Deceleration and acceleration capacities of heart rate in patients with drug-resistant epilepsy

Heart Rhythm Complexity as Predictors for the Prognosis of End-Stage Renal Disease Patients Undergoing Hemodialysis

“…Thereafter, automatic annotated results were carefully visual inspected and manually corrected by editing ectopic beats, arrhythmias and noise to suppress computational errors. Four-hour episodes of heart-beat intervals without naps and exercise within daytime (between 8 a.m. and 5 p.m.) were extracted from each recording for MSE and traditional HRV analysis [18,19] .…”

“…It comprises of two steps: 1) coarse-graining the time series in finite length into different time scales; 2) quantifying the degree of irregularity in each coarse-grained time series by sample entropy calculations [14,15] . The quantified entropy values of coarse-grained time series then are represented as the function of time scale factors to evaluate the complex structure of physiological time series, and the features of the MSE curve can be extracted for clinical categorization in several diseases [16][17][18][19] . In-depth, details of this methodology have been previously described [14,15] .…”

“…In-depth, details of this methodology have been previously described [14,15] . In the present study, the complexity index (CI) of the HRV time series were quantified by curve fitting and calculating the area between the MSE curve and the axis of scale factors [18,19] . The linear-fitted slope (Slope 5) and the area under the MSE curve between scales 1 and 5 (Area 1-5) were calculated to quantify the short-term complexity and to characterize the short-scale modulation pattern.…”

“…Empirical mode decomposition (EMD) is suitable for decomposition of nonstationary, non-linear physiologic time series and possesses advantages over wavelet and Fourier analysis because it employs a fully adaptive approach derived by means of a sifting process. In order to remove such effects, we used empirical mode decomposition (EMD) method for raw HRV time series filtering before performing MSE [18,19] .…”

“…Recently, multiscale entropy (MSE) measures quantify multiple spatial and temporal scale complexity of heart rate dynamics, have been introduced as new risk stratifiers [14,15] . At present, MSE has been extensively applied in the assessment of heart rhythm complexity in heart failure, stroke, myocardial infarction, epilepsy and other diseases for predicting patient outcomes [16][17][18][19] . Previous studies have also demonstrated that MSE parameters have greater predictive power than conventional HRV measures for risk stratification and prognosis [19,20] .…”