AIMC Topic: Ventricular Fibrillation

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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...
Electrocardiography, Ambulatory Electric Countershock Ventricular Fibrillation Electrocardiography Support Vector Machine Signal Processing, Computer-Assisted Defibrillators Out-of-Hospital Cardiac Arrest Humans Neural Networks, Computer Diagnosis, Computer-Assisted Deep Learning Algorithms Databases, Factual
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Ventricular Fibrillation and Tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning.

Computer methods and programs in biomedicine Feb 10, 2017
BACKGROUND AND OBJECTIVE: To safely select the proper therapy for Ventricullar Fibrillation (VF) is essential to distinct it correctly from Ventricular Tachycardia (VT) and other rhythms. Provided that the required therapy would not be the same, an e...
Tachycardia Signal Processing, Computer-Assisted Datasets as Topic Ventricular Fibrillation Machine Learning Electrocardiography Support Vector Machine Humans Neural Networks, Computer
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A novel algorithm for ventricular arrhythmia classification using a fuzzy logic approach.

Australasian physical & engineering sciences in medicine Nov 4, 2016
In the present study, it has been shown that an unnecessary implantable cardioverter-defibrillator (ICD) shock is often delivered to patients with an ambiguous ECG rhythm in the overlap zone between ventricular tachycardia (VT) and ventricular fibril...
Heart Ventricles Arrhythmias, Cardiac Databases as Topic ROC Curve Humans Electrocardiography Tachycardia, Ventricular Algorithms Fuzzy Logic Ventricular Fibrillation
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Combining Amplitude Spectrum Area with Previous Shock Information Using Neural Networks Improves Prediction Performance of Defibrillation Outcome for Subsequent Shocks in Out-Of-Hospital Cardiac Arrest Patients.

PloS one Feb 10, 2016
OBJECTIVE: Quantitative ventricular fibrillation (VF) waveform analysis is a potentially powerful tool to optimize defibrillation. However, whether combining VF features with additional attributes that related to the previous shock could enhance the ...
Defibrillators Out-of-Hospital Cardiac Arrest Signal Processing, Computer-Assisted Electric Countershock Ventricular Fibrillation Humans Electrocardiography Neural Networks, Computer Treatment Outcome Retrospective Studies Software Predictive Value of Tests Sensitivity and Specificity Area Under Curve ROC Curve
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Detection of Shockable Ventricular Arrhythmia using Variational Mode Decomposition.

Journal of medical systems Jan 21, 2016
Ventricular tachycardia (VT) and ventricular fibrillation (VF) are shockable ventricular cardiac ailments. Detection of VT/VF is one of the important step in both automated external defibrillator (AED) and implantable cardioverter defibrillator (ICD)...
Machine Learning Algorithms Sensitivity and Specificity Humans Image Processing, Computer-Assisted Defibrillators Tachycardia, Ventricular Electrocardiography Ventricular Fibrillation
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Effects of Shen-Fu injection on coagulation-fibrinolysis disorders in a porcine model of cardiac arrest.

The American journal of emergency medicine Dec 12, 2015
OBJECTIVE: The objective of the study is to investigate the effects of Shen-Fu injection (SFI) on coagulation-fibrinolysis disorders in a porcine model of cardiac arrest.
Blood Coagulation Disorders Epinephrine Phytotherapy Heart Arrest Fibrinolysis Resuscitation Disease Models, Animal Cardiovascular Agents Drugs, Chinese Herbal Swine, Miniature Animals Injections Ventricular Fibrillation Swine China
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A Hybrid GCN-LSTM Model for Ventricular Arrhythmia Classification Based on ECG Pattern Similarity.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Accurate differentiation between Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) is essential in the field of cardiology. Recent advancements in deep learning have facilitated automated arrhythmia recognition, surpassing traditional el...
Humans Signal Processing, Computer-Assisted Arrhythmias, Cardiac Tachycardia, Ventricular Ventricular Fibrillation Neural Networks, Computer Algorithms Electrocardiography
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The Feasibility of Arrhythmias Detection from A Capacitive ECG Measurement Using Convolutional Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Capacitive ECG (cECG) can measure the cardiac electrical signal via capacitive coupling between electrodes and skin. This unconstrained measurement is suitable for personal heart monitoring; however, the instability in the quality of the signal hinde...
Tachycardia, Ventricular Ventricular Fibrillation Neural Networks, Computer Electrocardiography Feasibility Studies Humans
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Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology Feb 1, 2019
AIMS: Electrical storm (ES) is a serious arrhythmic syndrome that is characterized by recurrent episodes of ventricular arrhythmias. Electrical storm is associated with increased mortality and morbidity despite the use of implantable cardioverter-def...
Machine Learning Databases, Factual Reproducibility of Results Risk Factors Humans Heart Failure Heart Rate Tachycardia, Ventricular Ventricular Fibrillation Defibrillators, Implantable Electric Countershock Predictive Value of Tests Remote Sensing Technology Signal Processing, Computer-Assisted Risk Assessment Time Factors Treatment Outcome
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