Analysis of heartbeat dynamics by point process adaptive filtering

A Novel Adaptive Noise Elimination Algorithm in Long RR Interval Sequences for Heart Rate Variability Analysis

As heart rate variability (HRV) studies become more and more prevalent in clinical practice, one of the most common and significant causes of errors is associated with distorted RR interval (RRI) data acquisition. The nature of such artifacts can be both mechanical as well as software based. Various currently used noise elimination in RRI sequences methods use filtering algorithms that eliminate artifacts without taking into account the fact that the whole RRI sequence time cannot be shortened or lengthened. Keeping that in mind, we aimed to develop an artifacts elimination algorithm suited to long-term (hours or days) sequences that does not affect the overall structure of the RRI sequence and does not alter the duration of data registration. An original adaptive smart time series step-by-step analysis and statistical verification methods were used. The adaptive algorithm was designed to maximize the reconstruction of the heart-rate structure and is suitable for use, especially in polygraphy. The authors submit the scheme and program for use.

Elementary integrate-and-fire process underlies pulse amplitudes in Electrodermal activity

Electrodermal activity (EDA) is a direct read-out of sweat-induced changes in the skin’s electrical conductance. Sympathetically-mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process, which yields an inverse Gaussian model as the inter-pulse interval distribution. We have previously showed that the inter-pulse intervals in EDA follow an inverse Gaussian distribution. However, the statistical structure of EDA pulse amplitudes has not yet been characterized based on the physiology. Expanding upon the integrate-and-fire nature of sweat glands, we hypothesized that the amplitude of an EDA pulse is proportional to the excess volume of sweat produced compared to what is required to just reach the surface of the skin. We modeled this as the difference of two inverse Gaussian models for each pulse, one which represents the time required to produce just enough sweat to rise to the surface of the skin and one which represents the time requires to produce the actual volume of sweat. We proposed and tested a series of four simplifications of our hypothesis, ranging from a single difference of inverse Gaussians to a single simple inverse Gaussian. We also tested four additional models for comparison, including the lognormal and gamma distributions. All models were tested on EDA data from two subject cohorts, 11 healthy volunteers during 1 hour of quiet wakefulness and a different set of 11 healthy volunteers during approximately 3 hours of controlled propofol sedation. All four models which represent simplifications of our hypothesis outperformed other models across all 22 subjects, as measured by Akaike’s Information Criterion (AIC), as well as mean and maximum distance from the diagonal on a quantile-quantile plot. Our broader model set of four simplifications offered a useful framework to enhance further statistical descriptions of EDA pulse amplitudes. Some of the simplifications prioritize fit near the mode of the distribution, while others prioritize fit near the tail. With this new insight, we can summarize the physiologically-relevant amplitude information in EDA with at most four parameters. Our findings establish that physiologically based probability models provide parsimonious and accurate description of temporal and amplitude characteristics in EDA.

Electrodermal activity (EDA) is an indirect read-out of the body’s sympathetic nervous system, or fight-or-flight response, measured as sweat-induced changes in the electrical conductance properties of the skin. Interest is growing in using EDA to track physiological conditions such as stress levels, sleep quality, and emotional states. Our previous worked showed that the times in between EDA pulses obeyed a specific statistical distribution, the inverse Gaussian, that arises from the physiology of EDA production. In this work, we build on that insight to analyze the amplitudes of EDA pulses. In an analysis of EDA data recorded in 11 healthy volunteers during quiet wakefulness and 11 different healthy volunteers during controlled propofol sedation, we establish that the amplitudes of EDA pulses also have specific statistical structure, as the differences of inverse Gaussians, that arises from the physiology of sweat production. We capture that structure using a series of progressively simpler models that each prioritize different parts of the pulse amplitude distribution. Our findings show that a physiologically-based statistical model provides a parsimonious and accurate description of EDA. This enables increased reliability and robustness in analyzing EDA data collected under any circumstance.

Point process temporal structure characterizes electrodermal activity

Electrodermal activity (EDA) is a readout of the body’s sympathetic nervous system measured as sweat-induced changes in the electrical conductance properties of the skin. Interest is growing in using EDA to track physiological conditions such as stress levels, sleep quality, and emotional states. The integrate-and-fire physiology underlying EDA production suggests that its interpulse intervals should obey an inverse Gaussian probability model. In an analysis of EDA data recorded in 11 healthy volunteers during quiet wakefulness, we established that the inverse Gaussian model accurately characterized the interpulse intervals. Our findings show a physiologically based statistical model provides a parsimonious and accurate description of EDA.

Electrodermal activity (EDA) is a direct readout of the body’s sympathetic nervous system measured as sweat-induced changes in the skin’s electrical conductance. There is growing interest in using EDA to track physiological conditions such as stress levels, sleep quality, and emotional states. Standardized EDA data analysis methods are readily available. However, none considers an established physiological feature of EDA. The sympathetically mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process. An integrate-and-fire process modeled as a Gaussian random walk with drift diffusion yields an inverse Gaussian model as the interpulse interval distribution. Therefore, we chose an inverse Gaussian model as our principal probability model to characterize EDA interpulse interval distributions. To analyze deviations from the inverse Gaussian model, we considered a broader model set: the generalized inverse Gaussian distribution, which includes the inverse Gaussian and other diffusion and nondiffusion models; the lognormal distribution which has heavier tails (lower settling rates) than the inverse Gaussian; and the gamma and exponential probability distributions which have lighter tails (higher settling rates) than the inverse Gaussian. To assess the validity of these probability models we recorded and analyzed EDA measurements in 11 healthy volunteers during 1 h of quiet wakefulness. Each of the 11 time series was accurately described by an inverse Gaussian model measured by Kolmogorov–Smirnov measures. Our broader model set offered a useful framework to enhance further statistical descriptions of EDA. Our findings establish that a physiologically based inverse Gaussian probability model provides a parsimonious and accurate description of EDA.

Impact of sex and depressed mood on the central regulation of cardiac autonomic function

Cardiac autonomic dysregulation has been implicated in the comorbidity of major psychiatric disorders and cardiovascular disease, potentially through dysregulation of physiological responses to negative stressful stimuli (here, shortened to stress response). Further, sex differences in these comorbidities are substantial. Here, we tested the hypothesis that mood- and sex-dependent alterations in brain circuitry implicated in the regulation of the stress response are associated with reduced peripheral parasympathetic activity during negative emotional arousal. Fifty subjects (28 females) including healthy controls and individuals with major depression, bipolar psychosis and schizophrenia were evaluated. Functional magnetic resonance imaging and physiology (cardiac pulse) data were acquired during a mild visual stress reactivity challenge. Associations between changes in activity and functional connectivity of the stress response circuitry and variations in cardiovagal activity [normalized high frequency power of heart rate variability (HFn)] were evaluated using GLM analyses, including interactions with depressed mood and sex across disorders. Our results revealed that in women with high depressed mood, lower cardiovagal activity in response to negative affective stimuli was associated with greater activation of hypothalamus and right amygdala and reduced connectivity between hypothalamus and right orbitofrontal cortex, amygdala, and hippocampus. No significant associations were observed in women with low levels of depressed mood or men. Our results revealed mood- and sex-dependent interactions in the central regulation of cardiac autonomic activity in response to negative affective stimuli. These findings provide a potential pathophysiological mechanism for previously observed sex differences in the comorbidity of major depression and cardiovascular disease.

Multichannel ECG recording from waist using textile sensors

BACKGROUND

The development of wearable health monitoring systems is garnering tremendous interest in research, technology and commercial applications. Their ability of providing unique capabilities in continuous, real-time, and non-invasive tracking of the physiological markers of users can provide insights into the performance and health of individuals. Electrocardiogram (ECG) signals are of particular interest, as cardiovascular disease is the leading cause of death globally. Monitoring heart health and its conditions such as ventricular disturbances and arrhythmias can be achieved through evaluating various features of ECG such as R-peaks, QRS complex, T-wave, and P-wave. Despite recent advances in biosensors for wearable applications, most of the currently available solutions rely solely on a single system attached to the body, limiting the ability to obtain reliable and multi-location biosignals. However, in engineering systems, sensor fusion, which is the optimal integration and processing of data from multiple sensors, has been a common theme and should be considered for wearables. In recent years, due to an increase in the availability and variety of different types of sensors, the possibility of achieving sensor fusion in wearable systems has become more attainable. Sensor fusion in multi-sensing systems results in significant enhancements of information inferences compared to those from systems with a sole sensor. One step towards the development of sensor fusion for wearable health monitoring systems is the accessibility to multiple reliable electrophysiological signals, which can be recorded continuously.

RESULTS

In this paper, we develop a textile-based multichannel ECG band that has the ability to measure ECG from multiple locations on the waist. As a proof of concept, we demonstrate that ECG signals can be reliably obtained from different locations on the waist where the shape of the QRS complex is nearly comparable with recordings from the chest using traditional gel electrodes. In addition, we develop a probabilistic approach-based on prediction and update strategies-to detect R-peaks from noisy textile data in different statuses, including sitting, standing, and jogging. In this approach, an optimal search method is utilized to detect R-peaks based on the history of the intervals between previously detected R-peaks. We show that the performance of our probabilistic approach in R-peak detection is significantly better than that based on Pan-Tompkins and optimal-threshold methods.

CONCLUSION

A textile-based multichannel ECG band was developed to track the heart rate changes from multiple locations on the waist. We demonstrated that (i) the ECG signal can be detected from different locations on the waist, and (ii) the accuracy of the detected R-peaks from textile sensors was improved by using our proposed probabilistic approach. Despite the limitations of the textile sensors that might compromise the quality of ECG signals, we anticipate that the textile-based multichannel ECG band can be considered as an effective wearable system to facilitate the development of sensor fusion methodology for pervasive and non-invasive health monitoring through continuous tracking of heart rate variability (HRV) from the waist. In addition, from the commercialization point of view, we anticipate that the developed band has the potential to be integrated into garments such as underwear, bras or pants so that individuals can use it on a daily basis.

A mixed filter algorithm for sympathetic arousal tracking from skin conductance and heart rate measurements in Pavlovian fear conditioning

Pathological fear and anxiety disorders can have debilitating impacts on individual patients and society. The neural circuitry underlying fear learning and extinction has been known to play a crucial role in the development and maintenance of anxiety disorders. Pavlovian conditioning, where a subject learns an association between a biologically-relevant stimulus and a neutral cue, has been instrumental in guiding the development of therapies for treating anxiety disorders. To date, a number of physiological signal responses such as skin conductance, heart rate, electroencephalography and cerebral blood flow have been analyzed in Pavlovian fear conditioning experiments. However, physiological markers are often examined separately to gain insight into the neural processes underlying fear acquisition. We propose a method to track a single brain-related sympathetic arousal state from physiological signal features during fear conditioning. We develop a state-space formulation that probabilistically relates features from skin conductance and heart rate to the unobserved sympathetic arousal state. We use an expectation-maximization framework for state estimation and model parameter recovery. State estimation is performed via Bayesian filtering. We evaluate our model on simulated and experimental data acquired in a trace fear conditioning experiment. Results on simulated data show the ability of our proposed method to estimate an unobserved arousal state and recover model parameters. Results on experimental data are consistent with skin conductance measurements and provide good fits to heartbeats modeled as a binary point process. The ability to track arousal from skin conductance and heart rate within a state-space model is an important precursor to the development of wearable monitors that could aid in patient care. Anxiety and trauma-related disorders are often accompanied by a heightened sympathetic tone and the methods described herein could find clinical applications in remote monitoring for therapeutic purposes.

Automatic Detection of General Anesthetic-States using ECG-Derived Autonomic Nervous System Features

Electroencephalogram (EEG)-based prediction systems are used to target anesthetic-states in patients undergoing procedures with general anesthesia (GA). These systems are not widely employed in resource-limited settings because they are cost-prohibitive. Although anesthetic-drugs induce highly-structured, oscillatory neural dynamics that make EEG-based systems a principled approach for anesthetic-state monitoring, anesthetic-drugs also significantly modulate the autonomic nervous system (ANS). Because ANS dynamics can be inferred from electrocardiogram (ECG) features such as heart rate variability, it may be possible to develop an ECG-based system to infer anesthetic-states as a low-cost and practical alternative to EEG-based anesthetic-state prediction systems. In this work, we demonstrate that an ECG-based system using ANS features can be used to discriminate between non-GA and GA states in sevoflurane, with a GA F₁ score of 0.834, [95% CI, 0.776, 0.892], and in sevoflurane-plus-ketamine, with a GA F₁ score of 0.880 [0.815, 0.954]. With further refinement, ECG-based anesthetic-state systems could be developed as a fully automated system for anesthetic-state monitoring in resource-limited settings.

Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition

BACKGROUND AND OBJECTIVE

It is challenging to conduct real-time identification of myocardial infarction (MI) due to artifact corruption and high dimensionality of multi-lead electrocardiogram (ECG). In the present study, we proposed an automated single-beat MI detection and localization system using dual-Q tunable Q-factor wavelet transformation (Dual-Q TQWT) denoising algorithm.

METHODS

After denoising and segmentation of ECG, a fourth-order wavelet tensor (leads × subbands × samples × beats) was constructed based on the discrete wavelet packet transform (DWPT), to represent the features considering the information of inter-beat, intra-beat, inter-frequency, and inter-lead. To reduce the tensor dimension and preserve the intrinsic information, the multilinear principal component analysis (MPCA) was employed. Afterward, 84 discriminate features were fed into a classifier of bootstrap-aggregated decision trees (Treebagger). A total of 78 healthy and 328 MI (6 types) records including 57557 beats were chosen from PTB diagnostic ECG database for evaluation.

RESULTS

The validation results demonstrated that our proposed MI detection and localization system embedded with Dual-Q TQWT and wavelet packet tensor decomposition outperformed commonly used discrete wavelet transform (DWT), empirical mode decomposition (EMD) denoising methods and vector-based PCA method. With the Treebagger classifier, we obtained an accuracy of 99.98% in beat level and an accuracy of 97.46% in record level training/testing for MI detection. We also achieved an accuracy of 99.87% in beat level and an accuracy of 90.39% in record level for MI localization.

CONCLUSION

Altogether, the automated system brings potential improvement in automated detection and localization of MI in clinical practice.

Continuous Pain Intensity Estimation from Autonomic Signals with Recurrent Neural Networks

Pain is usually measured by patient's self-report, which requires patient collaboration. Hence, the development of an objective automatic pain detection method would be useful in many clinical applications and patient populations. Previous studies have explored the feasibility of using physiological autonomic signals to detect the presence of pain. In this study, we focused on continuously estimating experimental heat pain intensity with high temporal resolution from autonomic signals. Specifically, we employed skin conductance deconvolution and point process heart rate variability analysis to continuously evaluate time-varying autonomic parameters, and presented a regression algorithm based on recurrent neural networks.

A Bayesian Filtering Approach for Tracking Arousal From Binary and Continuous Skin Conductance Features

OBJECTIVE

Neuroanatomical structures within the cortical and sub-cortical brain regions process emotion and cause subsequent variations in signals such as skin conductance and electrocardiography. The signals often encode information in their continuous-valued amplitudes or waves as well as in their underlying impulsive events. We propose to track psychological arousal from this hybrid source of skin conductance information.

METHODS

We present a point process state-space method in tandem with Bayesian filtering for determining a continuous-valued arousal state from skin conductance measurements. To perform state estimation, we relate arousal to binary- and continuous-valued observations derived from the phasic and tonic parts of a skin conductance signal, and recover model parameters using expectation-maximization. We evaluate our model on both synthetic and two different experimental data sets. Stress was artificially induced in the first experimental data set and the second comprised of a fear conditioning experiment.

RESULTS

Results on the first data set indicate high levels of arousal during exposure to cognitive stress and low arousal during relaxation. Plausible results are also obtained in the fear conditioning data set consistent with previous skin conductance studies in similar experimental contexts.

CONCLUSION

The state-space approach-which does not rely on external classification labels-is able to continuously track an arousal level from skin conductance features.

SIGNIFICANCE

The method is a promising arousal estimation scheme utilizing only skin conductance. The approach could find applications in wearable monitoring and the study of neuropsychiatric conditions such as post-traumatic stress disorder.

A Point Process Characterization Of Electrodermal Activity

Electrodermal activity (EDA) is a measure of sympathetic activity using skin conductance that has applications in research and in clinical medicine. However, current EDA analysis does not have physiologically-based statistical models that use stochastic structure to provide nuanced insight into autonomic dynamics. Therefore, in this study, we analyzed the data of two healthy volunteers under controlled propofol sedation. We identified a novel statistical model for EDA and used a point process framework to track instantaneous dynamics. Our results demonstrate for the first time that point process models rooted in physiology and built upon inherent statistical structure of EDA pulses have the potential to accurately track instantaneous dynamics in sympathetic tone.

Tracking autonomic balance using an open-loop model of the arterial baroreflex

Blood pressure control is vital for maintaining adequate perfusion of the brain and other organs in the body across varying physiological demands, and the arterial baroreceptor reflex (baroreflex) is the major short-term blood pressure control loop mediated by the autonomic nervous system (ANS). Accurate quantitative models of the baroreflex would provide physiological insight and could allow for real-time tracking of ANS activity in clinical settings. In this work, we formulate a causal, parametric beat-to-beat model, relating systolic blood pressure (input) to heart rate (output). Model structure and parameterization are explicitly based on prior physiological insights of the response dynamics of the sympathetic and parasympathetic branches of the ANS. We analyze the model's ability to track changes in autonomic balance using data from 14 nonsmoking adult males, without any history of cardiopulmonary disease, subject to both pharmacological blockade and postural changes. Our results show that the model parameters faithfully track expected changes in autonomic balance resulting from changing posture ( P < 0.01) and sympathetic blockade ( P < 0.05), and in many cases, the model parameters are more sensitive to changes in autonomic activity and balance than autonomic indices derived from the power spectral density of heart rate variability. Overall, the contributions of this work further the goal of obtaining real-time quantitative assessment of the ANS.

Dynamics of seizure-induced behavioral and autonomic arousal

PURPOSE

Arousal is the most primitive, powerful instinct with survival benefit present in all vertebrates. Even though the arousal systems are classically viewed as "ascending" brainstem phenomena, there is a "descending" cortical feedback system that maintains consciousness. In this study, we provide electrophysiological confirmation that seizures localized to the anterior cingulum can behaviorally manifest as paroxysms of arousal from sleep.

METHODS

Temporal dynamics of arousal induced by anterior cingulate seizures were analyzed by using multiple modalities including stereoelectroencephalography (phase lag index and phase amplitude coupling), lead-1 ECG (point-process heart rate variability analysis) and diffusion tractography (DTI).

RESULTS

The ictal arousal was associated with an increase in synchronization in the alpha band and an increase in local theta or alpha-gamma phase-amplitude coupling. In comparison to seizures that lacked clinical manifestations, ictal arousal was associated with an increase in heart rate but not heart rate variability. Finally, DTI demonstrated degeneration in white fiber tracts passing between the anterior cingulum and anterior thalamus ipsilateral to the epileptogenic cortex. The patient underwent resection of the anterior cingulum, and histopathology confirmed focal cortical dysplasia type II.

CONCLUSION

Anterior cingulate seizures inducing behavioral arousal have identifiable autonomic and EEG signatures.

Measures of sympathetic and parasympathetic autonomic outflow from heartbeat dynamics

Reliable and effective noninvasive measures of sympathetic and parasympathetic peripheral outflow are of crucial importance in cardiovascular physiology. Although many techniques have been proposed to take up this long-lasting challenge, none has proposed a satisfying discrimination of the dynamics of the two separate branches. Spectral analysis of heart rate variability is the most currently used technique for such assessment. Despite its widespread use, it has been demonstrated that the subdivision in the low-frequency (LF) and high-frequency (HF) bands does not fully reflect separate influences of the sympathetic and parasympathetic branches, respectively, mainly due to their simultaneous action in the LF. Two novel heartbeat-derived autonomic measures, the sympathetic activity index (SAI) and parasympathetic activity index (PAI), are proposed to separately assess the time-varying autonomic nervous system synergic functions. Their efficacy is validated in landmark autonomic maneuvers generally employed in clinical settings. The novel measures move beyond the classical frequency domain paradigm through identification of a set of coefficients associated with a proper combination of Laguerre base functions. The resulting measures were compared with the traditional LF and HF power. A total of 236 ECG recordings were analyzed for validation, including autonomic outflow changes elicited by procedures of different nature and temporal variation, such as postural changes, lower body negative pressure, and handgrip tests. The proposed SAI-PAI measures consistently outperform traditional frequency-domain indexes in tracking expected instantaneous autonomic variations, both vagal and sympathetic, and may aid clinical decision making, showing reduced intersubject variability and physiologically plausible dynamics. NEW & NOTEWORTHY While it is possible to obtain reliable estimates of parasympathetic activity from the ECG, a satisfying method to disentangle the sympathetic component from HRV has not been proposed yet. To overcome this long-lasting limitation, we propose two novel HRV-based indexes, the sympathetic and parasympathetic activity indexes.

On a unified point process approach for the characterization of bioelectric discrete phenomena

This paper discusses a unified method based on the theory of point processes to characterize various types of bioelectric discrete signals such as heart beat timing, myoelectric activity, discharge of primary sensory neurons or neurons in the central nervous systems. The doubly stochastic point processes, in which the discrete event occurring intensity is stochastic, forms the most general class to characterize the discrete phenomena. In this paper the self-exciting process has been shown to be useful to characterize wide range of discrete biosignals. The modeling of conditional intensity function is the essential part of the characterization. When the intensity has a parametric model, the maximum likelihood parameter estimation will be the useful way to characterize the phenomena. The effectiveness of the method is demonstrated by a specific modeling of the spontaneous neuronal burst discharges recorded from the brain thalamus during the neuro surgery. The first approximation model has four parameters obtained by the instantaneous nonlinearly transformed sinusoidal function. An extended model allows arbitrary periodic intensity with refractory period. Predicted interval histograms show good agreement with the observed ones indicating the validity of the proposed method.

Respiratory-gated Auricular Vagal Afferent Nerve Stimulation (RAVANS) effects on autonomic outflow in hypertension

Transcutaneous stimulation of the auricular branch of the vagus nerve (ABVN) has been proposed as a non-invasive alternative to vagus nerve stimulation (VNS). However, its cardiovagal effects are inconsistent across studies, likely due to inhomogeneity in the stimulation parameters. Here, we evaluate respiratory-gated ABVN stimulation (Respiratory-gated Auricular Vagal Afferent Nerve Stimulation, RAVANS), where the stimuli are delivered in 1 s bursts during the exhalation phase of respiration, thus mimicking the breathing-induced modulation of cardiac vagal activity. In this study, we present preliminary results from an ongoing single-arm, open label trial investigating the effects of different intensities of RAVANS in hypertensive subjects. We found that a mid-intensity RAVANS stimulation (rated as a 5 on a 0-10 scale) increases the cardiovagal tone and reduces the sympathetic tone during a paced breathing task. The present results could contribute to optimize RAVANS as a non-invasive, low-cost therapeutic intervention for hypertension.

Complexity Variability Assessment of Nonlinear Time-Varying Cardiovascular Control

The application of complex systems theory to physiology and medicine has provided meaningful information about the nonlinear aspects underlying the dynamics of a wide range of biological processes and their disease-related aberrations. However, no studies have investigated whether meaningful information can be extracted by quantifying second-order moments of time-varying cardiovascular complexity. To this extent, we introduce a novel mathematical framework termed complexity variability, in which the variance of instantaneous Lyapunov spectra estimated over time serves as a reference quantifier. We apply the proposed methodology to four exemplary studies involving disorders which stem from cardiology, neurology and psychiatry: Congestive Heart Failure (CHF), Major Depression Disorder (MDD), Parkinson's Disease (PD), and Post-Traumatic Stress Disorder (PTSD) patients with insomnia under a yoga training regime. We show that complexity assessments derived from simple time-averaging are not able to discern pathology-related changes in autonomic control, and we demonstrate that between-group differences in measures of complexity variability are consistent across pathologies. Pathological states such as CHF, MDD, and PD are associated with an increased complexity variability when compared to healthy controls, whereas wellbeing derived from yoga in PTSD is associated with lower time-variance of complexity.

Robust Estimation of Sparse Narrowband Spectra from Neuronal Spiking Data

OBJECTIVE

Characterizing the spectral properties of neuronal responses is an important problem in computational neuroscience, as it provides insight into the spectral organization of the underlying functional neural processes. Although spectral analysis techniques are widely used in the analysis of noninvasive neural recordings such as EEG, their application to spiking data is limited due to the binary and nonlinear nature of neuronal spiking. In this paper, we address the problem of estimating the power spectral density of the neural covariate driving the spiking statistics of a neuronal population from binary observations.

METHODS

We consider a neuronal ensemble spiking according to Bernoulli statistics, for which the conditional intensity function is given by the logistic map of a harmonic second-order stationary process with sparse narrowband spectra. By employing sparsity-promoting priors, we compute the maximum a posteriori estimate of the power spectral density of the process from the binary spiking observations. Furthermore, we construct confidence intervals for these estimates by an efficient posterior sampling procedure.

RESULTS

We provide simulation studies which reveal that our method outperforms the existing methods for extracting the frequency content of spiking data. Application of our method to clinically recorded spiking data from a patient under general anesthesia reveals a striking resemblance between our estimated power spectral density and that of the local field potential signal. This result corroborates existing findings regarding the salient role of the local field potential as a major neural covariate of rhythmic cortical spiking activity under anesthesia.

CONCLUSION

Our technique allows us to analyze the harmonic structure of spiking activity in a robust fashion, independently of the local field potentials, and without any prior assumption of the spectral spread and content of the underlying neural processes.

SIGNIFICANCE

Other than its usage in the spectral analysis of neuronal spiking data, our technique can be applied to a wide variety of binary data, such as heart beat data, in order to obtain a robust spectral representation.

Globally conditioned Granger causality in brain-brain and brain-heart interactions: a combined heart rate variability/ultra-high-field (7 T) functional magnetic resonance imaging study

The causal, directed interactions between brain regions at rest (brain-brain networks) and between resting-state brain activity and autonomic nervous system (ANS) outflow (brain-heart links) have not been completely elucidated. We collected 7 T resting-state functional magnetic resonance imaging (fMRI) data with simultaneous respiration and heartbeat recordings in nine healthy volunteers to investigate (i) the causal interactions between cortical and subcortical brain regions at rest and (ii) the causal interactions between resting-state brain activity and the ANS as quantified through a probabilistic, point-process-based heartbeat model which generates dynamical estimates for sympathetic and parasympathetic activity as well as sympathovagal balance. Given the high amount of information shared between brain-derived signals, we compared the results of traditional bivariate Granger causality (GC) with a globally conditioned approach which evaluated the additional influence of each brain region on the causal target while factoring out effects concomitantly mediated by other brain regions. The bivariate approach resulted in a large number of possibly spurious causal brain-brain links, while, using the globally conditioned approach, we demonstrated the existence of significant selective causal links between cortical/subcortical brain regions and sympathetic and parasympathetic modulation as well as sympathovagal balance. In particular, we demonstrated a causal role of the amygdala, hypothalamus, brainstem and, among others, medial, middle and superior frontal gyri, superior temporal pole, paracentral lobule and cerebellar regions in modulating the so-called central autonomic network (CAN). In summary, we show that, provided proper conditioning is employed to eliminate spurious causalities, ultra-high-field functional imaging coupled with physiological signal acquisition and GC analysis is able to quantify directed brain-brain and brain-heart interactions reflecting central modulation of ANS outflow.

Relationship between cardiac vagal activity and mood congruent memory bias in major depression

BACKGROUND

Previous studies suggest that autonomic reactivity during encoding of emotional information could modulate the neural processes mediating mood-congruent memory. In this study, we use a point-process model to determine dynamic autonomic tone in response to negative emotions and its influence on long-term memory of major depressed subjects.

METHODS

Forty-eight patients with major depression and 48 healthy controls were randomly assigned to either neutral or emotionally arousing audiovisual stimuli. An adaptive point-process algorithm was applied to compute instantaneous estimates of the spectral components of heart rate variability [Low frequency (LF), 0.04-0.15 Hz; High frequency (HF), 0.15-0.4 Hz]. Three days later subjects were submitted to a recall test.

RESULTS

A significant increase in HF power was observed in depressed subjects in response to the emotionally arousing stimulus (p=0.03). The results of a multivariate analysis revealed that the HF power during the emotional segment of the stimulus was independently associated with the score of the recall test in depressed subjects, after adjusting for age, gender and educational level (Coef. 0.003, 95%CI, 0.0009-0.005, p=0.008).

LIMITATIONS

These results could only be interpreted as responses to elicitation of specific negative emotions, the relationship between HF changes and encoding/recall of positive stimuli should be further examined.

CONCLUSIONS

Alterations on parasympathetic response to emotion are involved in the mood-congruent cognitive bias observed in major depression. These findings are clinically relevant because it could constitute the mechanism by which depressed patients maintain maladaptive patterns of negative information processing that trigger and sustain depressed mood.

Multiscale entropy analysis of biological signals: a fundamental bi-scaling law

Since introduced in early 2000, multiscale entropy (MSE) has found many applications in biosignal analysis, and been extended to multivariate MSE. So far, however, no analytic results for MSE or multivariate MSE have been reported. This has severely limited our basic understanding of MSE. For example, it has not been studied whether MSE estimated using default parameter values and short data set is meaningful or not. Nor is it known whether MSE has any relation with other complexity measures, such as the Hurst parameter, which characterizes the correlation structure of the data. To overcome this limitation, and more importantly, to guide more fruitful applications of MSE in various areas of life sciences, we derive a fundamental bi-scaling law for fractal time series, one for the scale in phase space, the other for the block size used for smoothing. We illustrate the usefulness of the approach by examining two types of physiological data. One is heart rate variability (HRV) data, for the purpose of distinguishing healthy subjects from patients with congestive heart failure, a life-threatening condition. The other is electroencephalogram (EEG) data, for the purpose of distinguishing epileptic seizure EEG from normal healthy EEG.

Modeling heart beat dynamics and fMRI signals during carotid stimulation by neck suction

Central autonomic control on the cardiovascular system has been widely investigated in the last decades. More recently, with the advent of brain imaging techniques, considerable effort is being spent on defining the role of specific brain areas, and their dynamic network, acting on autonomic efferents. A way to assess autonomic modulation is offered by carotid stimulation. In this work, we propose a methodology to investigate autonomic control in carotid stimulation experiments using heartbeat series in combination with fMRI imaging. We modeled cardiovascular signals using the point process model, and processed fMRI data in order to estimate independent components of correlated information. Using cross-correlation and surrogate analysis, we assessed the responsiveness of subjects to neck suction stimuli and identified stimulus-related fMRI independent components.

Revealing real-time emotional responses: a personalized assessment based on heartbeat dynamics

Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis, and 83.55% on the arousal axis.

Point process modeling of interbreath interval: a new approach for the assessment of instability of breathing in neonates

Interbreath interval (IBI), the time interval between breaths, is an important measure used to analyze irregular breathing patterns in neonates. The discrete bursts of neural activity generate the IBI time series, which exhibits stochastic as well as deterministic dynamics. To quantify the irregularity of breathing, we propose a point process model of IBI using a comprehensive stochastic dynamic modeling framework. The IBIs of immature breathing patterns exhibit a long tail distribution and within a point process model, we have considered the lognormal distribution to represent the stochastic IBI characteristics. An autoregressive (AR) function is embedded within the model to capture the short-term IBI dynamics including abrupt IBI prolongations related to sporadic and periodic apneas that are common in neonates. We tested the utility of our paradigm for depicting the respiratory dynamics in neonatal rats and in preterm infants. Kolmogorov-Smirnov (KS) and independence tests reveal that the model accurately tracks the dynamic characteristics of the signals. In preterm infants, our model-derived indices of IBI instability strongly correlate with clinically derived indices of maturation. Our results validate a new class of algorithms, based on the point process theory, for defining instantaneous measures of breathing irregularity in neonates.

Classification of emotional states from electrocardiogram signals: a non-linear approach based on Hurst

Background

Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.

Methods

Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature ‘Hurst’ was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers – Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm.

Results

Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively.

Conclusions

The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system.

nSTAT: open-source neural spike train analysis toolbox for Matlab

Over the last decade there has been a tremendous advance in the analytical tools available to neuroscientists to understand and model neural function. In particular, the point process - generalized linear model (PP-GLM) framework has been applied successfully to problems ranging from neuro-endocrine physiology to neural decoding. However, the lack of freely distributed software implementations of published PP-GLM algorithms together with problem-specific modifications required for their use, limit wide application of these techniques. In an effort to make existing PP-GLM methods more accessible to the neuroscience community, we have developed nSTAT--an open source neural spike train analysis toolbox for Matlab®. By adopting an object-oriented programming (OOP) approach, nSTAT allows users to easily manipulate data by performing operations on objects that have an intuitive connection to the experiment (spike trains, covariates, etc.), rather than by dealing with data in vector/matrix form. The algorithms implemented within nSTAT address a number of common problems including computation of peri-stimulus time histograms, quantification of the temporal response properties of neurons, and characterization of neural plasticity within and across trials. nSTAT provides a starting point for exploratory data analysis, allows for simple and systematic building and testing of point process models, and for decoding of stimulus variables based on point process models of neural function. By providing an open-source toolbox, we hope to establish a platform that can be easily used, modified, and extended by the scientific community to address limitations of current techniques and to extend available techniques to more complex problems.

A real-time automated point-process method for the detection and correction of erroneous and ectopic heartbeats

The presence of recurring arrhythmic events (also known as cardiac dysrhythmia or irregular heartbeats), as well as erroneous beat detection due to low signal quality, significantly affects estimation of both time and frequency domain indices of heart rate variability (HRV). A reliable, real-time classification and correction of ECG-derived heartbeats is a necessary prerequisite for an accurate online monitoring of HRV and cardiovascular control. We have developed a novel point-process-based method for real-time R-R interval error detection and correction. Given an R-wave event, we assume that the length of the next R-R interval follows a physiologically motivated, time-varying inverse Gaussian probability distribution. We then devise an instantaneous automated detection and correction procedure for erroneous and arrhythmic beats by using the information on the probability of occurrence of the observed beat provided by the model. We test our algorithm over two datasets from the PhysioNet archive. The Fantasia normal rhythm database is artificially corrupted with known erroneous beats to test both the detection procedure and correction procedure. The benchmark MIT-BIH Arrhythmia database is further considered to test the detection procedure of real arrhythmic events and compare it with results from previously published algorithms. Our automated algorithm represents an improvement over previous procedures, with best specificity for the detection of correct beats, as well as highest sensitivity to missed and extra beats, artificially misplaced beats, and for real arrhythmic events. A near-optimal heartbeat classification and correction, together with the ability to adapt to time-varying changes of heartbeat dynamics in an online fashion, may provide a solid base for building a more reliable real-time HRV monitoring device.

A combined HRV-fMRI approach to assess cortical control of cardiovagal modulation by motion sickness

Nausea is a commonly occurring symptom typified by epigastric discomfort with the urge to vomit. To date, the brain circuitry underlying the autonomic nervous system response to nausea has not been fully understood. Functional MRI (fMRI), together with a point process adaptive recursive algorithm for computation of the high-frequency (HF) index of heart rate variability (HRV) was combined to evaluate the brain circuitry underlying autonomic nervous system response to nausea. Alone, the point process analysis revealed increasing sympathetic and decreasing parasympathetic response during nausea with significant increased heart rate (HR) and decreased HF. The combined HRV-fMRI analysis demonstrated that the fMRI signal in the medial prefrontal cortex (MPFC) and pregenual anterior cingulate cortex (pgACC), regions of higher cortical functions and emotion showed a negative correlation at the baseline and a positive correlation during nausea. Overall, our findings confirm a sympathovagal shift (toward sympathetic) during nausea, which was related to brain activity in regions associated with emotion and higher cognitive function.

Instantaneous monitoring of sleep fragmentation by point process heart rate variability and respiratory dynamics

We present a novel, automatic point-process approach that is able to provide continuous, instantaneous estimates of heart rate variability (HRV) and respiratory sinus arrhythmia (RSA) in long duration data recordings such as those during an entire night of sleep. We analyze subjects with and without sleep apnea who underwent diagnostic polysomnography. The proposed algorithm is able to quantify multi-scale high time resolution autonomic signatures of sleep fragmentation, such as arousals and stage transitions, throughout an entire night. Results demonstrate the ability of our methods to track fast dynamic transitions from sleep to wake and between REM sleep and other sleep stages, providing resolution details not available in sleep scoring summaries. An automatic threshold-based procedure is further able to detect brief arousals, with the instantaneous indices characterizing specific arousal dynamic signatures.

Instantaneous assessment of autonomic cardiovascular control during general anesthesia

We present a comprehensive probabilistic point process framework to estimate and monitor the instantaneous heartbeat dynamics as related to specific cardiovascular control mechanisms and hemodynamics. Assessment of the model's statistics is established through the Wiener-Volterra theory and a multivariate autoregressive (AR) structure. A variety of instantaneous cardiovascular metrics, such as heart rate (HR), heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and baroreceptor-cardiac reflex (BRS), can be rigorously derived within a parametric framework and instantaneously updated with an adaptive algorithm. Instantaneous metrics of nonlinearity, such as the bispectrum of heartbeat intervals, can also be derived. We have applied the proposed point process framework to experimental recordings from healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. Results reveal interesting dynamic trends across different pharmacological interventions, confirming the ability of the algorithm to track important changes in cardiorespiratory elicited interactions, and pointing at our mathematical approach as a promising monitoring tool for an accurate, noninvasive assessment of general anesthesia.

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.

Using Laguerre expansion within point-process models of heartbeat dynamics: a comparative study

Point-process models have been recognized as a distinguished tool for the instantaneous assessment of heartbeat dynamics. Although not thoroughly linked to the physiology, nonlinear models also yield a more accurate quantification of cardiovascular control dynamics. Here, we propose a Laguerre expansion of the linear and nonlinear Wiener-Volterra kernels in order to account for the nonlinear and non-gaussian information contained in the ECG-derived heartbeat series while using a reduced number of parameters. Within an Inverse-Gaussian probability model, up to quadratic nonlinearities were considered to continuously estimate the dynamic spectrum and bispectrum. Results performed on 10 subjects undergoing a stand-up protocol show that this novel methodology improves on the algorithmic performances and, at the same time, more accurately characterizes sympatho-vagal changes to posture.

Instantaneous estimation of high-order nonlinear heartbeat dynamics by Lyapunov exponents

This paper introduces a novel methodology able to provide time varying estimates of the Lyapunov Spectrum within a point process framework. The algorithm is applied to ECG-derived data to characterize heartbeat nonlinear dynamics by using a cubic autoregressive point process model. Estimation of the model parameters is ensured by the Laguerre expansion of the Wiener-Volterra kernels along with a maximum local log-likelihood procedure. In addition to the instantaneous Lyapunov exponents, as well as indices related to higher order dynamic polyspectra, our method is also able to provide all the instantaneous time domain and frequency domain measures of instantaneous heart rate (HR) and heart rate variability (HRV) previously considered. Experimental results show that our method is able to track complex cardiovascular control dynamics during fast transitional gravitational changes.

Static and dynamic autonomic response with increasing nausea perception

BACKGROUND

Nausea is a commonly occurring symptom typified by epigastric discomfort with urge to vomit. The relationship between autonomic nervous system (ANS) outflow and increasing nausea perception is not fully understood.

METHODS

Our study employed a nauseogenic visual stimulus (horizontally translating stripes) while 17 female subjects freely rated transitions in nausea level and autonomic outflow was measured (heart rate, HR; heart rate variability, HRV; skin conductance response, SCR; respiratory rate). We also adopted a recent approach to continuous high-frequency (HF) HRV estimation to evaluate dynamic cardiovagal modulation.

RESULTS

HR increased from baseline for all increasing nausea transitions, especially transition to strong nausea (15.0 +/- 11.4 bpm), but decreased (-6.6 +/- 4.6 bpm) once the visual stimulus ceased. SCR also increased for all increasing nausea transitions, especially transition to strong nausea (1.76 +/- 1.68 microS), but continued to increase (0.52 +/- 0.65 microS) once visual stimulation ceased. LF/HF HRV increased following transition to moderate (1.54 +/- 2.11 a.u.) and strong (2.57 +/- 3.49 a.u.) nausea, suggesting a sympathetic shift in sympathovagal balance. However, dynamic HF HRV suggested that bursts of cardiovagal modulation precede transitions to higher nausea, perhaps influencing subjects to rate higher levels of nausea. No significant change in respiration rate was found.

CONCLUSIONS

Our results suggest that increasing nausea perception is associated with both increased sympathetic and decreased parasympathetic ANS modulation. These findings corroborate past ANS studies of nausea, applying perception-linked analyses and dynamic estimation of cardiovagal modulation in response to nausea.

A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis

Modeling heartbeat variability remains a challenging signal-processing goal in the presence of highly non-stationary cardiovascular control dynamics. We propose a novel differential autoregressive modeling approach within a point process probability framework for analyzing R-R interval and blood pressure variations. We apply the proposed model to both synthetic and experimental heartbeat intervals observed in time-varying conditions. The model is found to be extremely effective in tracking non-stationary heartbeat dynamics, as evidenced by the excellent goodness-of-fit performance. Results further demonstrate the ability of the method to appropriately quantify the non-stationary evolution of baroreflex sensitivity in changing physiological and pharmacological conditions.

A point process approach for analyzing gait variability dynamics

We present a novel statistical paradigm for modeling and analysis of gait variability which captures the natural point process structure of gait intervals and allows for definition of new measures instantaneous mean and standard deviation. We validate our model using two existing data sets from physionet.org. Results show an excellent model fit and yield insights into the underlying statistical structure behind human gait. Statistical analyses further corroborate previous findings of increased variability in gait at different speeds, both self-paced and metronome-paced, and reveal a significant increase in gait variability in Parkinson's subjects, as compared to young and elderly healthy subjects. These results indicate the validity of a point process approach to the analysis of gait, and the potential utility of incorporating instantaneous measures of gait into diagnostic or patient monitoring applications.

Dynamic assessment of baroreflex control of heart rate during induction of propofol anesthesia using a point process method

In this article, we present a point process method to assess dynamic baroreflex sensitivity (BRS) by estimating the baroreflex gain as focal component of a simplified closed-loop model of the cardiovascular system. Specifically, an inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by linear and bilinear bivariate regressions on both the previous R-R intervals (RR) and blood pressure (BP) beat-to-beat measures. The instantaneous baroreflex gain is estimated as the feedback branch of the loop with a point-process filter, while the RR-->BP feedforward transfer function representing heart contractility and vasculature effects is simultaneously estimated by a recursive least-squares filter. These two closed-loop gains provide a direct assessment of baroreflex control of heart rate (HR). In addition, the dynamic coherence, cross bispectrum, and their power ratio can also be estimated. All statistical indices provide a valuable quantitative assessment of the interaction between heartbeat dynamics and hemodynamics. To illustrate the application, we have applied the proposed point process model to experimental recordings from 11 healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. We present quantitative results during transient periods, as well as statistical analyses on steady-state epochs before and after propofol administration. Our findings validate the ability of the algorithm to provide a reliable and fast-tracking assessment of BRS, and show a clear overall reduction in baroreflex gain from the baseline period to the start of propofol anesthesia, confirming that instantaneous evaluation of arterial baroreflex control of HR may yield important implications in clinical practice, particularly during anesthesia and in postoperative care.

Estimating a State-Space Model from Point Process Observations: A Note on Convergence

Physiological signals such as neural spikes and heartbeats are discrete events in time, driven by continuous underlying systems. A recently introduced data-driven model to analyze such a system is a state-space model with point process observations, parameters of which and the underlying state sequence are simultaneously identified in a maximum likelihood setting using the expectation-maximization (EM) algorithm. In this note, we observe some simple convergence properties of such a setting, previously un-noticed. Simulations show that the likelihood is unimodal in the unknown parameters, and hence the EM iterations are always able to find the globally optimal solution.

Linear and nonlinear quantification of respiratory sinus arrhythmia during propofol general anesthesia

Quantitative evaluation of respiratory sinus arrhythmia (RSA) may provide important information in clinical practice of anesthesia and postoperative care. In this paper, we apply a point process method to assess dynamic RSA during propofol general anesthesia. Specifically, an inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by a linear or bilinear bivariate regression on the previous R-R intervals and respiratory measures. The estimated second-order bilinear interaction allows us to evaluate the nonlinear component of the RSA. The instantaneous RSA gain and phase can be estimated with an adaptive point process filter. The algorithm's ability to track non-stationary dynamics is demonstrated using one clinical recording. Our proposed statistical indices provide a valuable quantitative assessment of instantaneous cardiorespiratory control and heart rate variability (HRV) during general anesthesia.

Characterizing nonlinear heartbeat dynamics within a point process framework

Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R-R interval dynamics based on a nonlinear Volterra-Wiener expansion within a point process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to estimate instantaneous heart rate (HR) and heart rate variability (HRV) indexes, as well as the dynamic bispectrum characterizing higher order statistics of the nonstationary non-gaussian time series. The proposed point process probability heartbeat interval model was tested with synthetic simulations and two experimental heartbeat interval datasets. Results show that our model is useful in characterizing and tracking the inherent nonlinearity of heartbeat dynamics. As a feature, the fine temporal resolution allows us to compute instantaneous nonlinearity indexes, thus sidestepping the uneven spacing problem. In comparison to other nonlinear modeling approaches, the point process probability model is useful in revealing nonlinear heartbeat dynamics at a fine timescale and with only short duration recordings.

Point process time-frequency analysis of respiratory sinus arrhythmia under altered respiration dynamics

Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heartbeat. We propose an algorithm for quantifying instantaneous RSA as applied to heart beat interval and respiratory recordings under dynamic respiration conditions. The blood volume pressure derived heart beat series (pulse intervals, PI) are modeled as an inverse gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PI 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 by a frequency domain transfer function approach. The model is statistically validated using Kolmogorov-Smirnov (KS) 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. Experimental results confirm the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states.

Assessment of autonomic control and respiratory sinus arrhythmia using point process models of human heart beat dynamics

Tracking the autonomic control and respiratory sinus arrhythmia (RSA) from electrocardiogram and respiratory measurements is an important problem in cardiovascular control. We propose a point process adaptive filter algorithm based on an inverse Gaussian model to track heart beat intervals that incorporates respiratory measurements as a covariate and provides an analytic form for computing a dynamic estimate of RSA gain. We use Kolmogorov-Smirnov tests and autocorrelation function analyses to assess model goodness-of-fit. We illustrate the properties of the new dynamic estimate of RSA in the analysis of simulated heart beat data and actual heart beat data recorded from subjects in a four-state postural study of heart beat dynamics: control, sympathetic blockade, parasympathetic blockade, and combined sympathetic and parasympathetic blockade. In addition to giving an accurate description of the heart beat data, our adaptive filter algorithm confirms established findings pointing at a vagally mediated RSA and provides a new dynamic RSA estimate that can be used to track cardiovascular control between and within a broad range of postural, pharmacological, and age conditions. Our paradigm suggests a possible framework for designing a device for ambulatory monitoring and assessment of autonomic control in both laboratory research and clinical practice.

Assessment of hippocampal and autonomic neural activity by point process models

The development of statistical models that accurately describe the stochastic structure of neural oscillations is a fast growing area in quantitative research. In developing a novel statistical paradigm based on Bayes' theorem and the theory of point processes, we focused our recent research on two applications. The first studies how hippocampal neural activity represents and transmits information, whereas the second is aimed at characterizing activity of the central autonomic network as involved in cardiovascular control.

Characterizing nonlinear heartbeat dynamics within a point process framework

Heartbeat intervals are known to have nonlinear and non-stationary dynamics. In this paper, we propose a nonlinear Volterra-Wiener expansion modeling of human heartbeat dynamics within a point process framework. Inclusion of second-order nonlinearity allows us to estimate dynamic bispectrum. The proposed probabilistic model was examined with two recorded heartbeat interval data sets. Preliminary results show that our model is beneficial to characterize the inherent nonlinearity of the heartbeat dynamics.