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

Autor: Al Younis SM; Department of Biomedical Engineering, Healthcare Engineering Innovation Centre (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates., Hadjileontiadis LJ; Department of Biomedical Engineering, Healthcare Engineering Innovation Centre (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates.; Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece., Al Shehhi AM; Department of Biomedical Engineering, Healthcare Engineering Innovation Centre (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates., Stefanini C; Creative Engineering Design Lab at the BioRobotics Institute, Applied Experimental Sciences Scuola Superiore Sant'Anna, Pontedera (Pisa), Italy., Alkhodari M; Department of Biomedical Engineering, Healthcare Engineering Innovation Centre (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates.; Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom., Soulaidopoulos S; First Cardiology Department, School of Medicine, 'Hippokration' General Hospital, National and Kapodistrian University of Athens, Athens, Greece., Arsenos P; First Cardiology Department, School of Medicine, 'Hippokration' General Hospital, National and Kapodistrian University of Athens, Athens, Greece., Doundoulakis I; First Cardiology Department, School of Medicine, 'Hippokration' General Hospital, National and Kapodistrian University of Athens, Athens, Greece., Gatzoulis KA; First Cardiology Department, School of Medicine, 'Hippokration' General Hospital, National and Kapodistrian University of Athens, Athens, Greece., Tsioufis K; First Cardiology Department, School of Medicine, 'Hippokration' General Hospital, National and Kapodistrian University of Athens, Athens, Greece., Khandoker AH; Department of Biomedical Engineering, Healthcare Engineering Innovation Centre (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Dec 11; Vol. 18 (12), pp. e0295653. Date of Electronic Publication: 2023 Dec 11 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0295653
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Al Younis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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