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Objective Pre-eclampsia is a serious complication of pregnancy with high morbidity and mortality and an incidence of 3-5% in all pregnancies. Early prediction is still insufficient in clinical practice. Although most pre- eclamptic patients have pathological uterine perfusion in the second trimester, perfusion disturbance has a positive predictive accuracy (PPA) only of approximately 30%. Methods Non-invasive continuous blood pressure recordings were taken simultaneously via a finger cuff for 30 min. Time series of systolic as well as diastolic beat-to-beat pressure values were extracted to analyse heart rate and blood pressure variability and baroreflex sensitivity in 102 second- trimester pregnancies, to assess predictability for pre-eclampsia (n = 16). All women underwent Doppler investigations of the uterine arteries. Results We identified a combination of three variability and baroreflex parameters to best predict pre-eclampsia several weeks before clinical manifestation. The discriminant function of these three parameters classified patients with later pre-eclampsia with a sensitivity of 87.5%, a specificity of 83.7%, and a PPA of 50.0%. Combined with Doppler investigations of uterine arteries, PPA increased to 71.4%. Conclusions This technique of incorporating one-stop clinical assessment of uterine perfusion and variability parameters in the second trimester produces the most effective prediction of pre-eclampsia to date
Baroreflex sensitivity (BRS) is an important parameter in the classification of patients with reduced left ventricular function. This study aimed at investigating BRS in patients with dilated cardiomyopathy (DCM) and in healthy subjects (controls), as well as comparing the values of BRS parameters with parameters of heart rate variability (HRV) and blood pressure variability (BPV). ECG, continuous blood pressure and respiration curves were recorded for 30 min in 27 DCM patients and 27 control subjects. The Dual Sequence Method (DSM) includes the analysis of spontaneous fluctuations in systolic blood pressure and the corresponding beat-to-beat intervals of heart rate to estimate bradycardic, opposite tachycardic and delayed baroreflex fluctuations. The number of systolic blood pressure/beat-to- beat interval fluctuations in DCM patients was reduced in comparison with controls (DCM patients: male, 154.4+/-93.9 ms/ mmHg; female, 93.7+/-40.5 ms/mmHg; controls: male, 245.5+/-112.9 ms/mmHg; female, 150.6+/-55.8 ms/mmHg, P<0.05). The average slope in DCM patients was lower than in controls (DCM, 5.3+/-1.9 ms/mmHg; controls, 8.0+/-5.4 ms/mmHg; P<0.05). Discriminant function analysis showed that, in the synchronous range of the standard sequence method, the DCM and control groups could be discriminated to only 76% accuracy, whereas the DSM gave an improved accuracy of 84%. The combination of six parameters of HRV, BPV and DSM gives an accuracy of classification of 96%, whereas six parameters of HRV and BPV could separate the two groups to only 88% accuracy. Thus the DSM leads to an improved characterization of autonomous regulation in order to differentiate between DCM patients and healthy subjects. BRS in DCM patients is significantly reduced and apparently less effective.
Hypertensive pregnancy disorders are a leading cause of perinatal and maternal morbidity and mortality. Heart rate variability (HRV), blood pressure variability (BPV), and baroreflex sensitivity (BRS) are relevant predictors of cardiovascular risk in humans. The aim of the study was to evaluate whether HRV, BPV, and BRS differ between distinct hypertensive pregnancy disorders. Continuous heart rate and blood pressure recordings were performed in 80 healthy pregnant women as controls (CON), 19 with chronic hypertension (CH), 18 with pregnancy-induced hypertension (PIH), and 44 with pre-eclampsia (PE). The data were assessed by time and frequency domain analysis, nonlinear dynamics, and BRS. BPV is markedly altered in all three groups with hypertensive disorders compared to healthy pregnancies, whereby changes were most pronounced in PE patients. Interestingly, this increase in PE patients did not lead to elevated spontaneous baroreflex events, while BPV changes in both the other hypertensive groups were paralleled by alterations in baroreflex parameters. The HRV is unaltered in CH and PE but significantly impaired in PIH. We conclude that parameters of the HRV, BPV, and BRS differ between various hypertensive pregnancy disorders. Thus, distinct clinical manifestations of hypertension in pregnancy have different pathophysiological, regulatory, and compensatory mechanisms
Despite many previous Studies on the association between hyperthyroidism and the hyperadrenergic state, controversies still exist. Detrended fluctuation analysis (DFA) is a well recognized method in the nonlinear analysis of heart rate variability (HRV), and it has physiological significance related to the autonomic nervous system. In particular, an increased short-term scaling exponent alpha 1 calculated from DFA is associated with both increased sympathetic activity and decreased vagal activity. No study has investigated the DFA of HRV in hyperthyroidism. This study was designed to assess the sympathovagal balance in hyperthyroidism. We performed the DFA along with the linear analysis of HRV in 36 hyperthyroid Graves' disease patients (32 females and 4 males; age 30 +/- 1 years, means +/- SE) and 36 normal controls matched by sex, age and body mass index. Compared with the normal controls, the hyperthyroid patients revealed a significant increase (P < 0.001) in alpha 1 (hyperthyroid 1.28 +/- 0.04 versus control 0.91 +/- 0.02), long-term scaling exponent alpha 2 (1.05 +/- 0.02 versus 0.90 +/- 0.01), overall scaling exponent alpha (1.11 +/- 0.02 versus 0.89 +/- 0.01), low frequency power in normalized units (LF%) and the ratio of low frequency power to high frequency power (LF/HF); and a significant decrease (P < 0.001) in the standard deviation of the R-R intervals (SDNN) and high frequency power (HF). In conclusion, hyperthyroidism is characterized by concurrent sympathetic activation and vagal withdrawal. This sympathovagal imbalance state in hyperthyroidism helps to explain the higher prevalence of atrial fibrillation and exercise intolerance among hyperthyroid patients.
Correlation dimension analysis of heart rate variability in patients with dilated cardiomyopathy
(2005)
A correlation dimension analysis of heart rate variability (HRV) was applied to a group of 55 patients with dilated cardiomyopathy (DCM) and 55 healthy subjects as controls. The 24-h RR time series for each subject was divided into segments of 10,000 beats to determine the correlation dimension (CD) per segment. A study of the influence of the time delay (tag) in the calculation of CD was performed. Good discrimination between both groups (p < 0.005) was obtained with tag values of 5 or greater. CD values of DCM patients (8.4 ± 1.9) were significantly lower than CD values for controls (9.5 ± 1.9). An analysis of CD values of HRV showed that for healthy people, CD night values (10.6 ± 1.8) were significant greater than CD day values (9.2 ± 1.9), revealing a circadian rhythm. In DCM patients, this circadian rhythm was lost and there were no differences between CD values in day (8.8 ± 2.4) and night (8.9 ± 2. 1). © 2005 Elsevier Ireland Ltd. All rights reserved
The analysis of baroreflex sensitivity (BRS) and heart rate variability (HRV) leads to additional insights into patients' prognosis after cardiovascular events. The following study was performed to assess the differences in the post-operative recovery of autonomic regulation after mitral valve (MV) and aortic valve (AV) surgery with a heart lung machine. Among the 43 consecutive male patients enrolled in a prospective study, 26 underwent isolated AV surgery and 17 isolated MV surgery. Blood pressure as well as ECG signals were recorded the day before, 24 hours after and one week after surgery. BRS was calculated according to the dual sequence method, and HRV was calculated using standard linear as well as nonlinear parameters. There were no major differences between the two groups in the pre-operative values. At 24 hours a comparable depression of HRV and BRS in both groups was observed, while at 7 days there was partial recovery in AV patients, which was absent in MV patients: p(AV versus MV) < 0.001. While the response of the autonomic system to surgery is similar in AV and MV patients, there is obviously a decreased ability to recover in MV patients, probably attributed to traumatic lesions of the autonomic nervous system by opening the atria. Ongoing research is required for further clarification of the pathophysiology of this phenomenon and to establish strategies to restore autonomic function.
Atrial natriuretic peptides (ANP) and brain natriuretic peptides (BNP) are powerful neurohormonal indicators of left-ventricular function and prognosis in heart failure (HF). Chagas disease (CD) caused by the protozoan Trypanosoma cruzi. remains a major cause of HF in Latin America. We assessed whether the plasma concentration of the third natriuretic peptide, C-type natnuretic peptide (CNP), also has diagnostic and prognostic properties in patients with CD or other dilated cardiomyopathies (DCM). Blood samples were obtained from 66 patients with CD, 50 patients with DCM from other causes, and 30 gender- and age-matched healthy subjects. Patients were subdivided according to the New York Heart Association (NYHA) class. The CNP concentration was determined by radioimmunoassay (Immundiagnostik, Bensheim, Germany). The main duration of follow-up was 31.4 months (range 13 to 54 months), 19 patients had died and 11 patients received a heart transplant. CNP concentrations were only significantly altered in patients with DCM or CD of the NYHA classes III and IV (P < 0.05). The Pearson correlation of echocardiographic data with CNP revealed an association only with the left-ventricular end systolic volume (P = 0.03) in patients with DCM. Furthermore, CNP did not predict mortality or the necessity for heart transplant. Our data are the first to demonstrate the raised levels of the third natriuretic peptide CNP in CD and other DCM Whereas ANP and BNP have a high predictive value for mortality in both diseases, CNP is without any predictive potency.
Standard time and frequency parameters of heart rate variability (HRV) describe only linear and periodic behaviour, whereas more complex relationships cannot be recognised. A method that may be capable of assessing more complex properties is the non-linear measure of 'renormalised entropy.' A new concept of the method, RE(AR), has been developed, based on a non-linear renormalisation of autoregressive spectral distributions. To test the hypothesis that renormalised entropy may improve the result of high-risk stratification after myocardial infarction, it is applied to a clinical pilot study (41 subjects) and to prospective data of the St George's Hospital post- infarction database (572 patients). The study shows that the new RE(AR) method is more reproducible and more stable in time than a previously introduced method (p<0.001). Moreover, the results of the study confirm the hypothesis that on average, the survivors have negative values of RE(AR) (-0.11+/-0.18), whereas the non-survivors have positive values (0.03+/-0.22, p<0.01). Further, the study shows that the combination of an HRV triangular index and RE(AR) leads to a better prediction of sudden arrhythmic death than standard measurements of HRV. In summary, the new RE(AR) method is an independent measure in HRV analysis that may be suitable for risk stratification in patients after myocardial infarction.
Objectives. Ventricular tachycardia (VT) provoking sudden cardiac death (SCD) are a major cause of mortality in the developed countries. The most efficient therapy for SCID prevention are implantable cardioverter defibrillators (ICD). In this study heart rate variability (HRV) measures were analyzed for short-term forecasting of VT in order to improve VT sensing and to enable a patient warning of forthcoming shocks. Methods. The lost 1000 normal beat-to-beat intervals before 50 VT episodes stored by the ICD were analyzed and compared to individually acquire control time series (CON). HRV analysis was performed with standard parameters of time and frequency domain as suggested by the HRV Task Force and furthermore with a newly developed and optimized nonlinear parameter that assesses the compression entropy of heart rate (H-c). Results. Except of meanNN (p = 0.02) we found no significant differences in standard HRV parameters. In contrast, H, revealed highly significant (p = 0.007) alterations in VT compared with CON suggesting a decreased complexity before the onset of VT. Conclusion: Compression entropy might be a suitable parameter for short-term forecasting of life-threatening tachycardia in ICD
Objective: Impairment of the baroreceptor reflex activity reflects an alteration of the autonomous regulation of the cardiovascular system and has proven to predict fatal outcome in patients after acute myocardial infarction. The following study was performed to analyse the baroreceptor sensitivity, heart rate variability and blood pressure variability in patients early after coronary surgery. Methods: Twenty-five male patients undergoing coronary artery bypass were examined in a prospective study; normal values were obtained from healthy volunteers. Arterial pressure signals were recorded from a radial artery catheter for 30 min preoperatively and in short intervals after surgery. Mechanical manipulations and pharmacological interventions were avoided during the sampling periods. Baroreflex function was calculated according to the dual sequence method, heart rate variability and blood pressure variability were calculated including nonlinear methods. Results: Initial values of the patients did not differ from healthy volunteers. The strength of baroreflex sensitivity (increase in blood pressure causing a synchronous decrease of heart rate) is low 2 It postoperatively. The number of delayed tachycardic changes of heart rate, which are caused by sympathetic activation, is only moderately reduced as compared to values obtained from healthy volunteers. Heart rate variability is widely unchanged as compared to preoperative values; blood pressure variability showed an increase of low-frequency components, again indicating sympathetic predominance. Nonlinear analyses revealed reduced system complexity at the beginning of the postoperative course. Conclusion: Obviously, there is a vagal suppression 20 h after surgery, while the sympathetic tonus works in a normal range. This unbalanced interaction of the autonomous systems is similar to findings in patients after myocardial infarction. The predictive value of these markers has to be elucidated in further clinical studies. (C) 2003 Elsevier B.V. All rights reserved
Objectives: Scoring sleep visually based on polysomnography is an important but time-consuming element of sleep medicine. Where-as computer software assists human experts in the assignment of sleep stages to polysomnogram epochs, their performance is usually insufficient. This study evaluates the possibility to fully automatize sleep staging considering the reliability of the sleep stages available from human expert sleep scorers. Methods: We obtain features from EEG, ECG and respiratory signals of polysomnograms from ten healthy subjects. Using the sleep stages provided by three human experts, we evaluate the performance of linear discriminant analysis on the entire polysomnogram and:only on epochs where the three experts agree in their-sleep stage scoring. Results: We show that in polysomnogram intervals, to which all three scorers assign the same sleep stage, our algorithm achieves 90% accuracy. This high rate of agreement with the human experts is accomplished with only a small set of three frequency features from the EEG. We increase-the performance to 93% by including ECG and respiration features. In contrast, on intervals of ambiguous sleep stage, the sleep stage classification obtained from our algorithm, agrees with the human consensus scorer in approximately 61%. Conclusions: These findings suggest that machine classification is highly consistent with human sleep staging and that error in the algorithm's assignments is rather a problem of lack of well-defined criteria for human experts to judge certain polysomnogram epochs than an insufficiency of computational procedures
The incidence of cardiovascular diseases increases with the growth of the human population and an aging society, leading to very high expenses in the public health system. Therefore, it is challenging to develop sophisticated methods in order to improve medical diagnostics. The question whether the normal heart rate is chaotic or not is an attempt to elucidate the underlying mechanisms of cardiovascular dynamics and therefore a highly controversial topical challenge. In this contribution we demonstrate that linear and nonlinear parameters allow us to separate completely the data sets of the three groups provided for this controversial topic in nonlinear dynamics. The question whether these time series are chaotic or not cannot be answered satisfactorily without investigating the underlying mechanisms leading to them. We give an example of the dominant influence of respiration on heart beat dynamics, which shows that observed fluctuations can be mostly explained by respiratory modulations of heart rate and blood pressure (coefficient of determination: 96%). Therefore, we recommend reformulating the following initial question: "Is the normal heart rate chaotic?" We rather ask the following: " Is the normal heart rate 'chaotic' due to respiration?"
In this work, we reanalyze the heart rate variability (HRV) data from the 2002 Computers in Cardiology (CiC) Challenge using the concept of large-scale dimension densities and additionally apply this technique to data of healthy persons and of patients with cardiac diseases. The large-scale dimension density (LASDID) is estimated from the time series using a normalized Grassberger-Procaccia algorithm, which leads to a suitable correction of systematic errors produced by boundary effects in the rather large scales of a system. This way, it is possible to analyze rather short, nonstationary, and unfiltered data, such as HRV. Moreover, this method allows us to analyze short parts of the data and to look for differences between day and night. The circadian changes in the dimension density enable us to distinguish almost completely between real data and computer-generated data from the CiC 2002 challenge using only one parameter. In the second part we analyzed the data of 15 patients with atrial fibrillation (AF), 15 patients with congestive heart failure (CHF), 15 elderly healthy subjects (EH), as well as 18 young and healthy persons (YH). With our method we are able to separate completely the AF (rho(mu)(ls)=0.97 +/- 0.02) group from the others and, especially during daytime, the CHF patients show significant differences from the young and elderly healthy volunteers (CHF, 0.65 +/- 0.13; EH, 0.54 +/- 0.05; YH, 0.57 +/- 0.05; p < 0.05 for both comparisons). Moreover, for the CHF patients we find no circadian changes in rho(mu)(ls) (day, 0.65 +/- 0.13; night, 0.66 +/- 0.12; n.s.) in contrast to healthy controls (day, 0.54 +/- 0.05; night, 0.61 +/- 0.05; p=0.002). Correlation analysis showed no statistical significant relation between standard HRV and circadian LASDID, demonstrating a possibly independent application of our method for clinical risk stratification
In the last decade, there has been an increasing interest in compensating thermally induced errors to improve the manufacturing accuracy of modular tool systems. These modular tool systems are interfaces between spindle and workpiece and consist of several complicatedly formed parts. Their thermal behavior is dominated by nonlinearities, delay and hysteresis effects even in tools with simpler geometry and it is difficult to describe it theoretically. Due to the dominant nonlinear nature of this behavior the so far used linear regression between the temperatures and the displacements is insufficient. Therefore, in this study we test the hypothesis whether we can reliably predict such thermal displacements via nonlinear temperature-displacement regression functions. These functions are estimated firstly from learning measurements using the alternating conditional expectation (ACE) algorithm and then tested on independent data sets. First, we analyze data that were generated by a finite element spindle model. We find that our approach is a powerful tool to describe the relation between temperatures and displacements for simulated data. Next, we analyze the temperature-displacement relationship in a silent real experimental setup, where the tool system is thermally forced. Again, the ACE-algorithm is powerful to estimate the deformation with high precision. The corresponding errors obtained by using the nonlinear regression approach are 10-fold lower in comparison to multiple linear regression analysis. Finally, we investigate the thermal behavior of a modular tool system in a working milling machine and get again promising results. The thermally induced errors can be estimated with 1-2${mu m}$ accuracy using this nonlinear regression analysis. Therefore, this approach seems to be very useful for the development of new modular tool systems.