Key result
Support vector regression models estimating LVEF from 24-hour ECG heart rate variability showed the lowest Root Mean Square Error during 3-4 am, 5-6 am, and 6-7 pm.
Why the study?
To determine an optimal fit of estimated LVEF at hourly intervals from 24-hour ECG recordings and compare it with the fit based on two gold-standard guidelines.
Does a support vector regression model using circadian heart rate variability features accurately estimate LVEF levels in patients with varying LVEF?
Observational
Does a support vector regression model using circadian heart rate variability features accurately estimate LVEF levels in patients with varying LVEF?
Machine learning models using circadian heart rate variability from 24-hour ECGs can estimate LVEF, potentially enabling automated disease progression monitoring in CAD patients.
Enables continuous LVEF estimation from 24-h ECG; leaves open prospective validation before clinical adoption.
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.
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Alkhodari et al. (2020) conducted an observational in Preserved, mid-range, or reduced LVEF (CAD). Support vector regression (SVR) models using circadian heart rate variability features vs. Fit based on two gold-standard guidelines was evaluated on Root Mean Square Error (RMSE) between original and estimated LVEF levels. Support vector regression models estimating LVEF from 24-hour ECG heart rate variability showed the lowest Root Mean Square Error during 3-4 am, 5-6 am, and 6-7 pm.
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