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Utilizing Circadian Heart Rate Variability Features and Machine Learning for Estimating Left Ventricular Ejection
Nanxiang Zhang1, Qi Pan1, Shuo Yang1
1Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
Insights
Machine learning accurately estimates left ventricular ejection fraction (LVEF) using electrocardiography (ECG) signals. This non-invasive method offers a cost-effective approach for hypertension patient monitoring.
Area of Science:
- Cardiology
- Biomedical Engineering
- Machine Learning
Background:
- Hypertension management requires early identification of left ventricular ejection fraction (LVEF) to prevent cardiac issues.
- Current LVEF assessment methods are often invasive, costly, or lack definitive results.
- Developing non-invasive, cost-effective LVEF estimation tools is crucial for widespread clinical application.
Purpose of the Study:
- To develop a machine learning framework for automatic LVEF estimation from electrocardiography (ECG) signals.
- To evaluate the efficacy of using heart rate variability (HRV) features derived from Composite Multiscale Entropy (CMSE) for LVEF prediction.
- To compare the performance of different machine learning models in estimating LVEF.
Main Methods:
- Collected 24-hour Holter ECG and echocardiography data from 200 hypertensive patients.
- Extracted CMSE-based HRV features from hourly ECG intervals.
- Employed machine learning models including Linear Regression (LR), Support Vector Machines (SVMs), and Random Forests (RFs) with feature selection for LVEF estimation.
Main Results:
- The Linear Regression (LR) model demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of 4.61% and a Mean Absolute Error (MAE) of 3.74% during the early night interval (20:00-21:00).
- Specific CMSE parameters (Scales 1, 5, Slope 1-5, and Area 1-5) significantly improved the estimation accuracy of regression models.
- The developed framework enabled quantitative LVEF estimation from ECG signals.
Conclusions:
- CMSE-derived circadian HRV features from Holter ECG provide a non-invasive, cost-effective, and interpretable method for LVEF assessment in community settings.
- The study highlights the clinical potential of CMSE in analyzing autonomic dynamics and cardiac function.
- Machine learning interpretation of CMSE features offers valuable insights into cardiac health monitoring for hypertensive patients.
Background:
Early identification of left ventricular ejection fraction (LVEF) levels during the progression of hypertension is essential to prevent cardiac deterioration. However, achieving a non-invasive, cost-effective, and definitive assessment is challenging. It has prompted us to develop a comprehensive machine learning framework for the automatic quantitative estimation of LVEF levels from electrocardiography (ECG) signals.
Methods:
We enrolled 200 hypertensive patients from Zhongshan City, Guangdong Province, China, from 1 November 2022 to 1 January 2025. Participants underwent 24 h Holter monitoring and echocardiography for LVEF estimation. We developed a comprehensive machine learning framework that initiated with preprocessed ECG signal in one-hour intervals to extract CMSE-based heart rate variability (HRV) features, then utilized machine learning models such as linear regression (LR), Support Vector Machines (SVMs), and random forests (RFs) with recursive feature elimination for optimal LVEF estimation.
Results:
The LR model, notably during early night interval (20:00-21:00), achieved a RMSE of 4.61% and a MAE of 3.74%, highlighting its superiority. Compared with other similar studies, key CMSE parameters (Scales 1, 5, Slope 1-5, and Area 1-5) can effectively enhance regression models' estimation performance.
Conclusion:
Our findings suggest that CMSE-derived circadian HRV features from Holter ECG could serve as a non-invasive, cost-effective, and interpretable solution for LVEF assessment in community settings. From a machine learning interpretable perspective, the proposed method emphasized CMSE's clinical potential in capturing autonomic dynamics and cardiac function fluctuations.
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