AIMC Topic: Electrocardiography, Ambulatory

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Novel application of convolutional neural networks for artificial intelligence-enabled modified moving average analysis of P-, R-, and T-wave alternans for detection of risk for atrial and ventricular arrhythmias.

Journal of electrocardiology Dec 26, 2023
BACKGROUND: T-wave alternans (TWA) analysis was shown in >14,000 individuals studied worldwide over the past two decades to be a useful tool to assess risk for cardiovascular mortality and sudden arrhythmic death. TWA analysis by the modified moving ...
Electrocardiography Electrocardiography, Ambulatory Arrhythmias, Cardiac Heart Atria Humans Neural Networks, Computer Artificial Intelligence Reproducibility of Results
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Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks.

IEEE transactions on neural networks and learning systems Oct 27, 2023
Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acquired from mobile electrocardiogram (ECG) sensors, such as Holter monit...
China Electrocardiography Electrocardiography, Ambulatory Linear Models Neural Networks, Computer
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Deepaware: A hybrid deep learning and context-aware heuristics-based model for atrial fibrillation detection.

Computer methods and programs in biomedicine May 19, 2022
BACKGROUND: State-of-the-art automatic atrial fibrillation (AF) detection models trained on RR-interval (RRI) features generally produce high performance on standard benchmark electrocardiogram (ECG) AF datasets. These models, however, result in a si...
Electrocardiography, Ambulatory Heuristics Atrial Fibrillation Humans Algorithms Deep Learning Electrocardiography
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Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models.

Circulation. Arrhythmia and electrophysiology Jan 28, 2022
BACKGROUND: Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while ...
Risk Factors Risk Assessment Time Factors Treatment Outcome Humans Machine Learning Atrial Function, Left Models, Cardiovascular Patient-Specific Modeling Atrial Remodeling Atrial Fibrillation Heart Rate Magnetic Resonance Imaging Catheter Ablation Action Potentials Recurrence Electrocardiography, Ambulatory Fibrosis
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Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition.

Sensors (Basel, Switzerland) Aug 18, 2021
One of the most powerful techniques to diagnose cardiovascular diseases is to analyze the electrocardiogram (ECG). To increase diagnostic sensitivity, the ECG might need to be acquired using an ambulatory system, as symptoms may occur during a patien...
Humans Neural Networks, Computer Algorithms Electrocardiography Signal Processing, Computer-Assisted Electrocardiography, Ambulatory
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Artificial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Jul 22, 2021
OBJECTIVES: Embolic strokes of unknown source (ESUS) are common and often suspected to be caused by unrecognized paroxysmal atrial fibrillation (AF). An AI-enabled ECG (AI-ECG) during sinus rhythm has been shown to identify patients with unrecognized...
Electrocardiography, Ambulatory Atrial Fibrillation Heart Rate Embolic Stroke Male Middle Aged Risk Assessment Time Factors Action Potentials Predictive Value of Tests Signal Processing, Computer-Assisted Registries Risk Factors Hospitalization Aged Aged, 80 and over Humans Artificial Intelligence Female
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A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG.

Computational and mathematical methods in medicine Oct 20, 2019
Motion artifacts and myoelectrical noise are common issues complicating the collection and processing of dynamic electrocardiogram (ECG) signals. Recent signal quality studies have utilized a binary classification metric in which ECG samples are dete...
Arrhythmias, Cardiac Databases, Factual Heart Rate Female Reproducibility of Results Artifacts Aged Signal-To-Noise Ratio Humans Aged, 80 and over China Neural Networks, Computer Algorithms Adult Signal Processing, Computer-Assisted Male Electrocardiography, Ambulatory Middle Aged Motion
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Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia.

PloS one May 20, 2019
Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (V...
Diagnosis, Computer-Assisted Deep Learning Electrocardiography Support Vector Machine Signal Processing, Computer-Assisted Electrocardiography, Ambulatory Electric Countershock Ventricular Fibrillation Humans Neural Networks, Computer Algorithms Databases, Factual Defibrillators Out-of-Hospital Cardiac Arrest
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A new approach for arrhythmia classification using deep coded features and LSTM networks.

Computer methods and programs in biomedicine May 10, 2019
BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor hav...
Data Management Electrocardiography Principal Component Analysis Signal Processing, Computer-Assisted Electrocardiography, Ambulatory Software Arrhythmias, Cardiac Humans Algorithms Pattern Recognition, Automated Neural Networks, Computer Databases, Factual Data Compression Reproducibility of Results
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Predictors of atrial fibrillation early recurrence following cryoballoon ablation of pulmonary veins using statistical assessment and machine learning algorithms.

Heart and vessels Aug 23, 2018
Inflammation, oxidative stress, myocardial injury biomarkers and clinical parameters (longer AF duration, left atrial enlargement, the metabolic syndrome) are factors commonly related to AF recurrence. This study aims to assess the predictive value o...
Cryosurgery Middle Aged Time Factors Heart Conduction System Follow-Up Studies Humans Prognosis Atrial Fibrillation Electrocardiography, Ambulatory Machine Learning Retrospective Studies Recurrence Algorithms Female Pulmonary Veins Male
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