AIMC Topic: Ventricular Fibrillation
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Ventricular Arrhythmia Classification Using Similarity Maps and Hierarchical Multi-Stream Deep Learning.
IEEE transactions on bio-medical engineering
Feb 20, 2025
OBJECTIVE: Ventricular arrhythmias are the primary arrhythmias that cause sudden cardiac death. We address the problem of classification between ventricular tachycardia (VT), ventricular fibrillation (VF) and non-ventricular rhythms (NVR).
Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert-Huang and wavelet transforms with explainable vision transformer and CNN models.
Computer methods and programs in biomedicine
Oct 11, 2024
BACKGROUND AND OBJECTIVE: Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. H...
Development of artificial intelligence-driven biosignal-sensitive cardiopulmonary resuscitation robot.
Resuscitation
Aug 8, 2024
AIM OF THE STUDY: We evaluated whether an artificial intelligence (AI)-driven robot cardiopulmonary resuscitation (CPR) could improve hemodynamic parameters and clinical outcomes.
Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators.
Heart rhythm
May 24, 2024
BACKGROUND: Despite effectiveness of the implantable cardioverter-defibrillator (ICD) in saving patients with life-threatening ventricular arrhythmias (VAs), the temporal occurrence of VA after ICD implantation is unpredictable.
Deep Learning Strategy for Sliding ECG Analysis during Cardiopulmonary Resuscitation: Influence of the Hands-Off Time on Accuracy.
Sensors (Basel, Switzerland)
May 5, 2023
This study aims to present a novel deep learning algorithm for a sliding shock advisory decision during cardiopulmonary resuscitation (CPR) and its performance evaluation as a function of the cumulative hands-off time. We retrospectively used 13,570 ...
Prediction of the Presence of Ventricular Fibrillation From a Brugada Electrocardiogram Using Artificial Intelligence.
Circulation journal : official journal of the Japanese Circulation Society
Nov 12, 2022
BACKGROUND: Brugada syndrome is a potential cause of sudden cardiac death (SCD) and is characterized by a distinct ECG, but not all patients with A Brugada ECG develop SCD. In this study we sought to examine if an artificial intelligence (AI) model c...
LDIAED: A lightweight deep learning algorithm implementable on automated external defibrillators.
PloS one
Feb 25, 2022
Differentiating between shockable and non-shockable Electrocardiogram (ECG) signals would increase the success of resuscitation by the Automated External Defibrillators (AED). In this study, a Deep Neural Network (DNN) algorithm is used to distinguis...
Prediction of cerebral perfusion pressure during CPR using electroencephalogram in a swine model of ventricular fibrillation.
The American journal of emergency medicine
Feb 25, 2021
BACKGROUND: Measuring the quality of cardiopulmonary resuscitation (CPR) is important for improving outcomes in cardiac arrest. Cerebral perfusion pressure (CePP) could represent cerebral circulation during CPR, but it is difficult to measure non-inv...
Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death.
Circulation research
Nov 10, 2020
RATIONALE: Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside.
Death, Sudden, Cardiac
Risk Factors
Tachycardia, Ventricular
Electrophysiologic Techniques, Cardiac
Aged
Cardiomyopathies
Aged, 80 and over
Diagnosis, Computer-Assisted
Prospective Studies
Ventricular Fibrillation
Neural Networks, Computer
Time Factors
Phenotype
Predictive Value of Tests
Prognosis
Support Vector Machine
Humans
Signal Processing, Computer-Assisted
Female
Action Potentials
Male
Myocardial Infarction
Middle Aged
Risk Assessment
Recurrent probabilistic neural network-based short-term prediction for acute hypotension and ventricular fibrillation.
Scientific reports
Jul 20, 2020
In this paper, we propose a novel method for predicting acute clinical deterioration triggered by hypotension, ventricular fibrillation, and an undiagnosed multiple disease condition using biological signals, such as heart rate, RR interval, and bloo...
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