEG signals
An automated detection system for Parkinson's disease (PD) employing the convolutional
neural network (CNN) is proposed in this study. PD is characterized by the gradual …
neural network (CNN) is proposed in this study. PD is characterized by the gradual …
A new approach for arrhythmia classification using deep coded features and LSTM networks
Background and objective For diagnosis of arrhythmic heart problems, electrocardiogram
(ECG) signals should be recorded and monitored. The long-term signal records obtained …
(ECG) signals should be recorded and monitored. The long-term signal records obtained …
[HTML][HTML] A hybrid deep learning approach for ECG-based arrhythmia classification
Arrhythmias are defined as irregularities in the heartbeat rhythm, which may infrequently
occur in a human's life. These arrhythmias may cause potentially fatal complications, which …
occur in a human's life. These arrhythmias may cause potentially fatal complications, which …
Automated EEG-based screening of depression using deep convolutional neural network
In recent years, advanced neurocomputing and machine learning techniques have been
used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In …
used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In …
Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images
Glaucoma progressively affects the optic nerve and may cause partial or complete vision
loss. Raised intravascular pressure is the only factor which can be modified to prevent …
loss. Raised intravascular pressure is the only factor which can be modified to prevent …
Arrhythmia classification of LSTM autoencoder based on time series anomaly detection
P Liu, X Sun, Y Han, Z He, W Zhang, C Wu - Biomedical Signal Processing …, 2022 - Elsevier
Electrocardiogram (ECG) is widely used in the diagnosis of heart disease because of its
noninvasiveness and simplicity. The time series signals contained in the signal are usually …
noninvasiveness and simplicity. The time series signals contained in the signal are usually …