1,721,227 research outputs found
A Unified Framework for Investigating Aperiodic and Periodic Components in the Hearbeat Dynamics Spectrum: a Feasibility Study
Full text linkHeart Rate Variability (HRV) series is a widely used, non-invasive, and easy-to-acquire time-resolved signal for evaluating autonomic control on cardiovascular activity. Despite the recognition that heartbeat dynamics contains both periodic and aperiodic components, the majority of HRV modeling studies concentrate on only one component. On the one hand, there are models based on self-similarity and 1/f behavior that focus on the aperiodic component; on the other hand, there is the conventional division of the spectral domain into narrow-band oscillations, which considers HRV as a combination of periodic components. Taking inspiration from a recent parametrization of EEG power spectra, here we evaluate the applicability of a unified modeling framework to quantitatively assess heartbeat dynamics spectra as a mixture of aperiodic and periodic components. The proposed model is applied on publicly-available, real HRV series collected during postural changes from 10 healthy subjects. Results show that the proposed modeling effectively characterizes different experimental conditions and may complement HRV standard analysis defined in the frequency domain
Towards the definition of Microstates of the Cortical Brain-Heart Axis
Full text linkBrain microstates are defined as states with quasi-stable scalp activity topography and have been widely studied in literature. Whether those states are brain-specific or extend to the body level is unknown yet. We investigate the extension of cortical microstates to the peripheral autonomic nerve, specifically at the brain-heart axis level as a functional state of the central autonomic network. To achieve this, we combined Electroencephalographic (EEG) and heart rate variability (HRV) series from 36 healthy volunteers undergoing a cognitive workload elicitation after a resting state. Our results showed the existence of microstates at the functional brain-heart axis with spatio-temporal and quasi-stable states that exclusively pertained to the efferent direction from the brain to the heart. Some of the identified microstates are specific for neural or cardiovascular frequency bands, while others topographies are recurrent over the EEG and HRV spectra. Furthermore, some of the identified brain-heart microstates were associated with specific experimental conditions, while others were nonspecific to tasks. Our findings support the hypothesis that EEG microstates extend to the brain-heart axis level and may be exploited in future neuroscience and clinical research
Autonomic nervous system dynamics for mood and emotional-state recognition: significant advances in data acquisition, signal processing and classification
No full textThis monograph reports on advances in the measurement and study of autonomic nervous system (ANS) dynamics as a source of reliable and effective markers for mood state recognition and assessment of emotional responses. Its primary impact will be in affective computing and the application of emotion-recognition systems. Applicative studies of biosignals such as: electrocardiograms; electrodermal responses; respiration activity; gaze points; and pupil-size variation are covered in detail, and experimental results explain how to characterize the elicited affective levels and mood states pragmatically and accurately using the information thus extracted from the ANS. Nonlinear signal processing techniques play a crucial role in understanding the ANS physiology underlying superficially noticeable changes and provide important quantifiers of cardiovascular control dynamics. These have prognostic value in both healthy subjects and patients with mood disorders. Moreover, Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition proposes a novel probabilistic approach based on the point-process theory in order to model and characterize the instantaneous ANS nonlinear dynamics providing a foundation from which machine “understanding” of emotional response can be enhanced. Using mathematics and signal processing, this work also contributes to pragmatic issues such as emotional and mood-state modeling, elicitation, and non-invasive ANS monitoring. Throughout the text a critical review on the current state-of-the-art is reported, leading to the description of dedicated experimental protocols, novel and reliable mood models, and novel wearable systems able to perform ANS monitoring in a naturalistic environment. Biomedical engineers will find this book of interest, especially those concerned with nonlinear analysis, as will researchers and industrial technicians developing wearable systems and sensors for ANS monitoring
Perspective: It's All about Time
No full textNew knowledge on multi-scale temporal dynamics linking nanobio-time series, seasonal changes, immune response, and gut mictobiota can milestone (neuro) science soon
Brain Dynamics during Arousal-dependent Pleasant/Unpleasant Visual Elicitation: An Electroencephalographic Study on the Circumplex Model of Affect
Full text linkEmotion regulation to pleasant and unpleasant stimuli involves several brain areas, such as the prefrontal cortex, amygdala, and insular cortex. However, how a specific arousal level affects such brain dynamics is not fully understood. To this effect, we propose an electroencephalography (EEG)-based study, wherein 22 healthy subjects were emotionally elicited through affective pictures gathered from the International Affective Picture System. Based on the circumplex model of affect, we used four arousing levels; each of which includes two pleasantness levels. Considering these levels, we investigated the EEG power spectra and functional connectivity among channels. We then used this information to build an automatic valence classifier. The experimental results showed that the functional connectivity at the highest frequency bands (> 30Hz) was most sensitive to arousal modulation. Specifically, the high connectivity over the right hemisphere occurred during the pleasant elicitation, whereas that over the left hemisphere occurred during the negative elicitation. In addition, short-range connections in the frontal regions became weaker with increasing arousal level, whereas long-range connections were enhanced. Concerning the spectral analysis, the most significant valence-dependent changes were found at the intermediate arousing elicitations over the prefrontal and occipital regions. The valence classification showed a maximal recognition accuracy of 86.37
Complexity and nonlinearity in cardiovascular signals
No full textThis book reports on the latest advances in complex and nonlinear cardiovascular physiology aimed at obtaining reliable, effective markers for the assessment of heartbeat, respiratory, and blood pressure dynamics. The chapters describe in detail methods that have been previously defined in theoretical physics such as entropy, multifractal spectra, and Lyapunov exponents, contextualized within physiological dynamics of cardiovascular control, including autonomic nervous system activity. Additionally, the book discusses several application scenarios of these methods. The text critically reviews the current state-of-the-art research in the field that has led to the description of dedicated experimental protocols and ad-hoc models of complex physiology. This text is ideal for biomedical engineers, physiologists, and neuroscientists. This book also: Expertly reviews cutting-edge research, such as recent advances in measuring complexity, nonlinearity, and information-theoretic concepts applied to coupled dynamical systems Comprehensively describes applications of analytic technique to clinical scenarios such as heart failure, depression and mental disorders, atrial fibrillation, acute brain lesions, and more Broadens readers' understanding of cardiovascular signals, heart rate complexity, heart rate variability, and nonlinear analysi
Advances in electrodermal activity processing with applications for mental health: from heuristic methods to convex optimization
No full textThis book explores Autonomic Nervous System (ANS) dynamics as investigated through Electrodermal Activity (EDA) processing. It presents groundbreaking research in the technical field of biomedical engineering, especially biomedical signal processing, as well as clinical fields of psychometrics, affective computing, and psychological assessment. This volume describes some of the most complete, effective, and personalized methodologies for extracting data from a non-stationary, nonlinear EDA signal in order to characterize the affective and emotional state of a human subject. These methodologies are underscored by discussion of real-world applications in mood assessment. The text also examines the physiological bases of emotion recognition through noninvasive monitoring of the autonomic nervous system. This is an ideal book for biomedical engineers, physiologists, neuroscientists, engineers, applied mathmeticians, psychiatric and psychological clinicians, and graduate students in these fields.
This book also:
Expertly introduces a novel approach for EDA analysis based on convex optimization and sparsity, a topic of rapidly increasing interest
Authoritatively presents groundbreaking research achieved using EDA as an exemplary biomarker of ANS dynamics
Deftly explores EDA's potential as a source of reliable and effective markers for the assessment of emotional responses in healthy subjects, as well as for the recognition of pathological mood states in bipolar patient
Inferring directionality of coupled dynamical systems using Gaussian process priors: Application on neurovascular systems
Full text linkDynamical system theory has recently shown promise for uncovering causality and directionality in complex
systems, particularly using the method of convergent cross mapping (CCM). In spite of its success in the
literature, the presence of process noise raises concern about CCM’s ability to uncover coupling direction.
Furthermore, CCM’s capacity to detect indirect causal links may be challenged in simulated unidrectionally
coupled Rossler-Lorenz systems. To overcome these limitations, we propose a method that places a Gaussian
process prior on a cross mapping function (named GP-CCM) to impose constraints on local state space neighborhood
comparisons. Bayesian posterior likelihood and evidence ratio tests, as well as surrogate data analyses
are performed to obtain a robust statistic for dynamical coupling directionality. We demonstrate GP-CCM’s
performance with respect to CCM in synthetic data simulation as well as in empirical electroencephelography
(EEG) and functional near infrared spectroscopy (fNIRS) activity data. Our findings show that GP-CCM provides
a statistic that consistently reports indirect causal structures in non-separable unidirectional system interactions;
GP-CCM also provides coupling direction estimates in noisy physiological signals, showing that EEG likely
causes, i.e., drives, fNIRS dynamics
- …
