ArticleSensors (Basel, Switzerland)2021
Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- Closed-loop and Artificial intelligence guided cardiopulmonary resuscitation using real-time physiological signals: a systematic review and meta-analysis.BMC emergency medicine · 2026Pooled it
- Cascade Skip-Connection BiLSTM Autoencoder for CPR Artifact Removal Prior to AED Shock Advisory.IEEE open journal of the Computer Society · 2026Article
- Use of artificial intelligence in the out-of-hospital care settings: a scoping review.BMJ digital health & AI · 2026Article
- Rethinking the 2-minute rule in adult basic life support cardiopulmonary resuscitation.Resuscitation plus · 2025Article
- Comparison of Neural Network Structures for Identifying Shockable Rhythm During Cardiopulmonary Resuscitation.Journal of clinical medicine · 2025Article
- Machine learning innovations in CPR: a comprehensive survey on enhanced resuscitation techniques.Artificial intelligence review · 2025Article
- Implementing an intelligent diagnosis and treatment system for in-hospital cardiac arrest in the Utstein style: a multi-center case study.Journal of translational medicine · 2024Article
- Article
- Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG).Sensors (Basel, Switzerland) · 2024Article
- New Innovations to Address Sudden Cardiac Arrest.US cardiology · 2024Review
- 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
- Deep Learning Strategy for Sliding ECG Analysis during Cardiopulmonary Resuscitation: Influence of the Hands-Off Time on Accuracy.Sensors (Basel, Switzerland) · 2023Article
- Convolution Neural Network Algorithm for Shockable Arrhythmia Classification Within a Digitally Connected Automated External Defibrillator.Journal of the American Heart Association · 2023Article
- Current and Future Use of Artificial Intelligence in Electrocardiography.Journal of cardiovascular development and disease · 2023Review
- Real-time amplitude spectrum area estimation during chest compression from the ECG waveform using a 1D convolutional neural network.Frontiers in physiology · 2023Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Atrioventricular Synchronization for Detection of Atrial Fibrillation and Flutter in One to Twelve ECG Leads Using a Dense Neural Network Classifier.Sensors (Basel, Switzerland) · 2022Article
- Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
High performance of the shock advisory analysis of the electrocardiogram (ECG) during cardiopulmonary resuscitation (CPR) in out-of-hospital cardiac arrest (OHCA) is important for better management of the resuscitation protocol. It should provide fewer interruptions of chest compressions (CC) for non-shockable organized rhythms (OR) and Asystole, or prompt CC stopping for early treatment of shockable ventricular fibrillation (VF). Major disturbing factors are strong CC artifacts corrupting raw ECG, which we aimed to analyze with optimized end-to-end convolutional neural network (CNN) without pre-filtering or additional sensors. The hyperparameter random search of 1500 CNN models with 2-7 convolutional layers, 5-50 filters and 5-100 kernel sizes was done on large databases from independent OHCA interventions for training (3001 samples) and validation (2528 samples). The best model, named CNN3-CC-ECG network with three convolutional layers (filters@kernels: 5@5,25@20,50@20) presented Sensitivity Se(VF) = 89%(268/301), Specificity Sp(OR) = 91.7%(1504/1640), Sp(Asystole) = 91.1%(3325/3650) on an independent test OHCA database. CNN3-CC-ECG's ability to effectively extract features from raw ECG signals during CPR was comprehensively demonstrated, and the dependency on the CPR corruption level in ECG was tested. We denoted a significant drop of Se(VF) = 74.2% and Sp(OR) = 84.6% in very strong CPR artifacts with a signal-to-noise ratio of SNR < -9 dB,
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Registered trials
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.