Optimizing outcomes after out-of-hospital cardiac arrest with innovative approaches to public-access defibrillation: a scientific statement from the International Liaison …
Out-of-hospital cardiac arrest is a global public health issue experienced by≈ 3.8 million
people annually. Only 8% to 12% survive to hospital discharge. Early defibrillation of …
people annually. Only 8% to 12% survive to hospital discharge. Early defibrillation of …
Artificial intelligence in the diagnosis and management of arrhythmias
VD Nagarajan, SL Lee, JL Robertus… - European heart …, 2021 - academic.oup.com
The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI)
methodologies for decades. Recent renewed interest in deep learning techniques has …
methodologies for decades. Recent renewed interest in deep learning techniques has …
Applications of artificial intelligence in cardiology. The future is already here
PI Dorado-Díaz, J Sampedro-Gómez… - Revista Española de …, 2019 - Elsevier
There is currently no other hot topic like the ability of current technology to develop
capabilities similar to those of human beings, even in medicine. This ability to simulate the …
capabilities similar to those of human beings, even in medicine. This ability to simulate the …
Automated detection of shockable ECG signals: A review
Sudden cardiac death from lethal arrhythmia is a preventable cause of death. Ventricular
fibrillation and tachycardia are shockable electrocardiographic (ECG) rhythms that can …
fibrillation and tachycardia are shockable electrocardiographic (ECG) rhythms that can …
Deep neural networks for ECG-based pulse detection during out-of-hospital cardiac arrest
The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary
for the early recognition of the arrest and the detection of return of spontaneous circulation …
for the early recognition of the arrest and the detection of return of spontaneous circulation …
ECG-based classification of resuscitation cardiac rhythms for retrospective data analysis
Objective: There is a need to monitor the heart rhythm in resuscitation to improve treatment
quality. Resuscitation rhythms are categorized into: ventricular tachycardia (VT), ventricular …
quality. Resuscitation rhythms are categorized into: ventricular tachycardia (VT), ventricular …
Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia
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 …
of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning …
Artificial intelligence and machine learning applications in sudden cardiac arrest prediction and management: a comprehensive review
Abstract Purpose of Review This literature review aims to provide a comprehensive
overview of the recent advances in prediction models and the deployment of AI and ML in …
overview of the recent advances in prediction models and the deployment of AI and ML in …
[HTML][HTML] Cancelable ECG biometric based on combination of deep transfer learning with DNA and amino acid approaches for human authentication
Recently, electrocardiogram (ECG) signals have received a high level of attention as a
physiological signal in the field of biometrics. It has presented great possibilities for its …
physiological signal in the field of biometrics. It has presented great possibilities for its …
Fully convolutional deep neural networks with optimized hyperparameters for detection of shockable and non-shockable rhythms
V Krasteva, S Ménétré, JP Didon, I Jekova - Sensors, 2020 - mdpi.com
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 …
learned to self-extract significant features of the electrocardiogram (ECG) and can generally …