[HTML][HTML] 1D convolutional neural networks and applications: A survey
During the last decade, Convolutional Neural Networks (CNNs) have become the de facto
standard for various Computer Vision and Machine Learning operations. CNNs are feed …
standard for various Computer Vision and Machine Learning operations. CNNs are feed …
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 …
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG
workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning …
workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning …
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of
epilepsy. The EEG signal contains information about the electrical activity of the brain …
epilepsy. The EEG signal contains information about the electrical activity of the brain …
A deep convolutional neural network model to classify heartbeats
The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart.
Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a …
Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is 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 …
Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
The electrocardiogram (ECG) is a useful diagnostic tool to diagnose various cardiovascular
diseases (CVDs) such as myocardial infarction (MI). The ECG records the heart's electrical …
diseases (CVDs) such as myocardial infarction (MI). The ECG records the heart's electrical …
[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 …
A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification
Ö Yildirim - Computers in biology and medicine, 2018 - Elsevier
Long-short term memory networks (LSTMs), which have recently emerged in sequential data
analysis, are the most widely used type of recurrent neural networks (RNNs) architecture …
analysis, are the most widely used type of recurrent neural networks (RNNs) architecture …
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 …