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Dysregulation of the autonomic cardiovascular control is a complication of diseases including diabetes, hypertension, sleep apnea, and aging. A common factor in these conditions is an increase in reactive oxygen species (ROS) in neural, cardiac, and endothelial tissues. Cu/Zn superoxide dismutase (SOD1) is an intracellular anti-oxidant enzyme that catalyzes dismutation of the superoxide anion (O2.-) to hydrogen peroxide (H2O2). Expression and function of this enzyme are diminished in pathologies that impair cardiovascular autonomic control. This study employed mice overexpressing a transgene for human SOD1 (hSOD1) to determine if its overexpression would alter autonomic regulation of BP, HR, and BRS in healthy animals, and if this animal line (C57B6SJL-Tg (SOD1)2 Gur/J) could be used in future studies to determine if hSOD1 overexpression can preserve cardiac autonomic function in disease models. To accomplish this aim, using anesthetized SOD1 and C57 (control) mice, we recorded HR, and aortic depressor nerve (ADN) activity changes in response to pharmacologically-induced BP changes in order to measure baroreflex and baroreceptor sensitivity, respectively. In order to identify any alterations in central, efferent, and cardiac components of the baroreflex arc, we electrically stimulated the left ADN and left cervical vagus and compared the reductions in BP and HR between the C57 and SOD1 mice. Time- and frequency-domain analysis of heart rate variability (HRV) was performed using pulse pressure recordings prior to pharmacologic or surgical procedures. We found that hSOD1 overexpression in the SOD1 mouse line, in comparison to C57 controls did not significantly affect resting HR (C57: 558 ± 8 vs. SOD1:553 ± 13 beats-per-minute) or blood pressure (C57: 88.8 ± 2.9 vs.SOD1: 85.8 ± 2.1 mmHg). hSOD1 overexpression did not affect the decrease in average mean arterial pressure (MAP) following injection of sodium nitroprusside (SNP) (C57: 38.7 ± 1.4 vs. SOD1: 39.5 ± 1.3 mmHg) or increase in average MAP (C57: 135.8 ± 3.1 vs. SOD1: 136.6 ± 3.5 mmHg) following injection of phenylephrine (PE). BRS, as measured by the averaged regression lines for ΔHR/ΔMAP for the SNP-induced tachycardic baroreflex (C57: 0.57 ± 0.06 bpm/mmHg, SOD1: 0.61 ± 0.08 bpm/mmHg)) and the PE-induced bradycardic baroreflex (C57: -2.9 ± 0.57 bmp/mmHg, SOD1: -4.3 ± 0.84 bpm/mmHg) are not significantly different between C57 and SOD1. Baroreceptor activation showed a significant increase in gain (C57: 5.4 ± 0.3 vs. SOD1: 7.4 ± 0.5 %/mmHg, P < 0.01) in the SOD1 transgenic mice. Heart rate depression in response to electrical stimulation of the left ADN and cervical vagus was comparable between C57 and SOD1, though MAP reduction in response to ADN stimulation is slightly, but significantly increased at 50 Hz in SOD1 animals. Time- domain analysis of HRV did not reveal any significant difference in beat-to-beat variability between SOD1 and C57 (SDNN: C57: 2.78 ± 0.20, SOD1: 2.89 ± 0.27), although frequency-domain analysis uncovered a significant reduction in the low-frequency power component of the HRV power spectral distribution (C57: 1.19 ± 0.11, SOD1: 0.35 ± 0.06, P < 0.001). This study shows that although hSOD1 overexpression does not affect overall baroreflex function, it does potentiate baroreceptor sensitivity and brain stem control of arterial pressure, and reduces low-frequency beat-to-beat variations in HR, without affecting total HRV.

Heart Rate Variability (HRV) analysis has been garnering attention from researchers due to its wide range of applications. Medical researchers have always been interested in Heart Rate (HR) and HRV analysis, but nowadays, investigators from variety of other fields are also probing the subject. For instance, variation in HR and HRV is connected to emotional arousal. Therefore, knowledge from the fields of affective computing and psychology, can be employed to devise machines that understand the emotional states of humans. Recent advancements in non-contact HR and HRV measurement techniques will likely further boost interest in emotional estimation through . Such measurement methods involve the extraction of the photoplethysmography (PPG) signal from the human's face through a camera. The latest approaches apply Independent Component Analysis (ICA) on the color channels of video recordings to extract a PPG signal. Other investigated methods rely on Eulerian Video Magnification (EVM) to detect subtle changes in skin color associated with PPG. The effectiveness of the EVM in HR estimation has well been established. However, to the best of our knowledge, EVM has not been successfully employed to extract HRV feature from a video of a human face. In contrast, ICA based methods have been successfully used for HRV analysis. As we demonstrate in this thesis, these two approaches for HRV feature extraction are highly sensitive to noise. Hence, when we evaluated them in indoor settings, we obtained mean absolute error in the range of 0.012 and 28.4. Therefore, in this thesis, we present two approaches to minimize the error rate when estimating physiological measurements from recorded facial videos using a standard camera. In our first approach which is based on the EVM method, we succeeded in extracting HRV measurements but we could not get rid of high frequency noise, which resulted in a high error percentage for the result of the High frequency (HF) component. Our second proposed approach solved this issue by applying ICA on the red, green and blue (RGB) colors channels and we were able to achieve lower error rates and less noisy signal as compared to previous related works. This was done by using a Buterworth filter with the subject's specific HR range as its Cut-Off. The methods were tested with 12 subjects from the DISCOVER lab at the University of Ottawa, using artificial lights as the only source of illumination. This made it a challenge for us because artificial light produces HF signals which can interfere with the PPG signal. The final results show that our proposed ICA based method has a mean absolute error (MAE) of 0.006, 0.005, 0.34, 0.57 and 0.419 for the mean HR, mean RR, LF, HF and LF/HF respectively. This approach also shows that these physiological parameters are highly correlated with the results taken from the electrocardiography (ECG).

Physiological indicators like heart rate (HR) and its variability (HRV) from ECG (electrocardiograms), and frontal lobe alpha power asymmetry (AA) and frontoparietal connectivity from EEG (electroencephalograms), can elucidate the role of the nervous system and other visceral organs and their effects on behavioral measures of cognitive and emotional self-regulation. Knowledge of the intergenerational transmission of cardiac and cerebral physiology can provide insight as to the developmental patterns of the organization and stabilization of these physiological processes in children and their mothers. The current study addresses a key question: Is there a developmental shift from 3-9 years of age in the overall pattern of EEG and ECG similarity between children and their mothers? The hypothesis was that there would be increasing child-mother similarity with age. EEG and ECG physiology was examined during a resting-state baseline period, during completion of cognitive tasks, and as baseline-to-task changes in EEG AA and frontoparietal coherence, and ECG HR and HRV in children and their mothers. A socioeconomically diverse longitudinal sample of 171 mothers with their children at ages 3, 6, and 9 years completed questionnaires and laboratory visits. Results indicated that there was some evidence to suggest the presence of mother-child similarity. Twenty of the seventy-two estimated intraclass correlations were significant. Furthermore, of the 20 significant correlations overall, none were present at child age 3 years, 6 were significant at child age 6 years, and 14 were significant at child age 9 years. Thus, overall, there was evidence that by age 6 years, child-mother similarity in physiological indicators of SR had begun to emerge. Additionally, consistent with the study hypothesis, there was some evidence of a pattern of increasing similarity for certain physiological indicators. Of the 72 estimated age-difference Fisher tests for increasing similarity, 17 were significant and in the hypothesized direction. The greatest number were seen during the task condition for ages 6 and 9, and particularly for the frontoparietal EEG variables. Findings are interpreted in light of social learning and behavioral genetics theories.

Thesis advisor: Elizabeth Kensinger / Individuals with social anxiety disorder (SAD) tend to have emotional memory biases in the encoding and retrieval of social memories. Research has shown reduced heart rate variability (HRV) in clinical populations suffering from anxiety, including social anxiety. Heightened sympathetic activation—as measured by the electrodermal activity (EDA)—has also been associated with anxiety disorders. The aim of the present study was to examine the relation between HRV, social anxiety, and re-experiencing of emotional autobiographical memories. 44 healthy young adults were recruited from the Boston College campus through SONA. Participants were given an online survey that instructed them to retrieve 40 specific events from the past in response to 40 socially relevant cues. For each event, participants were instructed to provide a brief narrative, make several ratings for the event (on a scale from 1-7), and indicate the specific emotions they experienced both at the time of retrieval and of the event. Approximately one month after the completion of the memory survey, participants engaged in a 2-hour memory retrieval session while undergoing psychophysiological monitoring (heart rate, skin conductance, and respiration). Following the retrieval task, participants completed self-report questionnaires of social anxiety symptom severity and trait emotion regulation strategy (i.e., tendency to reappraise or suppress emotions). The present study found that positive memories had higher re-experiencing ratings as compared to negative memories. Contrary to the original study hypothesis, however, there was no significant interaction between average re-experiencing (or arousal) ratings of positive or negative social autobiographical memories and SAD likelihood. A nonlinear, cubic relationship was found between one of three metrics of HRV and social anxiety symptom severity. A significant effect was found between skin conductance and SAD likelihood, which was likely driven by an almost significance difference in skin conductance between the SAD unlikely and the SAD very probable groups; these findings provide further insight into the relationship between autonomic nervous system (ANS) functioning and social anxiety. Further, the present results suggest the intriguing possibility that there may be a nonlinear relationship between HRV and severity of social anxiety. Future research with a larger sample size is needed to corroborate these findings. / Thesis (BS) — Boston College, 2018. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Psychology.

The electro-optic technique of measuring the cardiovascular pulse wave known as photoplethysmography (PPG) is clinically utilised for noninvasive characterisation of physiological components by dynamic monitoring of tissue optical absorption. There has been a resurgence of interest in this technique in recent years, driven by the demand for a low cost, compact, simple and portable technology for primary care and community-based clinical settings, and the advancement of computer-based pulse wave analysis techniques. PPG signal provides a means of determining cardiovascular properties during the cardiac cycle and changes with ageing and disease. This thesis focuses on the photoplethysmographic signal for cardiovascular assessment. The contour of the PPG pulse wave is influenced by vascular ageing. Contour analysis of the PPG pulse wave provides a rapid means of assessing vascular tone and arterial stiffness. In this thesis, the parameters extracted from the PPG pulse wave are examined in young adults. The results indicate that the contour parameters of the PPG pulse wave could provide a simple and noninvasive means to study the characteristic change relating to arterial stiffness. The pulsatile component of the PPG signal is due to the pumping action of the heart, and thus could reveal the circulation changes of a specific vascular bed. Heart rate variability (HRV) represents one of the most promising quantitative markers of cardiovascular control. Calculation of HRV from the peripheral pulse wave using PPG, called pulse rate variability (PRV), is investigated. The current work has confirmed that the PPG signal could provide basic information about heart rate (HR) and its variability, and highly suggests a good alternative to understanding dynamics pertaining to the autonomic nervous system (ANS) without the use of an electrocardiogram (ECG) device. Hence, PPG measurement has the potential to be readily accepted in ambulatory cardiac monitoring due to its simplicity and comfort. Noncontact PPG (NPPG) is introduced to overcome the current limitations of contact PPG. As a contactless device, NPPG is especially attractive for physiological monitoring in ambulatory units, NICUs, or trauma centres, where attaching electrodes is either inconvenient or unfeasible. In this research, a prototype for noncontact reflection PPG (NRPPG) with a vertical cavity surface emitting laser (VCSEL) as a light source and a high-speed PiN photodiode as a photodetector is developed. The results from physiological experiments suggest that NRPPG is reliable to extract clinically useful information about cardiac condition and function. In summary, recent evidence demonstrates that PPG as a simple noninvasive measurement offers a fruitful avenue for noninvasive cardiovascular monitoring. Key words: Photoplethysmography (PPG), Cardiovascular assessment, Pulse wave contour analysis, Arterial stiffness, Heart rate (HR), Heart rate variability (HRV), Pulse rate variability (PRV), Autonomic nervous system (ANS), Electrocardiogram (ECG).

Einleitung: Es wird angenommen, dass die zyklusbedingten, hormonellen Änderungen die vegetative Ansteuerung des Herzens bei normotensiven Frauen beeinflussen. Die Herzfrequenzvariabilität (HRV) stellt einen der am häufigsten untersuchten, nicht-invasiven Parameter des Herz-Kreislauf-Systems dar. Deshalb war es das Ziel dieser Studie, den Verlauf der HRV Parameter bei ausdauertrainierten als auch untrainierten normotensiven Frauen in Abhängigkeit vom Menstruationszyklus zu untersuchen. Methode: Normotensive, untrainierte als auch trainierte Frauen nahmen an der Studie teil. Die Athletinnen absolvierten individuell abgestimmte Trainingspläne (>5h/Woche) während der Studie. Die HRV Messungen wurden in den folgenden fünf Zyklusphasen aufgezeichnet: In der Menstruation (M), der Mitte der Follikel- (MidF), der Ovulations- (O), der Mitte der Luteal- (MidL) und der Pre-Menstruationsphase (PreM). Die Basaltemperatur als auch die Hormonanalysen des Luteinisierenden (LH) und des Follikelstimulierenden Hormons (FSH), des β-17 Östrogens (E2) und des Progesterons (P) dienten der Verifizierung der Zyklusphasen. Die HRV Messungen wurden bei Spontanatmung im Liegen (20 min) wie auch während eines Orthosthase Tests aufgezeichnet. Parameter der Zeit als auch der Frequenzdomäne für Kurzzeitmessungen wurden ausgewertet. Resultate: Alle Frauen hatten einen normotensiven Menstruationszyklus mit typischen hormonellen Schwankungen und einem signifikanten Verlauf (p / Introduction: The autonomic control of the heart is assumed to be affected by endogenous hormonal fluctuations in normal ovulatory females. Analyzing heart rate variability (HRV) had become a tool for the noninvasive measurement of cardiac autonomic control. The purpose of the present study was to investigate the course of the HRV parameters in moderately active as well as in long time endurance trained women during the menstrual cycle. Methods: Normal ovulatory females, untrained and trained were enrolled. Female athletes were involved in individually different training patterns (>5h/week) during the study. HRV recordings were obtained during five different menstrual cycle phases: menstruation (M), middle of follicular (MidF), ovulation (O), middle of luteal (MidL) and pre menstruation phase (PreM). Phases were verified by basal body temperature and analysis of luteinizing hormone (LH), follicular stimulation hormone (FSH), β-17 estrogen (E2) and progesterone (P). HRV measurements took place at subjects’ spontaneous breathing frequency in supine position (20 min) as well as during an orthostatic test. Parameters of short-term recording were calculated in time and frequency domain. Results: All women had normal ovulatory menstrual cycles including typical endogenous hormonal fluctuations; levels of LH, FSH, E2 and P were significantly different (p

This thesis deals with evaluation EKG in terms of classification rhythm and analysis HRV. In theoretic part of work are described basics of heart physiology and its usual pathology, basics of electrocardiography, evaluation EKG and standard methods of HRV evaluation. In practical part are described algorithms used in created application. Mainly describes technique of rhythm evaluation, ectopic rhythms and delineation error elimination, data preparing for HRV evaluation, drift removal from DES and HRV evaluation methods. Created program was tested on CSE and MIT- BIH database records. For lack of suitable data and absence of tested data, it wasn’t possible to test all the classification rules of used algorithms. Tested part of program appears reliable and functional.

For most of the naturally occurring signals, especially biomedical signals, the underlying physical process generating the signal is often not fully known, making it difficult to obtain a parametric model. Therefore, signal processing techniques are used to analyze the signal for non-parametrically characterizing the underlying system from which the signals are produced. Most of the real life systems are nonlinear and time varying, which poses a challenge while characterizing them. Additionally, multiple sensors are used to extract signals from such systems, resulting in multichannel signals which are inherently coupled. In this thesis, we counter this challenge by using Recurrence Plot based techniques for characterizing biomedical systems such as heart or brain, using signals such as heart rate variability (HRV), electroencephalogram(EEG) or functional magnetic resonance imaging (fMRI), respectively, extracted from them. In time series analysis, it is well known that a system can be represented by a trajectory in an N-dimensional state space, which completely represents an instance of the system behavior. Such a system characterization has been done using dynamical invariants such as correlation dimension, Lyapunov exponent etc. Takens has shown that when the state variables of the underlying system are not known, one can obtain a trajectory in ‘phase space’ using only the signals obtained from such a system. The phase space trajectory is topologically equivalent to the state space trajectory. This enables us to characterize the system behavior from only the signals sensed from them. However, estimation of correlation dimension, Lyapunov exponent, etc, are vulnerable to non-stationarities in the signal and require large number of sample points for accurate computation, both of which are important in the case of biomedical signals. Alternatively, a technique called Recurrence Plots (RP) has been proposed, which addresses these concerns, apart from providing additional insights. Measures to characterize RPs of single and two channel data are called Recurrence Quantification Analysis (RQA) and cross RQA (CRQA), respectively. These methods have been applied with a good measure of success in diverse areas. However, they have not been studied extensively in the context of experimental biomedical signals, especially multichannel data. In this thesis, the RP technique and its associated measures are briefly reviewed. Using the computational tools developed for this thesis, RP technique has been applied on select single channel, multichannel and multimodal (i.e. multiple channels derived from different modalities) biomedical signals. Connectivity analysis is demonstrated as post-processing of RP analysis on multichannel signals such as EEG and fMRI. Finally, a novel metric, based on the modification of a CRQA measure is proposed, which shows improved results. For the case of single channel signal, we have considered a large database of HRV signals of 112 subjects recorded for both normal and abnormal (anxiety disorder and depression disorder) subjects, in both supine and standing positions. Existing RQA measures, Recurrence Rate and Determinism, were used to distinguish between normal and abnormal subjects with an accuracy of 58.93%. A new measure, MLV has been introduced, using which a classification accuracy of 98.2% is obtained. Correlation between probabilities of recurrence (CPR) is a CRQA measure used to characterize phase synchronization between two signals. In this work, we demonstrate its utility with application to multimodal and multichannel biomedical signals. First, for the multimodal case, we have computed running CPR (rCPR), a modification proposed by us, which allows dynamic estimation of CPR as a function of time, on multimodal cardiac signals (electrocardiogram and arterial blood pressure) and demonstrated that the method can clearly detect abnormalities (premature ventricular contractions); this has potential applications in cardiac care such as assisted automated diagnosis. Second, for the multichannel case, we have used 16 channel EEG signals recorded under various physiological states such as (i) global epileptic seizure and pre-seizure and (ii) focal epilepsy. CPR was computed pair-wise between the channels and a CPR matrix of all pairs was formed. Contour plot of the CPR matrix was obtained to illustrate synchronization. Statistical analysis of CPR matrix for 16 subjects of global epilepsy showed clear differences between pre-seizure and seizure conditions, and a linear discriminant classifier was used in distinguishing between the two conditions with 100% accuracy. Connectivity analysis of multichannel EEG signals was performed by post-processing of the CPR matrix to understand global network-level characterization of the brain. Brain connectivity using thresholded CPR matrix of multichannel EEG signals showed clear differences in the number and pattern of connections in brain connectivity graph between epileptic seizure and pre-seizure. Corresponding brain headmaps provide meaningful insights about synchronization in the brain in those states. K-means clustering of connectivity parameters of CPR and linear correlation obtained from global epileptic seizure and pre-seizure showed significantly larger cluster centroid distances for CPR as opposed to linear correlation, thereby demonstrating the efficacy of CPR. The headmap in the case of focal epilepsy clearly enables us to identify the focus of the epilepsy which provides certain diagnostic value. Connectivity analysis on multichannel fMRI signals was performed using CPR matrix and graph theoretic analysis. Adjacency matrix was obtained from CPR matrices after thresholding it using statistical significance tests. Graph theoretic analysis based on communicability was performed to obtain community structures for awake resting and anesthetic sedation states. Concurrent behavioral data showed memory impairment due to anesthesia. Given the fact that previous studies have implicated the hippocampus in memory function, the CPR results showing the hippocampus within the community in awake state and out of it in anesthesia state, demonstrated the biological plausibility of the CPR results. On the other hand, results from linear correlation were less biologically plausible. In biological systems, highly synchronized and desynchronized systems are of interest rather than moderately synchronized ones. However, CPR is approximately a monotonic function of synchronization and hence can assume values which indicate moderate synchronization. In order to emphasize high synchronization/ desynchronization and de-emphasize moderate synchronization, a new method of Correlation Synchronization Convergence Time (CSCT) is proposed. It is obtained using an iterative procedure involving the evaluation of CPR for successive autocorrelations until CPR converges to a chosen threshold. CSCT was evaluated for 16 channel EEG data and corresponding contour plots and histograms were obtained, which shows better discrimination between synchronized and asynchronized states compared to the conventional CPR. This thesis has demonstrated the efficacy of RP technique and associated measures in characterizing various classes of biomedical signals. The results obtained are corroborated by well known physiological facts, and they provide physiologically meaningful insights into the functioning of the underlying biological systems, with potential diagnostic value in healthcare.

The aim of this thesis is to describe the variability of cardiac rhythm and familiarity with the methods of the analysis, ie by monitoring changes in heart rhythm electrogram signal recording and using the methods in the time domain using recurrent diagram. The work describes the quantification of the methods and possibilities of quantifiers in the evaluation of heart rate variability analysis. It also describes the clinical significance of heart rate variability and diagnostic capabilities changes of heart rate variability caused by ischemic heart disease. The practical part describes how to create applications in Matlab to calculate the quantifiers analysis of heart rate variability in the time domain using recurrent diagram. The calculation was made of the positions R wave elektrogram signal isolated rabbit hearts. The calculated values of quantifiers both methods were statistically evaluated and discussed.