Characterization of QT and RR interval series during acute myocardial ischemia by means of recurrence quantification analysis

Association of heartbeat complexity with survival in advanced non-small cell lung cancer patients

Background

Previous studies have shown that the predictive value of traditional linear (time domain and frequency domain) heart rate variability (HRV) for the survival of patients with advanced non-small cell lung cancer (NSCLC) is controversial. Nonlinear methods, based on the concept of complexity, have been used to evaluate HRV, providing a new means to reveal the physiological and pathological changes in HRV. This study aimed to assess the association between heartbeat complexity and overall survival in patients with advanced NSCLC.

Methods

This study included 78 patients with advanced NSCLC (mean age: 62.0 ± 9.3 years). A 5-min resting electrocardiogram of advanced NSCLC patients was collected to analyze the following HRV parameters: time domain indicators, i.e., standard deviation of the normal-normal intervals (SDNN) and root mean square of successive interval differences (RMSSD); frequency domain indicators, i.e., total power (TP), low frequency power (LF), high frequency power (HF), and the ratio of LF to HF (LF/HF); nonlinear HRV indicators characterizing heartbeat complexity, i.e., approximate entropy (ApEn), sample entropy (SampEn), and recurrence quantification analysis (RQA) indexes: mean diagonal line length (Lmean), maximal diagonal line length (Lmax), recurrence rate (REC), determinism (DET), and shannon entropy (ShanEn).

Results

Univariate analysis revealed that the linear frequency domain parameter HF and nonlinear RQA parameters Lmax, REC, and DET were significantly correlated with the survival of advanced NSCLC patients (all p < 0.05). After adjusting for confounders in the multivariate analysis, HF, REC, and DET were found to be independent prognostic factors for the survival of patients with advanced NSCLC (all p < 0.05).

Conclusion

There was an independent association between heartbeat complexity and survival in advanced NSCLC patients. The nonlinear analysis method based on RQA may provide valuable additional information for the prognostic stratification of patients with advanced NSCLC and may supplement the traditional time domain and frequency domain analysis methods.

Non-invasive prognostic biomarker of lung cancer patients with brain metastases: Recurrence quantification analysis of heart rate variability

Background: It has previously been shown that the time-domain characteristic of heart rate variability (HRV) is an independent prognostic factor for lung cancer patients with brain metastasis (LCBM). However, it is unclear whether the nonlinear dynamic features contained in HRV are associated with prognosis in patients with LCBM. Recurrence quantification analysis (RQA) is a common nonlinear method used to characterize the complexity of heartbeat interval time series. This study was aimed to explore the association between HRV RQA parameters and prognosis in LCBM patients.

Methods: Fifty-six LCBM patients from the Department of Radiation Oncology, the First Affiliated Hospital of Bengbu Medical College, were enrolled in this study. Five-minute ECG data were collected by a mini-ECG recorder before the first brain radiotherapy, and then heartbeat interval time series were extracted for RQA. The main parameters included the mean diagonal line length (Lmean), maximal diagonal line length (Lmax), percent of recurrence (REC), determinism (DET) and Shannon entropy (ShanEn). Patients were followed up (the average follow-up time was 19.2 months, a total of 37 patients died), and the relationships between the RQA parameters and survival of LCBM patients were evaluated by survival analysis.

Results: The univariate analysis showed that an Lmax of >376 beats portended worse survival in LCBM patients. Multivariate Cox regression analysis revealed that the Lmax was still an independent prognostic factor for patients with LCBM after adjusting for confounders such as the Karnofsky performance status (KPS) (HR = 0.318, 95% CI: 0.151–0.669, p = 0.003).

Conclusion: Reduced heartbeat complexity indicates a shorter survival time in patients with LCBM. As a non-invasive biomarker, RQA has the potential for application in evaluating the prognosis of LCBM patients.

Discrimination of Patients with Varying Degrees of Coronary Artery Stenosis by ECG and PCG Signals Based on Entropy

Coronary heart disease (CHD) is the leading cause of cardiovascular death. This study aimed to propose an effective method for mining cardiac mechano-electric coupling information and to evaluate its ability to distinguish patients with varying degrees of coronary artery stenosis (VDCAS). Five minutes of electrocardiogram and phonocardiogram signals was collected synchronously from 191 VDCAS patients to construct heartbeat interval (RRI)-systolic time interval (STI), RRI-diastolic time interval (DTI), HR-corrected QT interval (QTcI)-STI, QTcI-DTI, Tpeak-Tend interval (TpeI)-STI, TpeI-DTI, Tpe/QT interval (Tpe/QTI)-STI, and Tpe/QTI-DTI series. Then, the cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), joint distribution entropy (JDistEn), magnitude-squared coherence function, cross power spectral density, and mutual information were applied to evaluate the coupling of the series. Subsequently, support vector machine recursive feature elimination and XGBoost were utilized for feature selection and classification, respectively. Results showed that the joint analysis of XSampEn, XFuzzyEn, and JDistEn had the best ability to distinguish patients with VDCAS. The classification accuracy of severe CHD-mild-to-moderate CHD group, severe CHD-chest pain and normal coronary angiography (CPNCA) group, and mild-to-moderate CHD-CPNCA group were 0.8043, 0.7659, and 0.7500, respectively. The study indicates that the joint analysis of XSampEn, XFuzzyEn, and JDistEn can effectively capture the cardiac mechano-electric coupling information of patients with VDCAS, which can provide valuable information for clinicians to diagnose CHD.

Recurrence quantification analysis to characterize cyclical components of environmental elemental exposures during fetal and postnatal development

Environmental exposures to essential and toxic elements may alter health trajectories, depending on the timing, intensity, and mixture of exposures. In epidemiologic studies, these factors are typically analyzed as a function of elemental concentrations in biological matrices measured at one or more points in time. Such an approach, however, fails to account for the temporal cyclicity in the metabolism of environmental chemicals, which if perturbed may lead to adverse health outcomes. Here, we conceptualize and apply a non-linear method–recurrence quantification analysis (RQA)–to quantify cyclical components of prenatal and early postnatal exposure profiles for elements essential to normal development, including Zn, Mn, Mg, and Ca, and elements associated with deleterious health effects or narrow tolerance ranges, including Pb, As, and Cr. We found robust evidence of cyclical patterns in the metabolic profiles of nutrient elements, which we validated against randomized twin-surrogate time-series, and further found that nutrient dynamical properties differ from those of Cr, As, and Pb. Furthermore, we extended this approach to provide a novel method of quantifying dynamic interactions between two environmental exposures. To achieve this, we used cross-recurrence quantification analysis (CRQA), and found that elemental nutrient-nutrient interactions differed from those involving toxicants. These rhythmic regulatory interactions, which we characterize in two geographically distinct cohorts, have not previously been uncovered using traditional regression-based approaches, and may provide a critical unit of analysis for environmental and dietary exposures in epidemiological studies.

QT interval variability in body surface ECG: measurement, physiological basis, and clinical value: position statement and consensus guidance endorsed by the European Heart Rhythm Association jointly with the ESC Working Group on Cardiac Cellular Electrophysiology

This consensus guideline discusses the electrocardiographic phenomenon of beat-to-beat QT interval variability (QTV) on surface electrocardiograms. The text covers measurement principles, physiological basis, and clinical value of QTV. Technical considerations include QT interval measurement and the relation between QTV and heart rate variability. Research frontiers of QTV include understanding of QTV physiology, systematic evaluation of the link between QTV and direct measures of neural activity, modelling of the QTV dependence on the variability of other physiological variables, distinction between QTV and general T wave shape variability, and assessing of the QTV utility for guiding therapy. Increased QTV appears to be a risk marker of arrhythmic and cardiovascular death. It remains to be established whether it can guide therapy alone or in combination with other risk factors. QT interval variability has a possible role in non-invasive assessment of tonic sympathetic activity.

Paradoxical response to an emotional task: trait characteristics and heart-rate dynamics

The present study evaluated the heart-rate dynamics of subjects reporting decreased (responders) or paradoxically increased relaxation (nonresponders) at the end of a threatening movie. Heart-rate dynamics were characterized by indices extracted through recurrence quantification analysis (RQA) and detrended fluctuation analysis (DFA). These indices were studied as a function of a few individual characteristics: hypnotizability, gender, absorption, anxiety, and the activity of the behavioral inhibition and activation systems (BIS/BAS). Results showed that (a) the subjective experience of responsiveness is associated with the activity of the behavioral inhibition system and (b) a few RQA and DFA indices are able to capture the influence of cognitive-emotional traits, including hypnotizability, on the responsiveness to the threatening task.

Ginseng total saponin attenuates myocardial injury via anti-oxidative and anti-inflammatory properties

Background

Ginseng total saponin (GTS) contains various ginsenosides. These ginsenosides are widely used for treating cardiovascular diseases in Asian communities. The aim of this study was to study the effects of GTS on cardiac injury after global ischemia and reperfusion (I/R) in isolated guinea pig hearts.

Methods

Animals were subjected to normothermic ischemia for 60 minutes, followed by 120 minutes of reperfusion. GTS significantly increased aortic flow, coronary flow, and cardiac output. Moreover, GTS significantly increased left ventricular systolic pressure and the maximal rate of contraction (+dP/dt_max) and relaxation (−dP/dt_max). In addition, GTS has been shown to ameliorate electrocardiographic changes such as the QRS complex, QT interval, and RR interval.

Results

GTS significantly suppressed the biochemical parameters (i.e., lactate dehydrogenase, creatine kinase-MB fraction, and cardiac troponin I levels) and normalized the oxidative stress markers (i.e., malondialdehyde, glutathione, and nitrite). In addition, GTS also markedly inhibits the expression of interleukin-1β (IL-1β), IL-6, and nuclear factor-κB, and improves the expression of IL-10 in cardiac tissue.

Conclusion

These data indicate that GTS mitigates myocardial damage by modulating the biochemical and oxidative stress related to cardiac I/R injury.

The quantification of the QT-RR interaction in ECG signal using the detrended fluctuationanalysis and ARARX modelling

In this paper, the detrended fluctuation analysis DFA is used to investigate and quantify the QT-RR interaction in different pathologic cases in order to distinguish between them. The study is carried out on the ECG signals of MIT-BIH universal database. Different ECG signals related to cardiac pathological cases are concerned with this study. These are: Premature Ventricular Contraction (PVC) (9 cases), Right Bundle Branch Block (RBBB) (4 cases), Left Bundle Branch Block (LBBB) (2 cases), Atrial Premature Beat (APB) (4 cases), Paced Beat (PB) (4 cases), and other pathologic cases with different severity (10 cases). All this cases are compared to the 15 normal cases. The obtained results show that the DFA can identify the healthy subject from the pathologic cases according to the values of the scaling exponent α. The results indicate that α varies between 0.5 and 1 in all cases which means that there is a long range correlation in RR and QT series. The QT and RR series are also modelled using the ARARX model. The parameters of the model are then extracted. The power spectral density (PSD) is estimated by using these parameters in order to provide further information about the causal interactions within the signals and also to determine the power scaling exponent β. This scaling exponent confirms the relationship between RR and QT intervals in all the studied cases except in APB and PB cases where the behaviour is similar to that of the white noise. The QT variability degrees are calculated and the DFA is applied on it. The obtained results show a long range correlation between RR and QT intervals in all cases and an ambiguity in the APB case. The DFA is compared to the Poincaré method in order to evaluate the algorithm performance using the Fuzzy Sugeno classifier is used for this purpose.

Ginsenoside-Re ameliorates ischemia and reperfusion injury in the heart: a hemodynamics approach

Ginsenosides are divided into two groups based on the types of the panaxadiol group (e.g., ginsenoside-Rb1 and -Rc) and the panaxatriol group (e.g., ginsenoside-Rg1 and -Re). Among them, ginsenoside-Re (G-Re) is one of the compounds with the highest content in Panax ginseng and is responsible for pharmacological effects. However, it is not yet well reported if G-Re increases the hemodynamics functions on ischemia (30 min)/reperfusion (120 min) (I/R) induction. Therefore, in the present study, we investigated whether treatment of G-Re facilitated the recovery of hemodynamic parameters (heart rate, perfusion pressure, aortic flow, coronary flow, and cardiac output) and left ventricular developed pressure (±dp/dt_max). This research is designed to study the effects of G-Re by studying electrocardiographic changes such as QRS interval, QT interval and R-R interval, and inflammatory marker such as tissue necrosis factor-α (TNF-α) in heart tissue in I/R-induced heart. From the results, I/R induction gave a significant increase in QRS interval, QT interval and R-R interval, but showed decrease in all hemodynamic parameters. I/R induction resulted in increased TNF-α level. Treatment of G-Re at 30 and 100 μM doses before I/R induction significantly prevented the decrease in hemodynamic parameters, ameliorated the electrocardiographic abnormality, and inhibited TNF-α level. In this study, G-Re at 100 μM dose exerted more beneficial effects on cardiac function and preservation of myocardium in I/R injury than 30 μM. Collectively, these results indicate that G-Re has distinct cardioprotectective effects in I/R induced rat heart.

Investigation of nonlinear pupil dynamics by recurrence quantification analysis

Pupil is controlled by the autonomous nervous system (ANS). It shows complex movements and changes of size even in conditions of constant stimulation. The possibility of extracting information on ANS by processing data recorded during a short experiment using a low cost system for pupil investigation is studied. Moreover, the significance of nonlinear information contained in the pupillogram is investigated. We examined 13 healthy subjects in different stationary conditions, considering habitual dental occlusion (HDO) as a weak stimulation of the ANS with respect to the maintenance of the rest position (RP) of the jaw. Images of pupil captured by infrared cameras were processed to estimate position and size on each frame. From such time series, we extracted linear indexes (e.g., average size, average displacement, and spectral parameters) and nonlinear information using recurrence quantification analysis (RQA). Data were classified using multilayer perceptrons and support vector machines trained using different sets of input indexes: the best performance in classification was obtained including nonlinear indexes in the input features. These results indicate that RQA nonlinear indexes provide additional information on pupil dynamics with respect to linear descriptors, allowing the discrimination of even a slight stimulation of the ANS. Their use in the investigation of pathology is suggested.

Korean Red Ginseng Induced Cardioprotection against Myocardial Ischemia in Guinea Pig

This study was designed to evaluate the protective effect of Korean red ginseng (KRG) against ischemia/reperfusion (I/R) injury in isolated guinea pig heart. KRG has been shown to possess various ginsenosides, which are the major components of Panax ginseng. These components are known naturally occurring compounds with beneficial effects and free radical scavenging activity. The heart was induced to ischemia for 60 min, followed by 120 min reperfusion. The hearts were randomly allocated into five groups (n=8 for each group): normal control (N/C), KRG control, I/R control, 250 mg/kg KRG group and 500 mg/kg KRG group. KRG significantly increased hemodynamics parameters such as aortic flow, coronary flow and cardiac output. Moreover, KRG significantly increased left ventricular systolic pressure (LVSP), the maximal rate of contraction (+dP/dt_max) and maximal rate of relaxation (-dP/dt_max). Also, treatment of KRG ameliorated electrocardiographic index such as the QRS, QT and RR intervals. Moreover, KRG significantly suppressed the lactate dehydrogenase, creatine kinase-MB fraction and cardiac troponin I and ameliorated the oxidative stress markers such as malondialdehyde and glutathione. KRG was standardized through ultra performance liquid chromatograph analysis for its major ginsenosides. Taken together, KRG has been shown to prevent cardiac injury by normalizing the biochemical and oxidative stress.

Influence of QRS complex detection errors on entropy algorithms. Application to heart rate variability discrimination

Signal entropy measures such as approximate entropy (ApEn) and sample entropy (SampEn) are widely used in heart rate variability (HRV) analysis and biomedical research. In this article, we analyze the influence of QRS detection errors on HRV results based on signal entropy measures. Specifically, we study the influence that QRS detection errors have on the discrimination power of ApEn and SampEn using the cardiac arrhythmia suppression trial (CAST) database. The experiments assessed the discrimination capability of ApEn and SampEn under different levels of QRS detection errors. The results demonstrate that these measures are sensitive to the presence of ectopic peaks: from a successful classification rate of 100%, down to a 75% when spikes are present. The discriminating capability of the metrics degraded as the number of misdetections increased. For an error rate of 2% the segmentation failed in a 12.5% of the experiments, whereas for a 5% rate, it failed in a 25%.