Heart Rate Variability Analysis Guided by Respiration in Major Depressive Disorder

In this study a Heart Rate Variability (HRV) analysis guided by respiration to evaluate different patterns of Autonomic Nervous System (ANS) in response to a cognitive stressor between Major Depressive Disorder (MDD) and control (CT) subjects is presented. Cardiorespiratory Time Frequency Coherence (TFC) reveals the local coupling of HRV and respiration signal which is essential and usually not included in estimation of ANS measures derived by HRV. Parasympathetic activity of ANS is measured as the power at the frequencies where TFC between HRV and respiration is significant, whereas sympathetic dominance is measured as the normalized power in the low frequency band [0.04,0.15] Hz of HRV excluding the power of those frequencies related to respiration. Results showed significantly lower (p <; 0.05) sympathetic dominance in MDD with respect to CT subjects during stress, suggesting that ANS reactivity as response to stress stimuli is lower in MDD patients. The study of ANS reactivity to a stressor may serve as a biomarker useful for the early diagnosis and monitoring of MDD patients

Autonomic nervous system biomarkers from multi-modal and model-based signal processing in mental health and illness

Esta tesis se centra en técnicas de procesado multimodal y basado en modelos de señales para derivar parámetros fisiológicos, es decir, biomarcadores, relacionados con el sistema nervioso autónomo (ANS). El desarrollo de nuevos métodos para derivar biomarcadores de ANS no invasivos en la salud y la enfermedad mental ofrece la posibilidad de mejorar la evaluación del estrés y la monitorización de la depresión. Para este fin, el presente documento se estructura en tres partes principales. En la Parte I, se proporciona unaintroducción a la salud y la enfermedad mental (Cap. 1). Además, se presenta un marco teórico para investigar la etiología de los trastornos mentales y el papel del estrés en la enfermedad mental (Cap. 2). También se destaca la importancia de los biomarcadores no invasivos para la evaluación del ANS, prestando especial atención en la depresión clínica (Cap. 3, 4). En la Parte II, se proporciona el marco metodológico para derivar biomarcadores del ANS. Las técnicas de procesado de señales incluyen el análisis conjunto de la variabilidad del rítmo cardíaco (HRV) y la señal respiratoria (Cap. 6), técnicas novedosas para derivar la señal respiratoria del electrocardiograma (ECG) (Cap. 7) y un análisis robusto que se basa en modelar la forma de ondas del pulso del fotopletismograma (PPG) (Ch. 8). En la Parte III, los biomarcadores del ANS se evalúan en la quantificacióndel estrés (Cap. 9) y en la monitorización de la depresión (Ch. 10).Parte I: La salud mental no solo está relacionada con ese estado positivo de bienestar, en el que un individuo puede enfrentar a las situaciones estresantes de la vida, sino también con la ausencia de enfermedad mental. La enfermedad o trastorno mental se puede definir como un trastorno emocional, cognitivo o conductual que causa un deterioro funcional sustancial en una o más actividades importantes de la vida. Los trastornos mentales más comunes, que muchas veces coexisten, son la ansiedad y el trastorno depresivo mayor (MDD). La enfermedad mental tiene un impacto negativo en la calidad de vida, ya que se asocia con pérdidas considerables en la salud y el funcionamiento, y aumenta ignificativamente el riesgo de una persona de padecer enfermedades ardiovasculares.Un instigador común que subyace a la comorbilidad entre el MDD, la patologíacardiovascular y la ansiedad es el estrés mental. El estrés es común en nuestra vida de rítmo rapido e influye en nuestra salud mental. A corto plazo, ANS controla la respuesta cardiovascular a estímulos estresantes. La regulación de parámetros fisiológicos, como el rítmo cardíaco, la frecuencia respiratoria y la presión arterial, permite que el organismo responda a cambios repentinos en el entorno. Sin embargo, la adaptación fisiológica a un fenómeno ambiental que ocurre regularmente altera los sistemas biológicos involucrados en la respuesta al estrés. Las alteraciones neurobiológicas en el cerebro pueden alterar lafunción del ANS. La disfunción del ANS y los cambios cerebrales estructurales tienen un impacto negativo en los procesos cognitivos, emocionales y conductuales, lo que conduce al desarrollo de una enfermedad mental.Parte II: El desarrollo de métodos novedosos para derivar biomarcadores del ANS no invasivos ofrece la posibilidad de mejorar la evaluacón del estrés en individuos sanos y la disfunción del ANS en pacientes con MDD. El análisis conjunto de varias bioseñales (enfoquemultimodal) permite la cuantificación de interacciones entre sistemas biológicos asociados con ANS, mientras que el modelado de bioseãles y el análisis posterior de los parámetros del modelo (enfoque basado en modelos) permite la cuantificación robusta de cambios en mecanismos fisiológicos relacionados con el ANS. Un método novedoso, quetiene en cuenta los fenómenos de acoplo de fase y frecuencia entre la respiración y las señales de HRV para evaluar el acoplo cardiorrespiratorio no lineal cuadrático se propone en el Cap. 6.3. En el Cap. 7 se proponen nuevas técnicas paramejorar lamonitorización de la respiración. En el Cap. 8, para aumentar la robustez de algunas medidas morfológicas que reflejan cambios en el tonno arterial, se considera el modelado del pulso PPG como una onda principal superpuesta con varias ondas reflejadas.Parte III: Los biomarcadores del ANS se evalúan en la cuantificación de diferentes tipos de estrés, ya sea fisiológico o psicológico, en individuos sanos, y luego, en la monitorización de la depresión. En presencia de estrés mental (Cap. 9.1), inducido por tareas cognitivas, los sujetos sanos muestran un incremento en la frecuencia respiratoria y un mayor número de interacciones no lineales entre la respiración y la seãl de HRV. Esto podría estar asociado con una activación simpática, pero también con una respiración menos regular. En presencia de estrés hemodinámico (Cap. 9.2), inducido por un cambio postural, los sujetos sanos muestran una reducción en el acoplo cardiorrespiratoriono lineal cuadrático, que podría estar relacionado con una retracción vagal. En presencia de estrés térmico (Cap. 9.3), inducido por la exposición a emperaturas ambientales elevadas, los sujetos sanos muestran un aumento del equilibrio simpatovagal. Esto demuestra que los biomarcadores ANS son capaces de evaluar diferentes tipos de estrés y pueden explorarse más en el contexto de la monitorización de la depresión. En el Cap. 10, se evalúan las diferencias en la función del ANS entre elMDD y los sujetos sanos durante un protocolo de estrés mental, no solo con los valores brutos de los biomarcadores del ANS, sino también con los índices de reactividad autónoma, que reflejan la capacidad deun individuo para afrontar con una situación desafiante. Los resultados muestran que la depresión se asocia con un desequilibrio autonómico, que se caracteriza por una mayor actividad simpática y una reducción de la distensibilidad arterial. Los índices de reactividad autónoma cuantificados por cambios, entre etapas de estrés y de recuperación, en los sustitutos de la rigidez arterial, como la pérdida de amplitud de PPG en las ondas reflejadas, muestran el mejor rendimiento en términos de correlación con el grado de la depresión, con un coeficiente de correlación r = −0.5. La correlación negativa implicaque un mayor grado de depresión se asocia con una disminución de la reactividadautónoma. El poder discriminativo de los biomarcadores del ANS se aprecia también por su alto rendimiento diagnóstico para clasificar a los sujetos como MDD o sanos, con una precisión de 80.0%. Por lo tanto, se puede concluir que los biomarcadores del ANS pueden usarse para evaluar el estrés y que la distensibilidad arterial deteriorada podría constituir un biomarcador de salud mental útil en el seguimiento de la depresión.This dissertation is focused on multi-modal and model-based signal processing techniques for deriving physiological parameters, i.e. biomarkers, related to the autonomic nervous system (ANS). The development of novel approaches for deriving noninvasive ANS biomarkers in mental health and illness offers the possibility to improve the assessment of stress and the monitoring of depression. For this purpose, the present document is structured in three main parts. In Part I, an introduction to mental health and illness is provided (Ch. 1). Moreover, a theoretical framework for investigating the etiology of mental disorders and the role of stress in mental illness is presented (Ch. 2). The importance of noninvasive biomarkers for ANS assessment, paying particular attention in clinical depression, is also highlighted (Ch. 3, 4). In Part II, themethodological framework for deriving ANS biomarkers is provided. Signal processing techniques include the joint analysis of heart rate variability (HRV) and respiratory signals (Ch. 6), novel techniques for deriving the respiratory signal from electrocardiogram (ECG) (Ch. 7), and a robust photoplethysmogram(PPG)waveform analysis based on amodel-based approach (Ch. 8). In Part III, ANS biomarkers are evaluated in stress assessment (Ch. 9) and in the monitoring of depression (Ch. 10). Part I:Mental health is not only related to that positive state ofwell-being, inwhich an individual can cope with the normal stresses of life, but also to the absence of mental illness. Mental illness or disorder can be defined as an emotional, cognitive, or behavioural disturbance that causes substantial functional impairment in one or more major life activities. The most common mental disorders, which are often co-occurring, are anxiety and major depressive disorder (MDD). Mental illness has a negative impact on the quality of life, since it is associated with considerable losses in health and functioning, and increases significantly a person’s risk for cardiovascular diseases. A common instigator underlying the co-morbidity between MDD, cardiovascular pathology, and anxiety is mental stress. Stress is common in our fast-paced society and strongly influences our mental health. In the short term, ANS controls the cardiovascular response to stressful stimuli. Regulation of physiological parameters, such as heart rate, respiratory rate, and blood pressure, allows the organism to respond to sudden changes in the environment. However, physiological adaptation to a regularly occurring environmental phenomenon alters biological systems involved in stress response. Neurobiological alterations in the brain can disrupt the function of the ANS. ANS dysfunction and structural brain changes have a negative impact on cognitive, emotional, and behavioral processes, thereby leading to development of mental illness. Part II: The development of novel approaches for deriving noninvasive ANS biomarkers offers the possibility to improve the assessment of stress in healthy individuals and ANS dysfunction in MDD patients. Joint analysis of various biosignals (multi-modal approach) allows for the quantification of interactions among biological systems associated with ANS, while the modeling of biosignals and subsequent analysis of the model’s parameters (model-based approach) allows for the robust quantification of changes in physiological mechanisms related to the ANS. A novel method, which takes into account both phase and frequency locking phenomena between respiration and HRV signals, for assessing quadratic nonlinear cardiorespiratory coupling is proposed in Ch. 6.3. Novel techniques for improving the monitoring of respiration are proposed in Ch. 7. In Ch. 8, to increase the robustness for some morphological measurements reflecting arterial tone changes, the modeling of the PPG pulse as amain wave superposed with several reflected waves is considered. Part III: ANS biomarkers are evaluated in the assessment of different types of stress, either physiological or psychological, in healthy individuals, and then, in the monitoring of depression. In the presence of mental stress (Ch. 9.1), induced by cognitive tasks, healthy subjects show an increment in the respiratory rate and higher number of nonlinear interactions between respiration and HRV signal, which might be associated with a sympathetic activation, but also with a less regular breathing. In the presence of hemodynamic stress (Ch. 9.2), induced by a postural change, healthy subjects show a reduction in strength of the quadratic nonlinear cardiorespiratory coupling, whichmight be related to a vagal withdrawal. In the presence of heat stress (Ch. 9.3), induced by exposure to elevated environmental temperatures, healthy subjects show an increased sympathovagal balance. This demonstrates that ANS biomarkers are able to assess different types of stress and they can be further explored in the context of depression monitoring. In Ch. 10, differences in ANS function between MDD and healthy subjects during a mental stress protocol are assessed, not only with the raw values of ANS biomarkers but also with autonomic reactivity indices, which reflect the ability of an individual to copewith a challenging situation. Results show that depression is associated with autonomic imbalance, characterized by increased sympathetic activity and reduced arterial compliance. Autonomic reactivity indices quantified by changes, from stress to recovery, in arterial stiffness surrogates, such as the PPG amplitude loss in wave reflections, show the best performance in terms of correlation with depression severity, yielding to correlation coefficient r = −0.5. The negative correlation implies that a higher degree of depression is associated with a decreased autonomic reactivity. The discriminative power of ANS biomarkers is supported by their high diagnostic performance for classifying subjects as having MDD or not, yielding to accuracy of 80.0%. Therefore, it can be concluded that ANS biomarkers can be used for assessing stress and that impaired arterial compliance might constitute a biomarker of mental health useful in the monitoring of depression.<br /

Depressive rumination and heart rate variability: A pilot study on the effect of biofeedback on rumination and its physiological concomitants

ObjectiveRecent studies suggest that lower resting heart rate variability (HRV) is associated with elevated vulnerability to depressive rumination. In this study, we tested whether increases in HRV after HRV-biofeedback training are accompanied by reductions in rumination levels. Materials and methodsSixteen patients suffering from depression completed a 6-week HRV-biofeedback training and fourteen patients completed a control condition in which there was no intervention (waitlist). The training included five sessions per week at home using a smartphone application and an ECG belt. Depressive symptoms and autonomic function at rest and during induced rumination were assessed before and after each of the two conditions. We used a well-established rumination induction task to provoke a state of pervasive rumination while recording various physiological signals simultaneously. Changes in HRV, respiration rate, skin conductance, and pupil diameter were compared between conditions and time points. ResultsA significant correlation was found between resting HRV and rumination levels, both assessed at the first laboratory session (r = -0.43, p < 0.05). Induction of rumination led to an acceleration of heart rate and skin conductance increases. After biofeedback training, resting vagal HRV was increased (p < 0.01) and self-ratings of state anxiety (p < 0.05), rumination (p < 0.05), perceived stress (p < 0.05), and depressive symptoms (QIDS, BDI; both p < 0.05) were decreased. In the control condition, there were no changes in autonomic indices or depressive symptomatology. A significant interaction effect group x time on HRV was observed. ConclusionOur results indicate that a smartphone-based HRV-biofeedback intervention can be applied to improve cardiovagal function and to reduce depressive symptoms including self-rated rumination tendencies

Blunted autonomic reactivity to mental stress in depression quantified by nonlinear cardiorespiratory coupling indices

In this study, differences in autonomic reactivity to mental stress between Major Depressive Disorder (MDD) patients and healthy control (HC) subjects are assessed by nonlinear cardiorespiratory coupling indices derived from the Real Wavelet Biphase. The degree and strength of Quadratic Phase Coupling (QPC) between interacting oscillations of Heart Rate Variability (HRV) and respiration are quantified before, during and after the execution of acognitive task. Results show that the QPC strength and QPC degree between the respiration and the respiratory sinus arrhythmia component of HRV were lower in HC compared to MDD during stress, suggesting that the parasympathetic branch was less inhibited in MDD patients. During recovery, only in HC group, this degree of QPC increased, while the respiratory rate was reduced, compared to the basal stage. The degree of QPC between the respiration and components of HRV in the low frequency band ([0.04, 0.15] Hz) increased in HC during stress, compared to the basal stage, while remained unchanged in MDD patients. These results imply that depression is associated with blunted autonomic reactivity to mental stres

An Exploration of Stress Reactivity, Stress Recovery, Mindfulness Meditation and Prayer with the use of Heart Rate Variability

This study investigated post-stress heart rate variability (HRV) changes during mindfulness meditation (MM) and while listening to a prayer passage from the Holy Quran. HRV was measured in Muslim students (N = 114) during: (1) a resting phase; (2) a cognitive stress-induction phase; and (3) a 10-minute post-stress phase. In the post-stress phase, participants were randomly assigned to one of the four conditions: (1) guided-MM; (2) description of MM; (3) prayer passage; or (4) description of prayer. Results revealed greater mean HF-HRV for male participants in the meditation experimental (MExp) group than the meditation control (MCon) group at the 15 min and 610 min post-stress phases and greater mean HF-HRV for female participants in the MExp group than the MCon group at the 15 min phase. Further analyses of females with self-reported dysphoric mood suggested that both MM and listening to a prayer can promote relaxation following exposure to a cognitive stressor

Unexpected Cardiovascular Oscillations at 0.1 Hz During Slow Speech Guided Breathing (OM Chanting) at 0.05 Hz.

Slow breathing at 0.1 Hz (i.e., 6 cycles per minute, cpm) leads to strong cardiovascular oscillations. However, the impact of breathing below 6 cpm is rarely addressed. We investigated the influence of OM chanting, an ancient Indian mantra, with approx. 3 respiratory cpm (0.05 Hz) on the synchronisation of heart period (RR), respiration (RESP) and systolic blood pressure (SBP). Nine healthy, trained speech practitioners chanted three sequences of five subsequent OM with 2 min pauses in between. Each single OM chanting consisted of taking a deep breath and a long "OM" during expiration and lasted approx. 20 s. ECG, respiration and blood pressure were recorded continuously, of which the RR tachogram, RESP and SBP were derived. Synchronisation between the signals was computed using the phase difference between two signals. During OM chanting synchronisation among the oscillations of RR, SBP and RESP was significantly increased compared to rest. Furthermore, OM chanting at breathing frequencies between 0.046 and 0.057 Hz resulted in 0.1 Hz oscillations in RR and SBP. In conclusion, OM chanting strongly synchronized cardiorespiratory and blood pressure oscillations. Unexpected oscillations at 0.1 Hz in SBP and RR appear at breathing frequencies of approx. 0.05 Hz. Such frequency doubling may originate from an interaction of breathing frequency with endogenous Mayer waves

The effect of spontaneous versus paced breathing on EEG, HRV, skin conductance and skin temperature

A dissertation submitted in fulfilment of the requirements for the degree Master of Science in Engineering, in the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg. January 2017 JohannesburgIt is well known that emotional stress has a negative impact on people’s health and physical, emotional and mental performance. Previous research has investigated the effects of stress on various aspects of physiology such as respiration, heart rate, heart rate variability (HRV), skin conductance, skin temperature and electrical activity in the brain. Essentially, HRV, Electroencephalography (EEG), skin conductance and skin temperature appear to reflect a stress response or state of arousal. Whilst the relationship between respiration rate, respiration rhythm and HRV is well documented, less is known about the relationship between respiration rate, EEG, skin conductance and skin temperature, whilst HRV is maximum (when there is resonance between HRV and respiration i.e. in phase with one another). This research project aims to investigate the impact that one session of slow paced breathing has on EEG, heart rate variability (HRV), skin conductance and skin temperature. Twenty male participants were randomly assigned to either a control or intervention group. Physiological data were recorded for the intervention and control group during one breathing session, over a short initial baseline (B1), a main session of 12 minutes, and a final baseline (B2). The only difference between the control and intervention groups was that during the main session, the intervention group practiced slow paced breathing (at 6 breaths per minute), while the control group breathed spontaneously. Wavelet transformation was used to analyse EEG data while Fourier transformation was used to analyse HRV. The study shows that slow-paced breathing significantly increases the low frequency and total power of the HRV but does not change the high frequency power of HRV. Furthermore, skin temperature significantly increased for the control group from B1 to Main, and was significantly higher for the control group when compared to the intervention group during the main session. There were no significant skin temperature changes between sessions for the intervention group. Skin conductance increased significantly from Main to B2 for the control group. No significant changes were found between sessions for the intervention group and between groups. EEG theta power at Cz decreased significantly from Main to B2 for the control group only, while theta power decreased at F4 from Main to B2 for both groups. Lastly, beta power at Cz decreased from B1 to B2 for the control group only. This significant effect that slow-paced breathing has on HRV suggests the hypothesis that with frequent practice, basal HRV would increase, and with it, potential benefits such as a reduction in anxiety and improved performance in specific tasks. Slow-paced breathing biofeedback thus shows promise as a simple, cheap, measurable and effective method to reduce the impact of stress on some physiological signals, suggesting a direction for future research.MT201

Photoplethysmographic waveform analysis for autonomic reactivity assessment in depression

Objective: In the present study, a photoplethysmographic (PPG) waveform analysis for assessing differences in autonomic reactivity to mental stress between patients with Major Depressive Disorder (MDD) and healthy control (HC) subjects is presented. Methods: PPG recordings of 40 MDD and 40 HC subjects were acquired at basal conditions, during the execution of cognitive tasks, and at the post-task relaxation period. PPG pulses are decomposed into three waves (a main wave and two reflected waves) using a pulse decomposition analysis. Pulse waveform characteristics such as the time delay between the position of the main wave and reflected waves, the percentage of amplitude loss in the reflected waves, and the heart rate (HR) are calculated among others. The intra-subject difference of a feature value between two conditions is used as an index of autonomic reactivity. Results: Statistically significant individual differences from stress to recovery were found for HR and the percentage of amplitude loss in the second reflected wave ( A13 ) in both HC and MDD group. However, autonomic reactivity indices related to A13 reached higher values in HC than in MDD subjects (Cohen's d =0.81, AUC = 0.74), implying that the stress response in depressed patients is reduced. A statistically significant (p < 0.001) negative correlation (r = 0.5) between depression severity scores and A13 was found. Conclusion: A decreased autonomic reactivity is associated with higher degree of depression. Significance: Stress response quantification by dynamic changes in PPG waveform morphology can be an aid for the diagnosis and monitoring of depression

Meditation-induced changes in high-frequency heart rate variability predict smoking outcomes

Background: High-frequency heart rate variability (HF-HRV) is a measure of parasympathetic nervous system (PNS) output that has been associated with enhanced self-regulation. Low resting levels of HF-HRV are associated with nicotine dependence and blunted stress-related changes in HF-HRV are associated with decreased ability to resist smoking. Meditation has been shown to increase HF-HRV. However, it is unknown whether tonic levels of HF-HRV or acute changes in HF-HRV during meditation predict treatment responses in addictive behaviors such as smoking cessation. Purpose: To investigate the relationship between HF-HRV and subsequent smoking outcomes. Methods: HF-HRV during resting baseline and during mindfulness meditation was measured within two weeks of completing a 4-week smoking cessation intervention in a sample of 31 community participants. Self-report measures of smoking were obtained at a follow up 17-weeks after the initiation of treatment. Results: Regression analyses indicated that individuals exhibiting acute increases in HF-HRV from resting baseline to meditation smoked fewer cigarettes at follow-up than those who exhibited acute decreases in HF-HRV (b = −4.89, p = 0.008). Conclusion: Acute changes in HF-HRV in response to meditation may be a useful tool to predict smoking cessation treatment response

Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research

Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Laboratory studies have elucidated the importance of several key physiological and behavioral biomarkers in the study of psychiatric disorders, but much less is known about the role of these biomarkers in naturalistic settings. These gaps are largely driven by methodological barriers to assessing biomarker data rapidly, reliably, and frequently outside the clinic or laboratory. Mobile health (mHealth) tools offer new opportunities to study relevant biomarkers in concert with other types of data (e.g., self-reports, global positioning system data). This review provides an overview on the state of this emerging field and describes examples from the literature where mHealth tools have been used to measure a wide array of biomarkers in the context of psychiatric functioning (e.g., psychological stress, anxiety, autism, substance use). We also outline advantages and special considerations for incorporating mHealth tools for remote biomarker measurement into studies of psychiatric illness and treatment and identify several specific opportunities for expanding this promising methodology. Integrating mHealth tools into this area may dramatically improve psychiatric science and facilitate highly personalized clinical care of psychiatric disorders