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Defibrillators

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Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators.

PloS one
Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survival of out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrillators (AED). AED algorithms for VF-detection are customarily...

Detection of Shockable Ventricular Arrhythmia using Variational Mode Decomposition.

Journal of medical systems
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)...

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia.

PloS one
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...

A Robust Machine Learning Architecture for a Reliable ECG Rhythm Analysis during CPR.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Chest compressions delivered during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may make the shock advice algorithms (SAA) of defibrillators inaccurate. There is evidence that methods consisting of adaptive filters that remov...

Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation.

Journal of the American Heart Association
Background Because chest compressions induce artifacts in the ECG, current automated external defibrillators instruct the user to stop cardiopulmonary resuscitation (CPR) while an automated rhythm analysis is performed. It has been shown that minimiz...

LDIAED: A lightweight deep learning algorithm implementable on automated external defibrillators.

PloS one
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...

A novel method for conformity assessment testing of defibrillators for post-market surveillance purposes.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Defibrillators are medical devices (MDs) used in the most critical situations, hence their performance must be ensured at all times. This requires defibrillators to be subjected to regular performance assessments after they have been plac...

A Deep-Learning-Based CPR Action Standardization Method.

Sensors (Basel, Switzerland)
In emergency situations, ensuring standardized cardiopulmonary resuscitation (CPR) actions is crucial. However, current automated external defibrillators (AEDs) lack methods to determine whether CPR actions are performed correctly, leading to inconsi...

Machine learning for improved medical device management: A focus on defibrillator performance.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundPoorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clinical effectiveness and efficiency of patient diagnosis and treatment. After the MD directive (MDD) h...