Analysis of heart failure is important in clinical practice to ensure coronary artery disease (CAD) patients will be provided with appropriate timely treatment. The current gold-standard, echocardiography, although reliable, provides a once-off left ventricular ejection fraction (LVEF) measurement and does not provide information about heart function disturbances during day / night cardiac cycles. Additionally, the discrimination between heart failure with preserved and mid-range ejection fraction remains challenging in echocardiography tests. In this vein, this study was sought to investigate the ability of heart rate variability (HRV) in categorizing CAD patients into multiple LVEF groups throughout the 24-hour circadian cycle and checking its agreement with established gold-standard echocardiography-based guidelines. A total of 92 CAD patients who have suffered from heart failure were included in this study. The newly introduced index, HRV ejection fraction (HRVEF), was based on optimizing indices extracted from HRV data, which are correlated with the sympathetic and parasympathetic nervous systems, to form group membership of the preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) LVEF categories. HRVEF groups optimized on hourly basis through Jenks natural breaks algorithm exhibited a consistent pattern with a goodness of variance fit (GVF) of more than 70% accuracy during the late-night to early-morning (01:00-08:00) and evening (17:00-23:00) time periods. At these hours, several HRV indices were found significant (p-value ≤ 0.05) in differentiating between HRVEF groups using statistical analysis of variance (ANOVA) test. These features include the successive differences between normal heartbeats (RMSSD), low and high frequency (LF, HF) power, standard deviation of normal heartbeats (SD2), short-term scaling exponent (alpha1), and percentage of normal heartbeats in alternation segments (PAS). The findings of this study suggest HRV as a promising supplementary tool to the once-off echocardiography for timely LVEF measurements and heart failure prognosis. It paves the way towards multi-time HRV-based estimations for LVEF according to the association between LVEF and HRV indices to better demonstrate the circadian cardiac function at different LVEF levels in CAD patients.
Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily attend to ectopic beats but do not consider technical issues that affect the ECG. A secondary aim of this study was to investigate the effect of increasing increments of artifacts on 24 of the most used HRV measures. A two-step preprocessing approach for denoising HRV is introduced which targets each type of noise separately. First, the technical artifacts in the ECG are eliminated by applying complete ensemble empirical mode decomposition with adaptive noise. The second step removes physiological artifacts from the HRV signal using a combination filter of single dependent rank order mean and an adaptive filtering algorithm. The performance of the two-step pre-processing tool showed a high correlation coefficient of 0.846 and RMSE value of 7.69 × 10–5 for 6% of added ectopic beats and 6 dB Gaussian noise. All HRV measures studied except HF peak and LF peak are significantly affected by both types of noise. Frequency measures of Total power, HF power, and LF power and fragmentation measures; PAS, PIP, and PSS are the most sensitive to both types of noise.
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.
Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily attend to ectopic beats but do not consider technical issues that affect the ECG. A secondary aim of this study was to investigate the effect of increasing increments of artifacts on 24 of the most used HRV measures. A two-step preprocessing approach for denoising HRV is introduced which targets each type of noise separately. First, the technical artifacts in the ECG are eliminated by applying complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The second step removes physiological artifacts from the HRV signal using a combination filter of single dependent rank order mean (SDROM) and an adaptive filtering algorithm (ADF). The performance of the two-step pre-processing tool showed a high correlation coefficient of 0.846 and RMSE value of 7.69 x10− 5 for 6% of added ectopic beats and 6dB Gaussian noise. All HRV measures studied except HF peak and LF peak are significantly affected by both types of noise. Frequecy measures of Total power, HF power, and LF power and fragmentation measures; PAS, PIP, and PSS are the most sensitive to both types of noise.
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