@article{WesselSchumannWesseletal.2000,
  author    = {Wessel, Niels and Schumann, Agnes and Wessel, Niels and Schumann, Agnes and Schirdewan, Alexander and Voss, Andreas and Kurths, J{\"u}rgen},
  title     = {Entropy measures in heart rate variability data},
  year      = {2000},
  language  = {en}
}
@article{WesselSchirdewan2006,
  author    = {Wessel, Niels and Schirdewan, Alexander},
  title     = {Toward a prediction of sudden death in propofol-related infusion syndrome},
  year      = {2006},
  language  = {en}
}
@article{WaltherWesselMalbergetal.2006,
  author    = {Walther, T and Wessel, Niels and Malberg, Hagen and Voss, Andreas and Stepan, H and Faber, R},
  title     = {A combined technique for predicting pre-eclampsia : concurrent measurement of uterine perfusion and analysis of heart rate and blood pressure variability},
  year      = {2006},
  abstract  = {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},
  language  = {en}
}
@article{CarvajalWesselVallverduetal.2005,
  author    = {Carvajal, R. and Wessel, Niels and Vallverdu, M. and Caminal, P. and Voss, Andreas},
  title     = {Correlation dimension analysis of heart rate variability in patients with dilated cardiomyopathy},
  issn      = {0169-2607},
  year      = {2005},
  abstract  = {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},
  language  = {en}
}
@article{HeringerWaltherMoreiraWesseletal.2005,
  author    = {Heringer-Walther, Silvia and Moreira, M. C. V. and Wessel, Niels and Saliba, J. L. and Silvia-Barra, J. and Pena, J. L. B. and Becker, S. and Siems, W. E. and Schultheiss, H. P. and Walther, T.},
  title     = {Brain natriuretic peptide predicts survival in Chagas' disease more effectively than atrial natriuretic peptide},
  issn      = {1355-6037},
  year      = {2005},
  language  = {en}
}
@article{RaabWesselSchirdewanetal.2006,
  author    = {Raab, Corinna and Wessel, Niels and Schirdewan, Alexander and Kurths, J{\"u}rgen},
  title     = {Large-scale dimension densities for heart rate variability analysis},
  issn      = {1539-3755},
  doi       = {10.1103/Physreve.73.041907},
  year      = {2006},
  abstract  = {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},
  language  = {en}
}
@phdthesis{Wessel2005,
  author    = {Wessel, Niels},
  title     = {Data analysis and modeling of the cardiovascular system},
  address   = {Potsdam},
  pages     = {Getr. Z{\"a}hlung : graph. Darst.},
  year      = {2005},
  language  = {en}
}
@article{WesselKonvickaWeidermannetal.2004,
  author    = {Wessel, Niels and Konvicka, Jan and Weidermann, Frank and Nestmann, S. and Neugebauer, R. and Schwarz, U. and Wessel, A. and Kurths, J{\"u}rgen},
  title     = {Predicting thermal displacements in modular tool systems},
  issn      = {1054-1500},
  year      = {2004},
  abstract  = {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 inducedaccuracy using this nonlinear regression analysis. Therefore, this approach seems to be very useful for the development of new modular tool systems. errors can be estimated with 1-2 micrometer},
  language  = {en}
}
@article{WesselMalbergWalther2004,
  author    = {Wessel, Niels and Malberg, Hagen and Walther, T.},
  title     = {Heart rate turbulence : higher predictive value than other risk stratifiers?},
  issn      = {0009-7322},
  year      = {2004},
  language  = {en}
}
@article{WesselAssmusWeidermannetal.2004,
  author    = {Wessel, Niels and Aßmus, Joerg and Weidermann, Frank and Konvicka, Jan and Nestmann, S. and Neugebauer, R. and Schwarz, Udo and Kurths, J{\"u}rgen},
  title     = {Modeling thermal displacements in modular tool systems},
  year      = {2004},
  abstract  = {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.},
  language  = {en}
}