Temporal dynamics of the circadian heart rate following low and high volume exercise training in sedentary male subjects

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

Investigating the effects of beta-blockers on circadian heart rhythm using heart rate variability in ischemic heart disease with preserved ejection fraction

Heart failure is characterized by sympathetic activation and parasympathetic withdrawal leading to an abnormal autonomic modulation. Beta-blockers (BB) inhibit overstimulation of the sympathetic system and are indicated in heart failure patients with reduced ejection fraction. However, the effect of beta-blocker therapy on heart failure with preserved ejection fraction (HFpEF) is unclear. ECGs of 73 patients with HFpEF > 55% were recruited. There were 56 patients in the BB group and 17 patients in the without BB (NBB) group. The HRV analysis was performed for the 24-h period using a window size of 1,4 and 8-h. HRV measures between day and night for both the groups were also compared. Percentage change in the BB group relative to the NBB group was used as a measure of difference. RMSSD (13.27%), pNN50 (2.44%), HF power (44.25%) and LF power (13.53%) showed an increase in the BB group relative to the NBB group during the day and were statistically significant between the two groups for periods associated with high cardiac risk during the morning hours. LF:HF ratio showed a decrease of 3.59% during the day. The relative increase in vagal modulated RMSSD, pNN50 and HF power with a decrease in LF:HF ratio show an improvement in the parasympathetic tone and an overall decreased risk of a cardiac event especially during the morning hours that is characterized by a sympathetic surge.

Does Exercise Training Improve Cardiac-Parasympathetic Nervous System Activity in Sedentary People? A Systematic Review with Meta-Analysis

The aim of this study was to investigate the training-induced effect on cardiac parasympathetic nervous system (PNS) activity, assessed by resting heart rate variability (HRV) and post-exercise heart rate recovery (HRR), in sedentary healthy people. Electronic searches were carried out in PubMed, Embase, and Web of Science. Random-effects models of between-group standardised mean difference (SMD) were estimated. Heterogeneity analyses were performed by means of the chi-square test and I² index. Subgroup analyses and meta-regressions were performed to investigate the influence of potential moderator variables on the training-induced effect. The results showed a small increase in RMSSD (SMD₊ = 0.57 [95% confidence interval (CI) = 0.23, 0.91]) and high frequency (HF) (SMD₊ = 0.21 [95% CI = 0.01, 0.42]) in favour of the intervention group. Heterogeneity tests reached statistical significance for RMSSD and HF (p ≤ 0.001), and the inconsistency was moderate (I² = 68% and 60%, respectively). We found higher training-induced effects on HF in studies that performed a shorter intervention or lower number of exercise sessions (p ≤ 0.001). Data were insufficient to investigate the effect of exercise training on HRR. Exercise training increases cardiac PNS modulation in sedentary people, while its effect on PNS tone requires future study.

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

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.

Investigating Circadian Heart Rate Variability in Coronary Artery Disease Patients with Various Degrees of Left Ventricle Ejection Fraction

Early and noninvasive identification of heart failure progression is an important adjunct to successful and timely intervention. Severity of heart failure (HF) was assessed by Left Ventricular Ejection Fraction (LVEF). In this paper, we explore the circadian (24-hour) heart rate variability (HRV) features from ''normal" (EF >50%), "at-risk" (EF <40%), and "border-line" (40% ≤ EF ≤ 50%) patient data to determine whether HRV features can predict the stage of heart failure. All coronary artery disease (CAD) 24-hour circadian heart rate data were fitted by a cosinor analysis algorithm. Hourly HRV features from time- and frequency-domains were then extracted from all 24-hour patient data. A one-way ANOVA test was performed followed by a Tukey post-hoc multiple comparison test to investigate the differences among the three groups. The results showed a statistically significant difference between the three groups when using the normalized high frequency (HF Norm), low frequency peak (LF Peak), and the normalized very-low frequency (VLF Norm) for the 05:00-06:00 and 18:00-19:00 time periods. These results highlight a possible link between the circadian variation of sympathetic and parasympathetic nervous system activity and LVEF for CAD patients. The results could be useful in differentiating the various degrees of LVEF by using only noninvasive HRV features derived over a 24-hour period.Clinical relevance- The proposed method could be clinically useful to estimate the extent of LVEF associated with the severity of heart failure by recording the circadian variation of the heart rate in CAD patients. However, further clinical trials on a larger cohort of patients and controls are required.