Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Deep learning-based ROSC prediction and ECG phenotyping in out-of-hospital cardiac arrest.

Resuscitation - 1 May 2026

Choi Dong Hyun, Hong Ki Jeong, Kim Ki Hong, Kim Yoonjic, Kim Min Woo, Shin Sang Do, Song Kyoung Jun, Ro Young Sun, Park Jeong Ho, Kim Tae Han, Jeong Joo

Abstract excerpt

INTRODUCTION: Electrocardiogram (ECG) signals during cardiac arrest contain detailed information on cardiac rhythm characteristics and have been associated with resuscitation outcomes. We aimed to develop a convolutional neural network (CNN) model to predict return of spontaneous circulation (ROSC) and identify novel ECG phenotypes in patients with out-of-hospital cardiac arrest (OHCA). METHODS: This...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Aged
Share to Aged, 80 and over

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Deep Learning

Share to Electrocardiography

Share to Phenotype

Share to Registries

Source

Identifiers

PMID: 41794117
DOI: 10.1016/j.resuscitation.2026.111035

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Deep learning-based ROSC prediction and ECG phenotyping in out-of-hospital cardiac arrest.PMID 41794117Read ↗

A Comparative Study of Convolutional Neural Network and Recurrent Neural Network models for Analysis of Cardiac Arrest Rhythms during Cardiopulmonary ResuscitationSemantic proximity Read ↗

Artificial intelligence-enhanced Electrocardiography Score for Perioperative Risk Assessment in Non-cardiac SurgerySemantic proximity Read ↗

Electrocardiogram-Based Deep Learning for Time-Resolved Prediction of Heart Failure With Reduced Ejection Fraction: A Multinational StudySemantic proximity Read ↗

Out of Hospital Cardiac Arrest - New Insights and a Call for a Worldwide Registry and GuidelinesSemantic proximity Read ↗

Artificial intelligence-based ECG reconstruction error as a continuous predictor of all-cause mortality: a multi-cohort retrospective validation studySemantic proximity Read ↗

Risk Prediction for Non-cardiac Surgery Using the 12-Lead Electrocardiogram: An Explainable Deep Learning ApproachSemantic proximity Read ↗

Predicting adverse outcomes after cardiac surgery using multi-task deep neural networks, clinical features, and electrocardiogramsSemantic proximity Read ↗

The usefulness of the CASPRI and the GO-FAR scores for predicting the neurological prognoses of patients who recovered spontaneous circulation after in-hospital cardiac arrest.Semantic proximity Read ↗

A Multicenter Validation Study of the Deep Learning-based Early Warning Score for Predicting in-hospital Cardiac Arrest in Patients Admitted to General WardsSemantic proximity Read ↗

Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.Semantic proximity Read ↗

Identifying Cardiogenic Shock Sub-Phenotypes with Machine Learning: A Multicenter Study Combining Clinical and Echocardiographic DataSemantic proximity Read ↗

Aberrant brain-heart coupling is associated with the severity and prognosis of hypoxic-ischemic brain injury after cardiac arrestSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.