Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features

Abstract

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.

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning

“…Following the ASE/EACVI guidelines, the patients were categorized into 92 with HFmEF, 129 with HFpEF, and 82 with heart failure with HfrEF. Choosing ASE/EACVI guidelines enhances the comparability of our study’s results with those of other research endeavors that have followed similar criteria [ 50 – 64 ]. This comparability is crucial for validating our findings and contributing to the cumulative knowledge within the scientific community.…”

“…Furthermore, the STT interval exhibits a close relationship with T-wave morphology. Twave abnormalities, including inversion or flattening, are frequently observed in individuals with heart failure [61][62][63][64]. These alterations in T-wave patterns can provide valuable insights into myocardial ischemia or injury and contribute to the accuracy of differentiating LVEF levels.…”