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Prediction of Short-Term Mortality of Cardiac Care Unit Patients Using Image-Transformed ECG Waveforms
Terumasa Kondo1, Atsushi Teramoto1, Eiichi Watanabe2
1Graduate School of Health SciencesFujita Health University Aichi 470-1192 Japan.
Insights
This study uses electrocardiogram (ECG) images and a convolutional neural network (CNN) to predict short-term prognosis in cardiac care unit (CCU) patients, aiding early detection of deterioration.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Early detection of cardiac disease is crucial for preventing sudden death and improving patient prognosis.
- Electrocardiograms (ECGs) are vital for screening and treatment planning, but complex patient conditions in CCUs can obscure severity.
- Predicting short-term prognosis in CCU patients is challenging due to comorbidities affecting ECG interpretation.
Purpose of the Study:
- To develop a method for predicting the short-term prognosis of CCU patients.
- To enable early detection of patient deterioration within the CCU.
- To assist in determining optimal treatment strategies and intensity for CCU patients.
Main Methods:
- ECG data from CCU patients (leads II, V3, V5, aVR) were converted into image formats.
- A two-dimensional convolutional neural network (CNN) was employed to analyze these ECG images.
- The CNN model was trained to predict the short-term prognosis based on ECG waveform characteristics.
Main Results:
- The developed CNN model achieved a prediction accuracy of 77.3% for short-term prognosis.
- GradCAM visualization indicated the CNN focused on waveform morphology and regularity, relevant to conditions like heart failure and myocardial infarction.
- The model demonstrated potential in identifying key ECG features indicative of cardiac events.
Conclusions:
- The proposed image-based CNN method shows promise for predicting short-term prognosis in CCU patients using ECG data.
- This approach may enhance the early identification of critical changes in patient condition.
- The findings suggest a potential tool for guiding clinical decisions regarding treatment intensity in CCUs.
Objective:
The 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.
Methods:
The 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).
Results:
The 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.
Conclusion:
These 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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