Cardiorespiratory Dynamic Response to Mental Stress: A Multivariate Time-Frequency Analysis

Mental stress is a growing problem in our society. In order to deal with this, it is important to understand the underlying stress mechanisms. In this study, we aim to determine how the cardiorespiratory interactions are affected by mental arithmetic stress and attention. We conduct cross time-frequency (TF) analyses to assess the cardiorespiratory coupling. In addition, we introduce partial TF spectra to separate variations in the RR interval series that are linearly related to respiration from RR interval variations (RRV) that are not related to respiration. The performance of partial spectra is evaluated in two simulation studies. Time-varying parameters, such as instantaneous powers and frequencies, are derived from the computed spectra. Statistical analysis is carried out continuously in time to evaluate the dynamic response to mental stress and attention. The results show an increased heart and respiratory rate during stress and attention, compared to a resting condition. Also a fast reduction in vagal activity is noted. The partial TF analysis reveals a faster reduction of RRV power related to (3 s) than unrelated to (30 s) respiration, demonstrating that the autonomic response to mental stress is driven by mechanisms characterized by different temporal scales

Cardiorespiratory Dynamics: Algorithms and Application to Mental StressMonitoring

The rate at which our heart beats, is a dynamical process enabling adaptive changes according to the demands of our body. These variations in heart rate are widely studied in so-called heart rate variability (HRV) analyses, as they contain much information about the activity of our autonomic nervous system. Variability in the heart rate arises from several processes, such as thermoregulation, hormones, arterial blood pressure, respiration, etc. One of the main short-term modulators of the heart rate is respiration. This phenomenon is called respiratory sinus arrhythmia (RSA) and comprises the rhythmic fluctuation of the heart rate at respiratory frequency. It has also widely been used as an index of vagal outflow. However, this has been widely debated as some studies have shown that the magnitude of RSA changes with respiratory rate and the depth of breathing, independently of parasympathetic activity. It is therefore questioned whether RSA represents a true index of vagal outflow. The lack of consensus on the precise mechanisms that are responsible for this cardiorespiratory interaction, lead to interpretational problems. It is nevertheless apparent that it is important to include information of respiration when interpretations of HRV studies are conducted. Inspired by the polemic nature of this debate on the interpretation of RSA, this dissertation focuses on three topics. The first part of the thesis deals with the development of a surrogate respiratory signal based on ECG recordings. This is termed ECG-derived respiration (EDR). It is an important topic to cope retrospectively with possible confounding respiratory parameters in HRV studies without separate respiratory recordings. Additionally, with the trend towards less obtrusive and more cost-efficient monitoring, the possibility to obtain reliable EDR signals would discard the need to separately record respiration using specialized equipment, that often also interferes with natural breathing. In this dissertation, a new algorithm is proposed for single lead ECGs and compared with state-of-the-art EDR methods. The second focus of the thesis is closely related to the interpretational problems concerning RSA and cardiac vagal activity. As such, the aim is to separate the tachogram in two components: one that is strictly related to respiration, and another component that is unrelated to respiration. Several methods to realize this separation have been proposed in the literature, and an extensive comparison is conducted in this thesis. Additionally, a separation method based on partial time-frequency analyses is discussed. It has the advantage over other methods that it can deal with nonstationary signals. The last part of this dissertation focuses on the characterization of common dynamics in HRV and respiration. It is well-known that the interactions that play in the cardiorespiratory system are complex. Although the common dynamics have been studied in the literature using techniques like synchronization, symbolic dynamics and coupled oscillators, the precise mechanisms are still unclear. Therefore, we aim to characterize the common dynamics in a different way in order to gain more insight in the underlying cardiorespiratory mechanisms and their interpretation. In particular, information-theoretic measures that quantify the information storage and internal information of HRV, and the information transfer and cross information from respiration to HRV are discussed. Throughout this dissertation, special attention is also paid to the application of mental stress monitoring. It has been found that persons who are chronically stressed, have an increased risk for cardiovascular diseases. Also breathing plays an important role, as research suggests that respiration can be used as an interface to deal with negative effects of mental stress, and thus alter cardiac autonomic activity. This makes mental stress an interesting application on which the impact of the last two topics is evaluated.nrpages: 206status: publishe

Stress classification by separation of respiratory modulations in heart rate variability using orthogonal subspace projection

The influence of respiration on the heart rate is a phenomenon known as respiratory sinus arrhythmia. However, effects of respiration are often ignored in studies of heart rate variability. In this paper, we take respiratory influences into account by separating the tachogram in two components, one related to respiration and one residual component, using orthogonal subspace projection. We demonstrate that it is important to remove respiratory influences during classification of rest and mental stress. Using merely the original tachogram, the classification accuracy is 57.13%, while the use of the residual tachogram results in an almost perfect classification (accuracy = 97.88%).PubMedID 24111137status: publishe

Cardiovascular Autonomic Adaptation in Lunar and Martian Gravity during Parabolic Flight

PURPOSE: Weightlessness has a well-known effect on the autonomic control of the cardiovascular system. With future missions to Mars in mind, it is important to know what the effect of partial gravity is on the human body. We aim to study the autonomic response of the cardiovascular system to partial gravity levels, as present on the Moon and on Mars, during parabolic flight. METHODS: ECG and blood pressure were continuously recorded during parabolic flight. A temporal analysis of blood pressure and heart rate to changing gravity was conducted to study the dynamic response. In addition, cardiovascular autonomic control was quantified by means of heart rate (HR) and blood pressure (BP) variability measures. RESULTS: Zero and lunar gravity presented a biphasic cardiovascular response, while a triphasic response was noted during martian gravity. Heart rate and blood pressure are positively correlated with gravity, while the general variability of HR and BP, as well as vagal indices showed negative correlations with increasing gravity. However, the increase in vagal modulation during weightlessness is not in proportion when compared to the increase during partial gravity. CONCLUSIONS: Correlations were found between the gravity level and modulations in the autonomic nervous system during parabolic flight. Nevertheless, with future Mars missions in mind, more studies are needed to use these findings to develop appropriate countermeasures.status: publishe

Analysis of cardio-respiratory dynamics during mental stress using (partial) time-frequency spectra

Mental stress is a major problem in today's society. It is therefore important to determine the mechanisms underlying stress. In this paper, we aim at studying the cardio-respiratory response to mental stress using a nonparametric multivariate time-frequency approach. In addition, partial spectra are considered to separate RR interval variations (RRV) that can be related to respiration from RRV that are unrelated to respiration. The results confirm vagal withdrawal during mental stress and also reveal that the autonomic response to stress is driven by mechanisms both related and unrelated to respiration that are characterized by different response times. © 2013 CCAL.status: publishe

Un método mejorado de respiración derivado de ECG que utiliza análisis de componentes principales del núcleo

Recent studies show that principal component analysis (PCA) of heart beats generates well-performing ECG-derived respiratory signals (EDR). This study aims at improving the performance of EDR signals using kernel PCA (kPCA). Kernel PCA is a generalization of PCA where nonlinearities in the data are taken into account for the decomposition. The performance of PCA and kPCA is evaluated by comparing the EDR signals to the reference respiratory signal. Correlation coefficients of 0.630 ± 0.189 and 0.675 ± 0.163, and magnitude squared coherence coefficients at respiratory frequency of 0.819 ± 0.229 and 0.894 ± 0.139 were obtained for PCA and kPCA respectively. The Wilcoxon signed rank test showed statistically significantly higher coefficients for kPCA than for PCA for both the correlation (p = 0.0257) and coherence (p = 0.0030) coefficients. To conclude, kPCA proves to outperform PCA in the extraction of a respiratory signal from single lead ECGs

Cardiorespiratory dynamic response to mental stress : a multivariate time-frequency analysis

Mental stress is a growing problem in our society. In order to deal with this, it is important to understand the underlying stress mechanisms. In this study, we aim to determine how the cardiorespiratory interactions are affected by mental arithmetic stress and attention. We conduct cross time-frequency (TF) analyses to assess the cardiorespiratory coupling. In addition, we introduce partial TF spectra to separate variations in the RR interval series that are linearly related to respiration from RR interval variations (RRV) that are not related to respiration. The performance of partial spectra is evaluated in two simulation studies. Time-varying parameters, such as instantaneous powers and frequencies, are derived from the computed spectra. Statistical analysis is carried out continuously in time to evaluate the dynamic response to mental stress and attention. The results show an increased heart and respiratory rate during stress and attention, compared to a resting condition. Also a fast reduction in vagal activity is noted. The partial TF analysis reveals a faster reduction of RRV power related to (3 s) than unrelated to (30 s) respiration, demonstrating that the autonomic response to mental stress is driven by mechanisms characterized by different temporal scales.status: publishe

Phase-Rectified Signal Averaging to Evaluate ANS Development in Premature Infants

Aim: Heart Rate Variability (HRV) is determined by the autonomic nervous system (ANS) and a low value of this parameter is related to neurological pathologies and infants mortality. This study aims to assess the utility and the advantages of HRV analysis by means of phase-rectified signal averaging (PRSA), a technique that obtains curves that are useful to determine the development of the ANS in preterm infants, with less obtrusive monitoring compared to electroencephalography. Methods: For a preliminary study, 24-hour ECGs were taken in NICU at the University Hospital in Leuven, from 12 babies: 4 were term, 4 were born preterm but reached a term postmenstrual age, and 4 were preterm. Heart rate tracks of segments of 27 minutes were extracted and analyzed with the PRSA technique. The curves obtained were quantified by the slope and by an acceleration/deceleration related parameter (AC/DC). Two independent analyses on acceleration and deceleration were carried out to visualize the effects of the sympathetic and parasympathetic system separately. Moreover, the immediate response and the response after 5 seconds were taken into account. Results and Conclusion: All the results were compared and validated with traditional HRV parameters. The results of slope and AD/DC in both types of analysis are promising in providing a simple parameter to assess neurological development deficiency in order to allow faster and preventive intervention. Further studies are needed in a larger populatio

Phase-rectified signal averaging to evaluate ANS development in premature infants

Aim: Heart Rate Variability (HRV) is determined by the autonomic nervous system (ANS) and a low value of this parameter is related to neurological pathologies and infants mortality. This study aims to assess the utility and the advantages of HRV analysis by means of phase-rectified signal averaging (PRSA), a technique that obtains curves that are useful to determine the development of the ANS in preterm infants, with less obtrusive monitoring compared to electroencephalography. Methods: For a preliminary study, 24-hour ECGs were taken in NICU at the University Hospital in Leuven, from 12 babies: 4 were term, 4 were born preterm but reached a term postmenstrual age, and 4 were preterm. Heart rate tracks of segments of 27 minutes were extracted and analyzed with the PRSA technique. The curves obtained were quantified by the slope and by an acceleration/deceleration related parameter (AC/DC). Two independent analyses on acceleration and deceleration were carried out to visualize the effects of the sympathetic and parasympathetic system separately. Moreover, the immediate response and the response after 5 seconds were taken into account. Results and Conclusion: All the results were compared and validated with traditional HRV parameters. The results of slope and AD/DC in both types of analysis are promising in providing a simple parameter to assess neurological development deficiency in order to allow faster and preventive intervention. Further studies are needed in a larger population.status: publishe

Heart Beat Detection in Multimodal Data Using Signal Recognition and Beat Location Estimation

The tachogram is typically constructed by detecting the R peaks in the electrocardiogram (ECG). Sometimes the ECG is however very noisy, which makes it hard to find the R peaks in these cases by using only the ECG. Information from other signals can then be used in order to find the R peaks. In this paper, a method is suggested that is able to automatically detect signals with the same periodic behavior as the ECG. Heart beat labels of the detected signals are combined by using majority voting, heart beat location estimation and Hjorth's mobility parameter. The average performance was 99.95% for the training set and 85.62% for the last phase of the 2014 Computing in Cardiology challenge. If the available labels for the signals are used, the performance on the hidden test set was 86.61%.status: publishe