AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Arrhythmias, Cardiac

Showing 141 to 150 of 272 articles

Clear Filters

Automated ECG classification based on 1D deep learning network.

Methods (San Diego, Calif.)
The standard 12-lead electrocardiogram (ECG) records the heart's electrical activity from electrodes on the skin, and is widely used in screening and diagnosis of the cardiac conditions due to its low price and non-invasive characteristics. Manual ex...

KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement.

Journal of healthcare engineering
Acquiring electrocardiographic (ECG) signals and performing arrhythmia classification in mobile device scenarios have the advantages of short response time, almost no network bandwidth consumption, and human resource savings. In recent years, deep ne...

Interpretable heartbeat classification using local model-agnostic explanations on ECGs.

Computers in biology and medicine
Treatment and prevention of cardiovascular diseases often rely on Electrocardiogram (ECG) interpretation. Dependent on the physician's variability, ECG interpretation is subjective and prone to errors. Machine learning models are often developed and ...

Comparing performance of iterative and non-iterative algorithms on various feature schemes for arrhythmia analysis.

Methods (San Diego, Calif.)
To evaluate the performance of the classic machine learning algorithms and the effectiveness of various features, the iterative algorithms (i.e., support vector machine (SVM), and least-squares SVM (LS-SVM)) and non-iterative algorithms (i.e., random...

A New ECG Denoising Framework Using Generative Adversarial Network.

IEEE/ACM transactions on computational biology and bioinformatics
This paper presents a novel Electrocardiogram (ECG) denoising approach based on the generative adversarial network (GAN). Noise is often associated with the ECG signal recording process. Denoising is central to most of the ECG signal processing tasks...

CEFEs: A CNN Explainable Framework for ECG Signals.

Artificial intelligence in medicine
In the healthcare domain, trust, confidence, and functional understanding are critical for decision support systems, therefore, presenting challenges in the prevalent use of black-box deep learning (DL) models. With recent advances in deep learning m...

Hybrid Prediction Method for ECG Signals Based on VMD, PSR, and RBF Neural Network.

BioMed research international
To explore a method to predict ECG signals in body area networks (BANs), we propose a hybrid prediction method for ECG signals in this paper. The proposed method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a r...

Machine Learning in Arrhythmia and Electrophysiology.

Circulation research
Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a si...

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal.

Sensors (Basel, Switzerland)
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal ...

Smartwatch Electrocardiogram and Artificial Intelligence for Assessing Cardiac-Rhythm Safety of Drug Therapy in the COVID-19 Pandemic. The QT-logs study.

International journal of cardiology
BACKGROUND: QTc interval monitoring, for the prevention of drug-induced arrhythmias is necessary, especially in the context of coronavirus disease 2019 (COVID-19). For the provision of widespread use, surrogates for 12‑lead ECG QTc assessment may be ...