ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2018
A Deep Learning Approach to Examine Ischemic ST Changes in Ambulatory ECG Recordings.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Systematic Review of Clinical Decision Support Systems for Prehospital Acute Coronary Syndrome Identification.Critical pathways in cardiology · 2020Pooled it
- ISENet: a deep learning model for detecting ischemic ST changes in long-term ECG monitoring.Medical & biological engineering & computing · 2025Article
- Using Multi-Task Learning-Based Framework to Detect ST-Segment and J-Point Deviation From Holter.Frontiers in physiology · 2022Article
- Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review.Frontiers in cardiovascular medicine · 2022Review
- KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement.Journal of healthcare engineering · 2021Article
- Deep Learning in Physiological Signal Data: A Survey.Sensors (Basel, Switzerland) · 2020Review
- Assessment of Electrocardiogram Rhythms by GoogLeNet Deep Neural Network Architecture.Journal of healthcare engineering · 2019Article
- Monitoring significant ST changes through deep learning.Journal of electrocardiologyArticle
- Applications of deep learning methods in digital biomarker research using noninvasive sensing data.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Patients with suspected acute coronary syndrome (ACS) are at risk of transient myocardial ischemia (TMI), which could lead to serious morbidity or even mortality. Early detection of myocardial ischemia can reduce damage to heart tissues and improve patient condition. Significant ST change in the electrocardiogram (ECG) is an important marker for detecting myocardial ischemia during the rule-out phase of potential ACS. However, current ECG monitoring software is vastly underused due to excessive false alarms. The present study aims to tackle this problem by combining a novel image-based approach with deep learning techniques to improve the detection accuracy of significant ST depression change. The obtained convolutional neural network (CNN) model yields an average area under the curve (AUC) at 89.6% from an independent testing set. At selected optimal cutoff thresholds, the proposed model yields a mean sensitivity at 84.4% while maintaining specificity at 84.9%.
Identifiers
29888083PMC5961830W3023600000What OpenQuestion holds
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.