ArticleIEEE journal of translational engineering in health and medicine2023
Prediction of Short-Term Mortality of Cardiac Care Unit Patients Using Image-Transformed ECG Waveforms.
Article in IEEE journal of translational engineering in health and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Machine learning-based myocardial infarction bibliometric analysis.Frontiers in medicine · 2025Pooled it
- MM-GradCAM: an improved multimodal GradCAM method with 1D and 2D ECG data for detection of cardiac arrhythmia.Scientific reports · 2026Article
- Multimodal Transformer-Based Electrocardiogram Analysis for Cardiovascular Comorbidity Detection: Model Development and Validation Study.JMIR formative research · 2026Article
- Automated scheme of plaque classification based on segmentation in carotid ultrasound images using transformer approach.Journal of medical ultrasonics (2001) · 2025Article
- Internet of Medical Things Enabled Multimodal Framework: Deep Machine Learning for Chronic Cardiac Disease Prediction in Healthcare 5.0.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe early detection of cardiac disease is important because the disease can lead to sudden death and poor prognosis. Electrocardiograms (ECG) are used to screen for cardiac diseases and are useful for the early detection and determination of treatment strategies. However, the ECG waveforms of cardiac care unit (CCU) patients with severe cardiac disease are often complicated by comorbidities and patient conditions, making it difficult to predict the severity of further cardiac disease. Therefore, this study predicts the short-term prognosis of CCU patients to detect further deterioration in CCU patients at an early stage.
methodsThe ECG data (II, V3, V5, aVR induction) of CCU patients were converted to image data. The transformed ECG images were used to predict short-term prognosis with a two-dimensional convolutional neural network (CNN).
resultsThe prediction accuracy was 77.3%. Visualization by GradCAM showed that the CNN tended to focus on the shape and regularity of waveforms, such as heart failure and myocardial infarction.
conclusionThese results suggest that the proposed method may be useful for short-term prognosis prediction using the ECG waveforms of CCU patients. CLINICAL IMPACT: The proposed method could be used to determine the treatment strategy and choose the intensity of treatment after admission to the CCU.
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