A fine decision tree regression model using circadian ECG features accurately estimated left ventricular ejection fraction in heart failure patients with an RMSE of 3.42% and correlation of 0.91.
Observational (n=229)
Yes
Can automated regression models using circadian features from 24-hour ECG recordings accurately estimate LVEF in heart failure patients?
Automated regression models using 24-hour ECG features can accurately estimate LVEF in heart failure patients, potentially offering a simple and continuous screening tool.
Effect estimate: Correlation 0.91
p-value: p=<0.01
Heart Failure (HF) significantly impacts approximately 26 million people worldwide, causing disruptions in the normal functioning of their hearts. The estimation of left ventricular ejection fraction (LVEF) plays a crucial role in the diagnosis, risk stratification, treatment selection, and monitoring of heart failure. However, achieving a definitive assessment is challenging, necessitating the use of echocardiography. Electrocardiogram (ECG) is a relatively simple, quick to obtain, provides continuous monitoring of patient's cardiac rhythm, and cost-effective procedure compared to echocardiography. In this study, we compare several regression models (support vector machine (SVM), extreme gradient boosting (XGBOOST), gaussian process regression (GPR) and decision tree) for the estimation of LVEF for three groups of HF patients at hourly intervals using 24-hour ECG recordings. Data from 303 HF patients with preserved, mid-range, or reduced LVEF were obtained from a multicentre cohort (American and Greek). ECG extracted features were used to train the different regression models in one-hour intervals. To enhance the best possible LVEF level estimations, hyperparameters tuning in nested loop approach was implemented (the outer loop divides the data into training and testing sets, while the inner loop further divides the training set into smaller sets for cross-validation). LVEF levels were best estimated using rational quadratic GPR and fine decision tree regression models with an average root mean square error (RMSE) of 3.83% and 3.42%, and correlation coefficients of 0.92 (p<0.01) and 0.91 (p<0.01), respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8-9 am, and 10-11 pm demonstrated to be the lowest RMSE values between the actual and predicted LVEF levels. The findings could potentially lead to the development of an automated screening system for patients with coronary artery disease (CAD) by using the best measurement timings during their circadian cycles.
Younis et al. (Mon,) conducted a observational in Heart Failure (n=229). Automated regression models (Decision Tree) using circadian ECG features vs. Actual LVEF from echocardiography was evaluated on Root mean square error (RMSE) of LVEF estimation (Correlation 0.91, p=<0.01). A fine decision tree regression model using circadian ECG features accurately estimated left ventricular ejection fraction in heart failure patients with an RMSE of 3.42% and correlation of 0.91.
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