AIMC Topic: Arrhythmias, Cardiac

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GeFeS: A generalized wrapper feature selection approach for optimizing classification performance.

Computers in biology and medicine Aug 20, 2020
In this paper, we propose a generalized wrapper-based feature selection, called GeFeS, which is based on a parallel new intelligent genetic algorithm (GA). The proposed GeFeS works properly under different numerical dataset dimensions and sizes, care...
Humans Machine Learning Algorithms Arrhythmias, Cardiac
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Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

Circulation. Arrhythmia and electrophysiology Jul 6, 2020
Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are...
Electrocardiography Humans Action Potentials Artificial Intelligence Reproducibility of Results Predictive Value of Tests Heart Conduction System Diagnosis, Computer-Assisted Signal Processing, Computer-Assisted Arrhythmias, Cardiac Machine Learning Deep Learning Heart Rate Prognosis Electrophysiologic Techniques, Cardiac
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Missing Value Estimation Methods Research for Arrhythmia Classification Using the Modified Kernel Difference-Weighted KNN Algorithms.

BioMed research international Jun 21, 2020
Electrocardiogram (ECG) signal is critical to the classification of cardiac arrhythmia using some machine learning methods. In practice, the ECG datasets are usually with multiple missing values due to faults or distortion. Unfortunately, many establ...
Electrocardiography Humans Arrhythmias, Cardiac Machine Learning Electronic Data Processing Computational Biology Models, Cardiovascular
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Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms.

Sensors (Basel, Switzerland) May 19, 2020
Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to self-extract significant features of the electrocardiogram (ECG) and can generally provide high-output diagnostic accuracy if subjected to robust train...
Arrhythmias, Cardiac Neural Networks, Computer Machine Learning Algorithms Electrocardiography Humans
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A Shifting Paradigm in Robotic Heart Surgery: From Single-Procedure Approach to Establishing a Robotic Heart Center of Excellence.

Innovations (Philadelphia, Pa.) May 8, 2020
Robotics Minimally Invasive Surgical Procedures Robotic Surgical Procedures Cardiac Surgical Procedures Humans Endoscopy Aortic Valve Atrial Fibrillation Mitral Valve Cardiomyopathy, Hypertrophic History, 21st Century Coronary Artery Bypass, Off-Pump Cardiac Resynchronization Therapy Arrhythmias, Cardiac Nobel Prize Myocardial Revascularization Myocardial Bridging Congenital Abnormalities Pericardiectomy Tricuspid Valve Coronary Artery Bypass Lead Poisoning History, 20th Century Patient Positioning
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Application of a convolutional neural network for predicting the occurrence of ventricular tachyarrhythmia using heart rate variability features.

Scientific reports Apr 21, 2020
Predicting the occurrence of ventricular tachyarrhythmia (VTA) in advance is a matter of utmost importance for saving the lives of cardiac arrhythmia patients. Machine learning algorithms have been used to predict the occurrence of imminent VTA. In t...
Heart Rate Tachycardia, Ventricular Prognosis Female Aged Adult Humans Neural Networks, Computer Machine Learning Male Algorithms Middle Aged Support Vector Machine Arrhythmias, Cardiac
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Deep Multi-Scale Fusion Neural Network for Multi-Class Arrhythmia Detection.

IEEE journal of biomedical and health informatics Apr 13, 2020
Automated electrocardiogram (ECG) analysis for arrhythmia detection plays a critical role in early prevention and diagnosis of cardiovascular diseases. Extracting powerful features from raw ECG signals for fine-grained diseases classification is stil...
Humans Neural Networks, Computer Algorithms Electrocardiography Arrhythmias, Cardiac Heart Rate
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Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review.

Computers in biology and medicine Apr 8, 2020
Deep learning models have become a popular mode to classify electrocardiogram (ECG) data. Investigators have used a variety of deep learning techniques for this application. Herein, a detailed examination of deep learning methods for ECG arrhythmia d...
Humans Deep Learning Electrocardiography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Heart Rate
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Deep Learning Applied to Electrocardiogram Interpretation.

The Canadian journal of cardiology Apr 2, 2020
Machine Learning Deep Learning Electrocardiography Arrhythmias, Cardiac Humans
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Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients With Ischemic Cardiomyopathy.

Circulation. Arrhythmia and electrophysiology Mar 18, 2020
BACKGROUND: Transition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhythmias (VAs). We sought to assess the utility of a novel machine learning approach for quantifying 3-...
Fourier Analysis Gadolinium DTPA Death, Sudden, Cardiac Heart Rate Humans Myocardial Ischemia Middle Aged Machine Learning Action Potentials Cardiomyopathies Risk Assessment Prognosis Predictive Value of Tests Male Diagnosis, Computer-Assisted Retrospective Studies Registries Ventricular Function, Left United States Imaging, Three-Dimensional Female Arrhythmias, Cardiac Stroke Volume Contrast Media Risk Factors Magnetic Resonance Imaging Aged
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