Point process time–frequency analysis of dynamic respiratory patterns during meditation practice

Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heart beats. We propose a robust algorithm for quantifying instantaneous RSA as applied to heart beat intervals and respiratory recordings under dynamic breathing patterns. The blood volume pressure-derived heart beat series (pulse intervals, PIs) are modeled as an inverse Gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PIs and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated via a frequency domain transfer function evaluated at instantaneous respiratory frequency where high coherence between respiration and PIs is observed. The model is statistically validated using Kolmogorov–Smirnov goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. The presented analysis confirms the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states, reporting statistically significant increase in RSA gain as measured by our paradigm.National Institutes of Health (U.S.) (Grant R01-HL084502)National Institutes of Health (U.S.) (Grant R01-DA015644)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant K01-AT00694-01

How Does the Body Affect the Mind? Role of Cardiorespiratory Coherence in the Spectrum of Emotions

The brain is considered to be the primary generator and regulator of emotions; however, afferent signals originating throughout the body are detected by the autonomic nervous system (ANS) and brainstem, and, in turn, can modulate emotional processes. During stress and negative emotional states, levels of cardiorespiratory coherence (CRC) decrease, and a shift occurs toward sympathetic dominance. In contrast, CRC levels increase during more positive emotional states, and a shift occurs toward parasympathetic dominance. Te dynamic changes in CRC that accompany different emotions can provide insights into how the activity of the limbic system and afferent feedback manifest as emotions. The authors propose that the brainstem and CRC are involved in important feedback mechanisms that modulate emotions and higher cortical areas. That mechanism may be one of many mechanisms that underlie the physiological and neurological changes that are experienced during pranayama and meditation and may support the use of those techniques to treat various mood disorders and reduce stress

Meditation Experiences, Self, and Boundaries of Consciousness

Our experiences with the external world are possible mainly through vision, hearing, taste, touch, and smell providing us a sense of reality. How the brain is able to seamlessly integrate stimuli from our external and internal world into our sense of reality has yet to be adequately explained in the literature. We have previously proposed a three-dimensional unified model of consciousness that partly explains the dynamic mechanism. Here we further expand our model and include illustrations to provide a better conception of the ill-defined space within the self, providing insight into a unified mind-body concept. In this article, we propose that our senses “super-impose” on an existing dynamic space within us after a slight, imperceptible delay. The existing space includes the entire intrapersonal space and can also be called the “the body’s internal 3D default space”. We provide examples from meditation experiences to help explain how the sense of ‘self’ can be experienced through meditation practice associated with underlying physiological processes that take place through cardio-respiratory synchronization and coherence that is developed among areas of the brain. Meditation practice can help keep the body in a parasympathetic dominant state during meditation, allowing an experience of inner ‘self’. Understanding this physical and functional space could help unlock the mysteries of the function of memory and cognition, allowing clinicians to better recognize and treat disorders of the mind by recommending proven techniques to reduce stress as an adjunct to medication treatment

Time-frequency and point process algorithms for cardiac arrhythmia analysis and cardiorespiratory control

Cardiovascular diseases are major causes of disability and premature death globally. In particular, atrial fibrillation is the most common cardiac arrhythmia condition found in clinical practice, and is associated with an increased risk of stroke. Heart rate variability (HRV) and respiratory sinus arrhythmia (RSA) are important indicators of cardiovascular health, and provide useful information on autonomic nervous system inputs to cardiac cycle and cardiorespiratory coupling, respectively. New methods to support the treatment of cardiovascular diseases and identifying efficient ways of measuring cardiovascular health could yield significant benefits. In this thesis, we present a number of advanced algorithms for cardiorespiratory signal processing. We present algorithms for analyzing atrial fibrillation arrhythmia from electrocardiograms (ECG). We propose an orthonormal basis function based representation for fibrillatory waveforms, and use a regularized least square solution for atrial activity extraction from ECG, suppressing more dominant ventricular components. Time-frequency analysis of atrial activity is used to identify and track fibrillatory frequencies from extracted atrial activity, which provides possible guidance to tailored treatments. In addressing the problem of tracking fibrillatory frequencies, we have developed a framework for generating new classes of time-frequency distributions with many desirable properties. This framework is based on multi-dimensional Fourier transform of a radially symmetric function, and can be used to generate new distributions with unique characteristics. A realization of this framework on a high-dimensional radial delta function results in a new class of time-frequency distributions, which we call radial-delta distributions. The class of radial-delta distributions unifies number of well known distributions, and further provides methods for high resolution time-frequency analysis of multi-component signals with low interference terms. We present a maximum likelihood inverse Gaussian point process model for dynamic and instantaneous HRV and RSA estimation from heart beat interval series and respiration recordings. Unlike previous methods, we perform time-frequency analysis of heart beat interval series, respiration, as well as the coherence between the two, and dynamically evaluate RSA transfer function based on instantaneous respiration and maximum coherence frequencies. The point process algorithm and dynamic respiration based RSA estimation methods are applied on two experimental protocols, a meditation experiment and a pain experiment. These applications demonstrate the robustness of the point process model in estimating HRV and RSA under different psychophysiological states. Regardless of the significant variations in respiration during meditation practice, goodness-of-fit tests are still found to be well within the desired confidence bounds, which validate the proposed models. Results indicate a sign! ificant increase in RSA during meditation practice, which suggest positive influence of meditation on the cardiovascular health. In the second experiment, reduced RSA during pain indicates the ability of the method to differentiate between different acute pain levels. Novel time-frequency distributions and orthonormal basis atrial activity representation based analysis provide accurate tracking of fibrillatory frequencies of atrial fibrillation arrhythmia from ECG. The point process model with time-frequency analysis provides accurate estimations of HRV and RSA, and is robust to dynamic changes in respiration and autonomic inputs. These algorithms provide useful tools for monitoring cardiovascular health and particular arrhythmia conditions

Breathing Signature as Vitality Score Index Created by Exercises of Qigong: Implications of Artificial Intelligence Tools Used in Traditional Chinese Medicine.

Rising concerns about the short- and long-term detrimental consequences of administration of conventional pharmacopeia are fueling the search for alternative, complementary, personalized, and comprehensive approaches to human healthcare. Qigong, a form of Traditional Chinese Medicine, represents a viable alternative approach. Here, we started with the practical, philosophical, and psychological background of Ki (in Japanese) or Qi (in Chinese) and their relationship to Qigong theory and clinical application. Noting the drawbacks of the current state of Qigong clinic, herein we propose that to manage the unique aspects of the Eastern 'non-linearity' and 'holistic' approach, it needs to be integrated with the Western "linearity" "one-direction" approach. This is done through developing the concepts of "Qigong breathing signatures," which can define our life breathing patterns associated with diseases using machine learning technology. We predict that this can be achieved by establishing an artificial intelligence (AI)-Medicine training camp of databases, which will integrate Qigong-like breathing patterns with different pathologies unique to individuals. Such an integrated connection will allow the AI-Medicine algorithm to identify breathing patterns and guide medical intervention. This unique view of potentially connecting Eastern Medicine and Western Technology can further add a novel insight to our current understanding of both Western and Eastern medicine, thereby establishing a vitality score index (VSI) that can predict the outcomes of lifestyle behaviors and medical conditions

Dynamic and Static Models of Body-Mind Approaches from Neurobiological Perspectives

Body-mind approaches (e.g., yoga, mindfulness meditation, Pilates method, and cognitive behavior therapy) are commonly used by the public today. However, the comprehensive neurobiological framework of effects of body-mind approaches is unknown. To begin, we discuss the dynamic and static models of each body-mind approaches from neurobiological perspectives, as well as from the standpoint of practical issues. By the dynamic components of body-mind approaches, people enhances meta-cognitive function, and it lead to decreases in avoidance behavior in social aversive context are suggested. On the other hand, it is assumed that static components of body-mind approaches enhance non-reactive monitoring function for baseline of self. Therefore, we discuss the implications of these findings for practitioners and for future research on body-mind researchers. Additionally, this chapter covers the essential ethical guidelines of body-mind approaches within the domain of medical or educational fields

Smart Biofeedback

Smart biofeedback is receiving attention because of the widespread availability of advanced technologies and smart devices that are used in effective collection, analysis, and feedback of physiologic data. Researchers and practitioners have been working on various aspects of smart biofeedback methodologies and applications by using wireless communications, the Internet of Things (IoT), wearables, biomedical sensors, artificial intelligence, big data analytics, clinical virtual reality, smartphones, and apps, among others. The current paradigm shift in information and communication technologies (ICT) has been propelling the rapid pace of innovation in smart biofeedback. This book addresses five important topics of the perspectives and applications in smart biofeedback: brain networks, neuromeditation, psychophysiological psychotherapy, physiotherapy, and privacy, security, and integrity of data

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

Brain Mechanisms Supporting the Modulation of Pain by Mindfulness Meditation

The subjective experience of one’s environment is constructed by interactions among sensory, cognitive, and affective processes. For centuries, meditation has been thought to influence such processes by enabling a nonevaluative representation of sensory events. To better understand how meditation influences the sensory experience, we used arterial spin labeling functional magnetic resonance imaging to assess the neural mechanisms by which mindfulness meditation influences pain in healthy human participants. After 4 d of mindfulness meditation training, meditating in the presence of noxious stimulation significantly reduced pain unpleasantness by 57% and pain intensity ratings by 40% when compared to rest. A two-factor repeated-measures ANOVA was used to identify interactions between meditation and pain-related brain activation. Meditation reduced pain-related activation of the contralateral primary somatosensory cortex. Multiple regression analysis was used to identify brain regions associated with individual differences in the magnitude of meditation-related pain reductions. Meditation-induced reductions in pain intensity ratings were associated with increased activity in the anterior cingulate cortex and anterior insula, areas involved in the cognitive regulation of nociceptive processing. Reductions in pain unpleasantness ratings were associated with orbitofrontal cortex activation, an area implicated in reframing the contextual evaluation of sensory events. Moreover, reductions in pain unpleasantness also were associated with thalamic deactivation, which may reflect a limbic gating mechanism involved in modifying interactions between afferent input and executive-order brain areas. Together, these data indicate that meditation engages multiple brain mechanisms that alter the construction of the subjectively available pain experience from afferent information