Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review
Background The electrocardiogram (ECG) is one of the most commonly used diagnostic
tools in medicine and healthcare. Deep learning methods have achieved promising results …
tools in medicine and healthcare. Deep learning methods have achieved promising results …
A survey on deep learning in medicine: Why, how and when?
New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …
data, clinical images, genome sequences, data on prescribed therapies and results …
[PDF][PDF] Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution.
Electrocardiogram (ECG) signal is a measure of the heart's electrical activity. Recently, ECG
detection and classification have benefited from the use of computer-aided systems by …
detection and classification have benefited from the use of computer-aided systems by …
Cardiac arrhythmia detection from 2d ecg images by using deep learning technique
Arrhythmia is irregular changes of normal heart rhythm and effective manual identifying of
them require a lot of time and depends on experience of clinicians. This paper proposes …
them require a lot of time and depends on experience of clinicians. This paper proposes …
ECG recurrence plot-based arrhythmia classification using two-dimensional deep residual CNN features
In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia
classification algorithm that can be implemented in portable devices is presented. Public …
classification algorithm that can be implemented in portable devices is presented. Public …
IM-ECG: An interpretable framework for arrhythmia detection using multi-lead ECG
R Tao, L Wang, Y Xiong, YR Zeng - Expert Systems with Applications, 2024 - Elsevier
Multi-lead electrocardiogram (ECG) is a fundamental and reliable diagnostic tool for the
detection of heart arrhythmias. An increasing number of deep neural network models have …
detection of heart arrhythmias. An increasing number of deep neural network models have …
An automatic premature ventricular contraction recognition system based on imbalanced dataset and pre-trained residual network using transfer learning on ECG …
The development of automatic monitoring and diagnosis systems for cardiac patients over
the internet has been facilitated by recent advancements in wearable sensor devices from …
the internet has been facilitated by recent advancements in wearable sensor devices from …
Multi-module recurrent convolutional neural network with transformer encoder for ECG arrhythmia classification
The automatic classification of electrocardiogram (ECG) signals has played an important
role in cardiovascular diseases diagnosis and prediction. Deep neural networks (DNNs) …
role in cardiovascular diseases diagnosis and prediction. Deep neural networks (DNNs) …
Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet
Introduction: Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically
identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood …
identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood …
[PDF][PDF] Integrated CWT-CNN for epilepsy detection using multiclass EEG dataset
Electroencephalography is a common clinical procedure to record brain signals generated
by human activity. EEGs are useful in Brain controlled interfaces and other intelligent …
by human activity. EEGs are useful in Brain controlled interfaces and other intelligent …