ArticleJournal of the American Heart Association2021
Real-Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks.
Article in Journal of the American Heart Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review.BMC medical informatics and decision making · 2025Pooled it
- A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation.Current cardiology reviews · 2025Pooled it
- Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment-implications for clinical practice.Frontiers in medical technology · 2025Pooled it
- A three-level risk stratification algorithm for arrhythmia based on CNN-LSTM palindromic structure.BioData mining · 2026Article
- Optimization of artificial intelligence models for prediction of new-onset cardiovascular disease in patients with arterial hypertension.PLOS digital health · 2026Article
- Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review.Korean circulation journal · 2026Review
- Predicting outcomes using neural networks in the intensive care unit.World journal of clinical cases · 2025Article
- Non-genetic factors determine deep learning identified ECG differences between black and white healthy subjects.NPJ cardiovascular health · 2025Article
- Machine learning based, subject-specific, gender and race independent, non-invasive estimation of the arterial blood pressure.NPJ cardiovascular health · 2025Article
- Predicting Ventricular Arrhythmia in Myocardial Ischemia Using Machine Learning.Computing in cardiology · 2025Article
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- Deep-Learning-Based Arrhythmia Detection Using ECG Signals: A Comparative Study and Performance Evaluation.Diagnostics (Basel, Switzerland) · 2023Article
- Current and Future Use of Artificial Intelligence in Electrocardiography.Journal of cardiovascular development and disease · 2023Review
- Neural Network-Enabled Identification of Weak Inspiratory Efforts during Pressure Support Ventilation Using Ventilator Waveforms.Journal of personalized medicine · 2023Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Machine learning techniques for arrhythmic risk stratification: a review of the literature.International journal of arrhythmia · 2022Article
- Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis.International journal of arrhythmia · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
Background Accurate detection of arrhythmic events in the intensive care units (ICU) is of paramount significance in providing timely care. However, traditional ICU monitors generate a high rate of false alarms causing alarm fatigue. In this work, we develop an algorithm to improve life threatening arrhythmia detection in the ICUs using a deep learning approach. Methods and Results This study involves a total of 953 independent life-threatening arrhythmia alarms generated from the ICU bedside monitors of 410 patients. Specifically, we used the ECG (4 channels), arterial blood pressure, and photoplethysmograph signals to accurately detect the onset and offset of various arrhythmias, without prior knowledge of the alarm type. We used a hybrid convolutional neural network based classifier that fuses traditional handcrafted features with features automatically learned using convolutional neural networks. Further, the proposed architecture remains flexible to be adapted to various arrhythmic conditions as well as multiple physiological signals. Our hybrid- convolutional neural network approach achieved superior performance compared with methods which only used convolutional neural network. We evaluated our algorithm using 5-fold cross-validation for 5 times and obtained an accuracy of 87.5%±0.5%, and a score of 81%±0.9%. Independent evaluation of our algorithm on the publicly available PhysioNet 2015 Challenge database resulted in overall classification accuracy and score of 93.9% and 84.3%, respectively, indicating its efficacy and generalizability. Conclusions Our method accurately detects multiple arrhythmic conditions. Suitable translation of our algorithm may significantly improve the quality of care in ICUs by reducing the burden of false alarms.
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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.