Heart Rate Variability based Classification of Normal and Hypertension Cases by Linear-nonlinear Method

The aim of this study is to analyse and compare the heart rate variability (HRV) of normal and hypertension cases using time domain, frequency domain, and nonlinear methods. For short term HRV analysis, a five-minute electrocardiogram (ECG) of 57 normal and 56 hypertension subjects were recorded with prior verification of their clinical status by a cardiologist. Most time domain features of hypertension cases have clearly reduced values over normal subjects, frequency domain features, like power in different spectral bands, also have the distinguishable decreased values, whereas sympathovagal balance has clear edge over hypertension cases than normal cases. Nonlinear parameters of Poincare plot, approximate entropy and sample entropy, have higher values in normal cases when compared with hypertension cases. Support vector machine-based binary system classifies these two classes with 100 per cent accuracy and 100 per cent sensitivity when all time domain, frequency domain, and nonlinear features were used. It may work as a better predictor for in patients with hypertension.Science Journal, Vol. 64, No. 6, November 2014, pp.542-548, DOI:http://dx.doi.org/10.14429/dsj.64.786

Application of Entropy Techniques in Analyzing Heart Rate Variabilityusing ECG Signals

The variation of the heart rate about a mean value is the Heart Rate Variability (HRV). HRV reflects the functioning of cardio-respiratory control system. It is used as one of the diagnostic measures to detect heart disorders. In the proposed work, HRV analysis using entropy measures is carried out on healthy, Congestive Heart Failure (CHF) and Atrial Fibrillations (AF) subjects using their ECG signals. The entropy methods used in the work are Approximate entropy (ApE), Symbolic entropy (SyE) and Spectral entropy (SpE). ECG signals of 20 healthy subjects in the age group of 21 – 30 years were acquired using dry electrode at a sampling rate of 500 Hz for 10 minutes. Signal processing algorithms for removal of baseline wandering, power line interference and motion artefacts were applied for the raw ECG signal. The ECG signals for CHF and AF subjects in the age group of 30 – 75 years were obtained from the Physionet database. From the analysis it was found that values of ApE and SyE were highest for AF subjects and for SpE, the value was highest for healthy subjects. Further, values of all the three entropies were lowest for CHF subjects. In conclusion, it indicates that the entropy techniques are useful tools in diagnosing patients having heart disorders

Cardiac damage biomarkers and heart rate variability following a 118-km mountain race: relationship with performance and recovery

This study aimed to assess the release of cardiac damage biomarkers jointly with cardiac autonomic modulation after a mountain ultramarathon. Such knowledge and the possible relationship of these markers with race time is of primary interest to establish possible recommendations upon athletes’ recovery and return to training following these competitions. Forty six athletes enrolled in the Penyagolosa Trails CSP115 race (118 km and a total positive elevation of 5439 m) took part in the study. N-terminal pro-brain natriuretic peptide (NT-proBNP) and high-sensitive cardiac troponin T (hs-TNT) concentrations as well as linear and nonlinear heart rate variability (HRV) were evaluated before and after the race. NT-proBNP and hs-TNT significantly increased post-race; fifty percent of the finishers surpassed the Upper Reference Limit (URL) for hs-TNT while 87% exceeded the URL for NT-proBNP. Overall and vagally-mediated HRV were diminished and cardiac autonomic modulation became less complex and more predictable following the race. More pronounced vagal modulation decreases were associated with higher levels of postexertional NT-proBNP. Moreover, rise in hs-TNT and NT-proBNP was greater among faster runners, while pre-race overall and vagally-mediated HRV were correlated with finishing time. Participation in a 118-km ultratrail induces an acute release of cardiac damage biomarkers and a large alteration of cardiac autonomic modulation. Furthermore, faster runners were those who exhibited a greater rise in those cardiac damage biomarkers. In light of these findings, an appropriate recovery period after ultraendurance races appears prudent and particularly important among better performing athletes. At the same time, HRV analysis is shown as a promising tool to assess athletes’ readiness to perform at their maximum level in an ultraendurance race

Sample Entropy Analysis of Noisy Atrial Electrograms during Atrial Fibrillation

[EN] Most cardiac arrhythmias can be classified as atrial flutter, focal atrial tachycardia, or atrial fibrillation. They have been usually treated using drugs, but catheter ablation has proven more effective. This is an invasive method devised to destroy the heart tissue that disturbs correct heart rhythm. In order to accurately localise the focus of this disturbance, the acquisition and processing of atrial electrograms form the usual mapping technique. They can be single potentials, double potentials, or complex fractionated atrial electrogram (CFAE) potentials, and last ones are the most effective targets for ablation. The electrophysiological substrate is then localised by a suitable signal processing method. Sample Entropy is a statistic scarcely applied to electrograms but can arguably become a powerful tool to analyse these time series, supported by its results in other similar biomedical applications. However, the lack of an analysis of its dependence on the perturbations usually found in electrogram data, such as missing samples or spikes, is even more marked. This paper applied SampEn to the segmentation between non-CFAE and CFAE records and assessed its class segmentation power loss at different levels of these perturbations. The results confirmed that SampEn was able to significantly distinguish between non-CFAE and CFAE records, even under very unfavourable conditions, such as 50% of missing data or 10% of spikes.This research was supported by Research Center for Informatics (no. CZ.02.1.01/0.0/0.0/16-019/0000765).Cirugeda Roldan, EM.; Molina Picó, A.; Novák, D.; Cuesta Frau, D.; Kremen, V. (2018). Sample Entropy Analysis of Noisy Atrial Electrograms during Atrial Fibrillation. Computational and Mathematical Methods in Medicine. https://doi.org/10.1155/2018/1874651SAhmed, S., Claughton, A., & Gould, P. A. (2015). Atrial Flutter — Diagnosis, Management and Treatment. Abnormal Heart Rhythms. doi:10.5772/60700Kirchhof, P., & Calkins, H. (2016). Catheter ablation in patients with persistent atrial fibrillation. European Heart Journal, 38(1), 20-26. doi:10.1093/eurheartj/ehw260Nademanee, K., Lockwood, E., Oketani, N., & Gidney, B. (2010). Catheter ablation of atrial fibrillation guided by complex fractionated atrial electrogram mapping of atrial fibrillation substrate. Journal of Cardiology, 55(1), 1-12. doi:10.1016/j.jjcc.2009.11.002NG, J., & GOLDBERGER, J. J. (2007). Understanding and Interpreting Dominant Frequency Analysis of AF Electrograms. Journal of Cardiovascular Electrophysiology, 18(6), 680-685. doi:10.1111/j.1540-8167.2007.00832.xKottkamp, H., & Hindricks, G. (2007). Complex fractionated atrial electrograms in atrial fibrillation: A promising target for ablation, but why, when, and how? Heart Rhythm, 4(8), 1021-1023. doi:10.1016/j.hrthm.2007.05.011Křemen, V., Lhotská, L., Macaš, M., Čihák, R., Vančura, V., Kautzner, J., & Wichterle, D. (2008). A new approach to automated assessment of fractionation of endocardial electrograms during atrial fibrillation. Physiological Measurement, 29(12), 1371-1381. doi:10.1088/0967-3334/29/12/002Nademanee, K., McKenzie, J., Kosar, E., Schwab, M., Sunsaneewitayakul, B., Vasavakul, T., … Ngarmukos, T. (2004). A new approach for catheter ablation of atrial fibrillation: mapping of the electrophysiologic substrate. Journal of the American College of Cardiology, 43(11), 2044-2053. doi:10.1016/j.jacc.2003.12.054Scherr, D., Dalal, D., Cheema, A., Cheng, A., Henrikson, C. A., Spragg, D., … Dong, J. (2007). Automated detection and characterization of complex fractionated atrial electrograms in human left atrium during atrial fibrillation. Heart Rhythm, 4(8), 1013-1020. doi:10.1016/j.hrthm.2007.04.021Almeida, T. P., Chu, G. S., Salinet, J. L., Vanheusden, F. J., Li, X., Tuan, J. H., … Schlindwein, F. S. (2016). Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation. Medical & Biological Engineering & Computing, 54(11), 1695-1706. doi:10.1007/s11517-016-1456-2Molina-Picó, A., Cuesta-Frau, D., Aboy, M., Crespo, C., Miró-Martínez, P., & Oltra-Crespo, S. (2011). Comparative study of approximate entropy and sample entropy robustness to spikes. Artificial Intelligence in Medicine, 53(2), 97-106. doi:10.1016/j.artmed.2011.06.007Cuesta–Frau, D., Miró–Martínez, P., Jordán Núñez, J., Oltra–Crespo, S., & Molina Picó, A. (2017). Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics. Computers in Biology and Medicine, 87, 141-151. doi:10.1016/j.compbiomed.2017.05.028Demont-Guignard, S., Benquet, P., Gerber, U., & Wendling, F. (2009). Analysis of Intracerebral EEG Recordings of Epileptic Spikes: Insights From a Neural Network Model. IEEE Transactions on Biomedical Engineering, 56(12), 2782-2795. doi:10.1109/tbme.2009.2028015Molina–Picó, A., Cuesta–Frau, D., Miró–Martínez, P., Oltra–Crespo, S., & Aboy, M. (2013). Influence of QRS complex detection errors on entropy algorithms. Application to heart rate variability discrimination. Computer Methods and Programs in Biomedicine, 110(1), 2-11. doi:10.1016/j.cmpb.2012.10.014Ganesan, P., Cherry, E. M., Pertsov, A. M., & Ghoraani, B. (2015). Characterization of Electrograms from Multipolar Diagnostic Catheters during Atrial Fibrillation. BioMed Research International, 2015, 1-9. doi:10.1155/2015/272954Lake, D. E., Richman, J. S., Griffin, M. P., & Moorman, J. R. (2002). Sample entropy analysis of neonatal heart rate variability. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 283(3), R789-R797. doi:10.1152/ajpregu.00069.2002Kim, K. K., Baek, H. J., Lim, Y. G., & Park, K. S. (2012). Effect of missing RR-interval data on nonlinear heart rate variability analysis. Computer Methods and Programs in Biomedicine, 106(3), 210-218. doi:10.1016/j.cmpb.2010.11.011Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049. doi:10.1152/ajpheart.2000.278.6.h2039Cirugeda–Roldán, E., Novak, D., Kremen, V., Cuesta–Frau, D., Keller, M., Luik, A., & Srutova, M. (2015). Characterization of Complex Fractionated Atrial Electrograms by Sample Entropy: An International Multi-Center Study. Entropy, 17(12), 7493-7509. doi:10.3390/e17117493PORTER, M., SPEAR, W., AKAR, J. G., HELMS, R., BRYSIEWICZ, N., SANTUCCI, P., & WILBER, D. J. (2008). Prospective Study of Atrial Fibrillation Termination During Ablation Guided by Automated Detection of Fractionated Electrograms. Journal of Cardiovascular Electrophysiology, 19(6), 613-620. doi:10.1111/j.1540-8167.2008.01189.xKonings, K. T., Kirchhof, C. J., Smeets, J. R., Wellens, H. J., Penn, O. C., & Allessie, M. A. (1994). High-density mapping of electrically induced atrial fibrillation in humans. Circulation, 89(4), 1665-1680. doi:10.1161/01.cir.89.4.1665Fay, M. P., & Proschan, M. A. (2010). Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Statistics Surveys, 4(0), 1-39. doi:10.1214/09-ss051Richman, J. S. (2007). Sample Entropy Statistics and Testing for Order in Complex Physiological Signals. Communications in Statistics - Theory and Methods, 36(5), 1005-1019. doi:10.1080/03610920601036481Pincus, S. M., Gladstone, I. M., & Ehrenkranz, R. A. (1991). A regularity statistic for medical data analysis. Journal of Clinical Monitoring, 7(4), 335-345. doi:10.1007/bf01619355Alcaraz, R., & Rieta, J. J. (2009). Non-invasive organization variation assessment in the onset and termination of paroxysmal atrial fibrillation. Computer Methods and Programs in Biomedicine, 93(2), 148-154. doi:10.1016/j.cmpb.2008.09.001Alcaraz, R., Abásolo, D., Hornero, R., & Rieta, J. J. (2010). Optimal parameters study for sample entropy-based atrial fibrillation organization analysis. Computer Methods and Programs in Biomedicine, 99(1), 124-132. doi:10.1016/j.cmpb.2010.02.009Costa, M., Goldberger, A. L., & Peng, C.-K. (2002). Multiscale Entropy Analysis of Complex Physiologic Time Series. Physical Review Letters, 89(6). doi:10.1103/physrevlett.89.06810

Multiscale entropy analysis of unattended oximetric recordings to assist in the screening of paediatric sleep apnoea at home

Producción CientíficaUntreated paediatric obstructive sleep apnoea syndrome (OSAS) can severely affect the development and quality of life of children. In-hospital polysomnography (PSG) is the gold standard for a definitive diagnosis though it is relatively unavailable and particularly intrusive. Nocturnal portable oximetry has emerged as a reliable technique for OSAS screening. Nevertheless, additional evidences are demanded. Our study is aimed at assessing the usefulness of multiscale entropy (MSE) to characterise oximetric recordings. We hypothesise that MSE could provide relevant information of blood oxygen saturation (SpO2) dynamics in the detection of childhood OSAS. In order to achieve this goal, a dataset composed of unattended SpO2 recordings from 50 children showing clinical suspicion of OSAS was analysed. SpO2 was parameterised by means of MSE and conventional oximetric indices. An optimum feature subset composed of five MSE-derived features and four conventional clinical indices were obtained using automated bidirectional stepwise feature selection. Logistic regression (LR) was used for classification. Our optimum LR model reached 83.5% accuracy (84.5% sensitivity and 83.0% specificity). Our results suggest that MSE provides relevant information from oximetry that is complementary to conventional approaches. Therefore, MSE may be useful to improve the diagnostic ability of unattended oximetry as a simplified screening test for childhood OSAS.Sociedad Española de Neumología y Cirugía Torácica (SEPAR) project 153/2015Junta de Castilla y León (Consejería de Educación) y el Fondo Europeo de Desarrollo Regional (FEDER), projects (RTC-2015-3446-1) y (TEC2014-53196-R)Ministerio de Economía y Competitividad (MINECO) y FEDER, y el proyecto POCTEP 0378_AD_EEGWA_2_P de la Comisión Europea. L.National Institutes of Health (NIH) grant 1R01HL130984-01Ministerio de Asuntos Económicos y Transformación Digital, grant IJCI-2014-2266

Physiological time-series investigations of cardiovascular regulation in healthy young adults during physical exercise.

Physiological parameters may be recorded non-invasively to gain information on cardiovascular function which can then characterise populations with various pathologies. Physical exercise produces specific autonomic nervous system (ANS) changes. There has been no comprehensive profiling of cardiovascular function during exercise or simultaneous characterisation of the influence of exercise on cardiac ventricular function and electrical activity. This work aims to address that, using a combination of physiological parameters. Between-lead agreement for ambulatory electrocardiographic (EGG) depolarisation-repolarisation (QT) interval was quantified during rest and exercise. In contrast to cardiac interval (RR) data, between-lead bias and limits of agreement for QT interval data should be quantified when reporting results from an ambulatory EGG system and between-gender QT differences should also be accounted for. EGG electrode location appears to significantly affect QT-RR hysteresis, the shortening of the post-exercise QT interval relative to that at similar heart rates during exercise or pre-exercise rest, further emphasising the need for standardisation of EGG electrode placement. Sample entropy (SampEn) measures data complexity. Few studies have compared SampEn of RR data (SampEn-RR) during exercise, whilst none have examined SampEn for the corresponding QT interval (SampEn-QT). Fractal analysis assesses data correlation and scaling structures. Detrended fluctuation analysis (DFA) provides a scaling exponent (a) which describes these properties. This has not been quantified for RR interval data during post-exercise recovery and has not been reported for QT interval data. Differences in a magnitudes for RR and QT data suggest that these quantities have different fractal properties. Exercise perturbs the resting QT-RR relationship via hysteresis. The QT variability index (QTVI) quantifies the relative autonomic influence on the atrial and ventricular myocardium during rest and exercise. QTVI is a consistent measure of cardiac ventricular function and as such appears to be a more useful index than other parameters based on RR or QT interval alone

RSV sequence variation and within-host minority variant dynamics

Respiratory syncytial virus (RSV) infection is a common disease that causes the most severe disease in the extremes of age. Parts of the RSV genome are extremely variable, however, origination of genomic variation of RSV is not studied very well. Epidemiology studies have shown rapidly changing RSV strains and grouped these in genotypes based on a part of the RSV genome that consists of the (partial) G gene. In this thesis, the genotyping system was inspected and it showed that the part of the G gene previously used for genotyping did not contain enough information to reliably determine which genotype a strain belonged to. Phylogenetic analysis was performed to determine the necessary and sufficient part of the genome to determine the genotype reliably, which was full G. Other proteins were investigated for variability as well and both F and L carried plenty of variation as well. The amount of variation within a patient has been understudied. Therefore, a new method was optimised to detect the prevalence of minority variations in clinical samples. The prevalence of minority variants was examined in a community cohort and hospital cohort from season 2015-2016 of which all samples were spatiotemporally and age-matched. The detected genotypes were GA2 and ON1. Most clinical samples in this study did carry minority variants, however, there was no difference in the amount of variation between community and hospital samples. The gene that displayed the most variations per nucleotide, and most non-synonymous variations was G. This research also demonstrates that these variations can be transmitted or develop during acute infection. Consecutive samples from volunteers inoculated with a known RSV strain showed that both synonymous and non-synonymous variations can occur and their frequency can increase, decrease or remain stable over time. The F gene rarely developed non-synonymous variations in this study.Open Acces

Assessing Variability of EEG and ECG/HRV Time Series Signals Using a Variety of Non-Linear Methods

Time series signals, such as Electroencephalogram (EEG) and Electrocardiogram (ECG) represent the complex dynamic behaviours of biological systems. The analysis of these signals using variety of nonlinear methods is essential for understanding variability within EEG and ECG, which potentially could help unveiling hidden patterns related to underlying physiological mechanisms. EEG is a time varying signal, and electrodes for recording EEG at different positions on the scalp give different time varying signals. There might be correlation between these signals. It is important to know the correlation between EEG signals because it might tell whether or not brain activities from different areas are related. EEG and ECG might be related to each other because both of them are generated from one co-ordinately working body. Investigating this relationship is of interest because it may reveal information about the correlation between EEG and ECG signals. This thesis is about assessing variability of time series data, EEG and ECG, using variety of nonlinear measures. Although other research has looked into the correlation between EEGs using a limited number of electrodes and a limited number of combinations of electrode pairs, no research has investigated the correlation between EEG signals and distance between electrodes. Furthermore, no one has compared the correlation performance for participants with and without medical conditions. In my research, I have filled up these gaps by using a full range of electrodes and all possible combinations of electrode pairs analysed in Time Domain (TD). Cross-Correlation method is calculated on the processed EEG signals for different number unique electrode pairs from each datasets. In order to obtain the distance in centimetres (cm) between electrodes, a measuring tape was used. For most of our participants the head circumference range was 54-58cm, for which a medium-sized I have discovered that the correlation between EEG signals measured through electrodes is linearly dependent on the physical distance (straight-line) distance between them for datasets without medical condition, but not for datasets with medical conditions. Some research has investigated correlation between EEG and Heart Rate Variability (HRV) within limited brain areas and demonstrated the existence of correlation between EEG and HRV. But no research has indicated whether or not the correlation changes with brain area. Although Wavelet Transformations (WT) have been performed on time series data including EEG and HRV signals to extract certain features respectively by other research, so far correlation between WT signals of EEG and HRV has not been analysed. My research covers these gaps by conducting a thorough investigation of all electrodes on the human scalp in Frequency Domain (FD) as well as TD. For the reason of different sample rates of EEG and HRV, two different approaches (named as Method 1 and Method 2) are utilised to segment EEG signals and to calculate Pearson’s Correlation Coefficient for each of the EEG frequencies with each of the HRV frequencies in FD. I have demonstrated that EEG at the front area of the brain has a stronger correlation with HRV than that at the other area in a frequency domain. These findings are independent of both participants and brain hemispheres. Sample Entropy (SE) is used to predict complexity of time series data. Recent research has proposed new calculation methods for SE, aiming to improve the accuracy. To my knowledge, no one has attempted to reduce the computational time of SE calculation. I have developed a new calculation method for time series complexity which could improve computational time significantly in the context of calculating a correlation between EEG and HRV. The results have a parsimonious outcome of SE calculation by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain. Time series analysis method has been utilised to study complex systems that appear ubiquitous in nature, but limited to certain dynamic systems (e.g. analysing variables affecting stock values). In this thesis, I have also investigated the nature of the dynamic system of HRV. I have disclosed that Embedding Dimension could unveil two variables that determined HRV

An atrioventricular node model incorporating autonomic tone

The response to atrial fibrillation (AF) treatment is differing widely among patients, and a better understanding of the factors that contribute to these differences is needed. One important factor may be differences in the autonomic nervous system (ANS) activity. The atrioventricular (AV) node plays an important role during AF in modulating heart rate. To study the effect of the ANS-induced activity on the AV nodal function in AF, mathematical modelling is a valuable tool. In this study, we present an extended AV node model that incorporates changes in autonomic tone. The extension was guided by a distribution-based sensitivity analysis and incorporates the ANS-induced changes in the refractoriness and conduction delay. Simulated RR series from the extended model driven by atrial impulse series obtained from clinical tilt test data were qualitatively evaluated against clinical RR series in terms of heart rate, RR series variability and RR series irregularity. The changes to the RR series characteristics during head-down tilt were replicated by a 10% decrease in conduction delay, while the changes during head-up tilt were replicated by a 5% decrease in the refractory period and a 10% decrease in the conduction delay. We demonstrate that the model extension is needed to replicate ANS-induced changes during tilt, indicating that the changes in RR series characteristics could not be explained by changes in atrial activity alone

Multivariate assessment of linear and non-linear causal coupling pathways within the central-autonomic-network in patients suffering from schizophrenia

Im Bereich der Zeitreihenanalyse richtet sich das Interesse zunehmend darauf, wie Einblicke in die Interaktions- und Regulationsprozesse von pathophysiologischen- und physiologischen Zuständen erlangt werden können. Neuste Fortschritte in der nichtlinearen Dynamik, der Informationstheorie und der Netzwerktheorie liefern dabei fundiertes Wissen über Kopplungswege innerhalb (patho)physiologischer (Sub)Systeme. Kopplungsanalysen zielen darauf ab, ein besseres Verständnis dafür zu erlangen, wie die verschiedenen integrierten regulatorischen (Sub)Systeme mit ihren komplexen Strukturen und Regulationsmechanismen das globale Verhalten und die unterschiedlichen physiologischen Funktionen auf der Ebene des Organismus beschreiben. Insbesondere die Erfassung und Quantifizierung der Kopplungsstärke und -richtung sind wesentliche Aspekte für ein detaillierteres Verständnis physiologischer Regulationsprozesse. Ziel dieser Arbeit war die Charakterisierung kurzfristiger unmittelbarer zentral-autonomer Kopplungspfade (top-to-bottom und bottom to top) durch die Kopplungsanalysen der Herzfrequenz, des systolischen Blutdrucks, der Atmung und zentraler Aktivität (EEG) bei schizophrenen Patienten und Gesunden. Dafür wurden in dieser Arbeit neue multivariate kausale und nicht-kausale, lineare und nicht-lineare Kopplungsanalyseverfahren (HRJSD, mHRJSD, NSTPDC) entwickelt, die in der Lage sind, die Kopplungsstärke und -richtung, sowie deterministische regulatorische Kopplungsmuster innerhalb des zentralen-autonomen Netzwerks zu quantifizieren und zu klassifizieren. Diese Kopplungsanalyseverfahren haben ihre eigenen Besonderheiten, die sie einzigartig machen, auch im Vergleich zu etablierten Kopplungsverfahren. Sie erweitern das Spektrum neuartiger Kopplungsansätze für die Biosignalanalyse und tragen auf ihre Weise zur Gewinnung detaillierter Informationen und damit zu einer verbesserten Diagnostik/Therapie bei. Die Hauptergebnisse dieser Arbeit zeigen signifikant schwächere nichtlineare zentral-kardiovaskuläre und zentral-kardiorespiratorische Kopplungswege und einen signifikant stärkeren linearen zentralen Informationsfluss in Richtung des Herzkreislaufsystems auf, sowie einen signifikant stärkeren linearen respiratorischen Informationsfluss in Richtung des zentralen Nervensystems in der Schizophrenie im Vergleich zu Gesunden. Die detaillierten Erkenntnisse darüber, wie die verschiedenen zentral-autonomen Netzwerke mit paranoider Schizophrenie assoziiert sind, können zu einem besseren Verständnis darüber führen, wie zentrale Aktivierung und autonome Reaktionen und/oder Aktivierung in physiologischen Netzwerken unter pathophysiologischen Bedingungen zusammenhängen.In the field of time series analysis, increasing interest focuses on insights gained how the coupling pathways of regulatory mechanisms work in healthy and ill states. Recent advances in non-linear dynamics, information theory and network theory lead to a new sophisticated body of knowledge about coupling pathways within (patho)physiological (sub)systems. Coupling analyses aim to provide a better understanding of how the different integrated physiological (sub)systems, with their complex structures and regulatory mechanisms, describe the global behaviour and distinct physiological functions at the organism level. In particular, the detection and quantification of the coupling strength and direction are important aspects for a more detailed understanding of physiological regulatory processes. This thesis aimed to characterize short-term instantaneous central-autonomic-network coupling pathways (top-to-bottom and bottom to top) by analysing the coupling of heart rate, systolic blood pressure, respiration and central activity (EEG) in schizophrenic patients and healthy participants. Therefore, new multivariate causal and non-causal linear and non-linear coupling approaches (HRJSD, mHRJSD, NSTPDC) that are able to determine the coupling strength and direction were developed. Whereby, the HRJSD and mHRJSD approaches allow the quantification and classification of deterministic regulatory coupling patterns within and between the cardiovascular- the cardiorespiratory system and the central-autonomic-network were developed. These coupling approaches have their own unique features, even as compared to well-established coupling approaches. They expand the spectrum of novel coupling approaches for biosignal analysis and thus contribute in their own way to detailed information obtained, and thereby contribute to improved diagnostics/therapy. The main findings of this thesis revealed significantly weaker non-linear central-cardiovascular and central-cardiorespiratory coupling pathways, and significantly stronger linear central information flow in the direction of the cardiac- and vascular system, and a significantly stronger linear respiratory information transfer towards the central nervous system in schizophrenia in comparison to healthy participants. This thesis provides an enhanced understanding of the interrelationship of central and autonomic regulatory mechanisms in schizophrenia. The detailed findings on how variously-pronounced, central-autonomic-network pathways are associated with paranoid schizophrenia may enable a better understanding on how central activation and autonomic responses and/or activation are connected in physiology networks under pathophysiological conditions