ArticleEntropy (Basel, Switzerland)2019
Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest.
Article in Entropy (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Artificial intelligence applications in out-of-hospital cardiac arrest: an analysis of emerging research trends.International journal of emergency medicine · 2026Review
- Detection of return of spontaneous circulation during cardiopulmonary resuscitation using continuous carotid artery Doppler blood flow monitored by AI in an animal model.Resuscitation plus · 2026Article
- Use of artificial intelligence in the out-of-hospital care settings: a scoping review.BMJ digital health & AI · 2026Article
- An enhanced deep learning framework for muscle artifact removal from ECG signal integrating resnet, GCAB, and BI-LSTM.Physical and engineering sciences in medicine · 2025Article
- Artificial intelligence in resuscitation: a scoping review.Resuscitation plus · 2025Review
- Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review.Prehospital and disaster medicine · 2024Article
- Seeking a Treatable Cause of Out-of-Hospital Cardiac Arrest during and after Resuscitation.Journal of clinical medicine · 2024Review
- Extracting physiologic and clinical data from defibrillators for research purposes to improve treatment for patients in cardiac arrest.Resuscitation plus · 2024Review
- Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level.NPJ digital medicine · 2024Article
- Artificial intelligence to support out-of-hospital cardiac arrest care: A scoping review.Resuscitation plus · 2023Article
- Artificial intelligence and machine learning in prehospital emergency care: A scoping review.iScience · 2023Article
- An Arrhythmia Classification Model Based on Vision Transformer with Deformable Attention.Micromachines · 2023Article
- A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification.Bioengineering (Basel, Switzerland) · 2022Article
- RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms.ACM journal on emerging technologies in computing systems · 2022Article
- A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal.BioMed research international · 2022Article
- Machine learning and feature engineering for predicting pulse presence during chest compressions.Royal Society open science · 2021Article
- A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest.Entropy (Basel, Switzerland) · 2021Article
- Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation.Sensors (Basel, Switzerland) · 2021Article
- A Bayesian Deep Neural Network for Safe Visual Servoing in Human-Robot Interaction.Frontiers in robotics and AI · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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 (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.