13 research outputs found
Original and estimated LVEF values per patient for hours (22:00–23:00, 08:00–09:00, 00:00–01:00) using the Tree, GPR, XGBOOST and SVM, respectively.
Original and estimated LVEF values per patient for hours (22:00–23:00, 08:00–09:00, 00:00–01:00) using the Tree, GPR, XGBOOST and SVM, respectively.</p
Bland-Altman plots between the average value of (Original and estimated LVEF) and their corresponding difference for hours (22:00–23:00, 08:00–09:00, 00:00–01:00) using the Tree, GPR, XGBOOST and SVM, respectively.
Bland-Altman plots between the average value of (Original and estimated LVEF) and their corresponding difference for hours (22:00–23:00, 08:00–09:00, 00:00–01:00) using the Tree, GPR, XGBOOST and SVM, respectively.</p
Average RMSE values through 24-Hours, using regression models with best hyperparameters.
Average RMSE values through 24-Hours, using regression models with best hyperparameters.</p
ECG features for the three groups (HFpEF, HFmEF, and HFrEF).
ECG features for the three groups (HFpEF, HFmEF, and HFrEF).</p
Feature importance over the Tree regression model (10:00–11:00pm).
This study used normalized importance scores for the ECG features (QTc, QRS, ST-T, TP, Entropy, and Instant Frequency) to estimate the LVEF levels in the three HF categories. Indicating that QTc is the most important feature for evaluating the LVEF levels.</p
RMSE per hour using the best hyperparameters combination for the four regression models.
Red circles show the hours of occurrence of the lowest RMSE values. Where QTc and QRS are found to be the most important features for evaluating the LVEF levels.</p
Correlation plots between the original and estimated LVEF values for hours (22:00–23:00, 08:00–09:00, 00:00–01:00) using the Tree, GPR, XGBOOST and SVM, respectively.
Correlation plots between the original and estimated LVEF values for hours (22:00–23:00, 08:00–09:00, 00:00–01:00) using the Tree, GPR, XGBOOST and SVM, respectively.</p
Definitions of ECG features.
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</div
Clinical characteristics of the heart failure patients based on their LVEF categories.
Clinical characteristics of the heart failure patients based on their LVEF categories.</p
An illustration of the overall procedure followed in this study.
An illustration of the overall procedure followed in this study.</p