Estimating Left Ventricle Ejection Fraction Levels Using Circadian Heart Rate Variability Features and Support Vector Regression Models

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26 Scopus citations

Abstract

Objectives: The purpose of this study was to set an optimal fit of the estimated LVEF at hourly intervals from 24-hour ECG recordings and compare it with the fit based on two gold-standard guidelines. Methods: Support vector regression (SVR) models were applied to estimate LVEF from ECG derived heart rate variability (HRV) data in one-hour intervals from 24-hour ECG recordings of patients with either preserved, mid-range, or reduced LVEF, obtained from the Intercity Digital ECG Alliance (IDEAL) study. A step-wise feature selection approach was used to ensure the best possible estimations of LVEF levels. Results: The experimental results have shown that the lowest Root Mean Square Error (RMSE) between the original and estimated LVEF levels was during 3-4 am, 5-6 am and 6-7 pm. Conclusion: The observations suggest these hours as possible times for intervention and optimal treatment outcomes. In addition, LVEF classifications following the ACCF/AHA guidelines leads to a more accurate assessment of mid-range LVEF. Significance: This study paves the way to explore the use of HRV features in the prediction of LVEF percentages as an indicator of disease progression, which may lead to an automated classification process for CAD patients.

Original languageBritish English
Article number9116952
Pages (from-to)746-754
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Volume25
Issue number3
DOIs
StatePublished - Mar 2021

Keywords

  • coronary artery disease (CAD)
  • electrocardiography (ECG)
  • Heart failure (HF)
  • heart rate variability (HRV)
  • left ventricle ejection fraction (LVEF)
  • machine learning
  • support vector regression (SVR)

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