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Arrhythmias, Cardiac

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Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model.

Sensors (Basel, Switzerland)
An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various heart-related conditions in patients. An automated diagnostic tool is required to detect significant episodes in long-term ECG records. It is a very cha...

Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals.

International journal of environmental research and public health
Cardiovascular diseases, like arrhythmia, as the leading causes of death in the world, can be automatically diagnosed using an electrocardiogram (ECG). The ECG-based diagnostic has notably resulted in reducing human errors. The main aim of this study...

A review of arrhythmia detection based on electrocardiogram with artificial intelligence.

Expert review of medical devices
INTRODUCTION: With the widespread availability of portable electrocardiogram (ECG) devices, there will be a surge in ECG diagnoses. Traditional computer-aided diagnosis of arrhythmia mainly relies on the rules of medical knowledge, which are insuffic...

Visualization deep learning model for automatic arrhythmias classification.

Physiological measurement
With the improvement of living standards, heart disease has become one of the common diseases that threaten human health. Electrocardiography (ECG) is an effective way of diagnosing cardiovascular diseases. With the rapid growth of ECG examinations a...

Multi-Scale Convolutional Neural Network Ensemble for Multi-Class Arrhythmia Classification.

IEEE journal of biomedical and health informatics
The automated analysis of electrocardiogram (ECG) signals plays a crucial role in the early diagnosis and management of cardiac arrhythmias. The diverse etiology of arrhythmia and the subtle variations in the pathological ECG characteristics pose cha...

Deep Learning-Based Data Augmentation and Model Fusion for Automatic Arrhythmia Identification and Classification Algorithms.

Computational intelligence and neuroscience
Automated ECG-based arrhythmia detection is critical for early cardiac disease prevention and diagnosis. Recently, deep learning algorithms have been widely applied for arrhythmia detection with great success. However, the lack of labeled ECG data an...

Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification.

Computational intelligence and neuroscience
The electrical activity produced during the heartbeat is measured and recorded by an ECG. Cardiologists can interpret the ECG machine's signals and determine the heart's health condition and related causes of ECG signal abnormalities. However, cardio...

ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches.

Computational intelligence and neuroscience
According to the World Health Organization (WHO) report, heart disease is spreading throughout the world very rapidly and the situation is becoming alarming in people aged 40 or above (Xu, 2020). Different methods and procedures are adopted to detect...

Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique.

Sensors (Basel, Switzerland)
Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely a...

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs.

Sensors (Basel, Switzerland)
The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to es...