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.

Mortality Prediction in Severe Congestive Heart Failure Patients with Multifractal Point-Process Modeling of Heartbeat Dynamics

Multifractal analysis of human heartbeat dynamics has been demonstrated to provide promising markers of Congestive Heart Failure (CHF). Yet, it crucially builds on the interpolation of RR intervals series, which has been generically performed with limited links to CHF pathophysiology. We devise a novel methodology estimating multifractal autonomic dynamics from heartbeat-derived series defined in the continuous time. We hypothesize that markers estimated from our novel framework are also effective for mortality prediction in severe CHF. We merge multifractal analysis within a methodological framework based on inhomogeneous point process models of heartbeat dynamics. Specifically, wavelet coefficients and wavelet leaders are computed over measures extracted from instantaneous statistics of probability density functions characterizing and predicting the time until the next heartbeat event occurs. The proposed approach is tested on data from 94 CHF patients, aiming at predicting survivor and non-survivor individuals as determined after a 4 years follow up. Instantaneous markers of vagal and sympatho-vagal dynamics display power-law scaling for a large range of scales, from s to s. Using standard SVM algorithms, the proposed inhomogeneous point-process representation based multifractal analysis achieved the best CHF mortality prediction accuracy of 79.11 % (sensitivity 90.48%, specificity 67.74%). Our results suggest that heartbeat scaling and multifractal properties in CHF patients are not generated at the sinus-node level, but rather by the intrinsic action of vagal short-term control and of sympatho-vagal fluctuations associated with circadian cardiovascular control, especially within the VLF band. These markers might provide critical information in devising a clinical tool for individualized prediction of survivor and non-survivor CHF patients.

Point-process Nonlinear Autonomic Assessment of Depressive States in Bipolar Patients

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems”.

Objectives: The goal of this work is to apply a computational methodology able to characterize mood states in bipolar patients through instantaneous analysis of heartbeat dynamics.

Methods: A Point-Process-based Nonlinear Autoregressive Integrative (NARI) model is applied to analyze data collected from five bipolar patients (two males and three females, age 42.4 ± 10.5 range 32−56) undergoing a dedicated affective elicitation protocol using images from the International Affective Picture System (IAPS) and Thematic Apperception Test (TAT). The study was designed within the European project PSYCHE (Personalised monitoring SYstems for Care in mental HEalth).

Results: Results demonstrate that the inclusion of instantaneous higher order spectral (HOS) features estimated from the NARI nonlinear assessment significantly improves the accuracy in successfully recognizing specific mood states such as euthymia and depression with respect to results using only linear indices. In particular, a specificity of 74.44% using the instantaneous linear features set, and 99.56% using also the nonlinear feature set were achieved. Moreover, IAPS emotional elicitation resulted in a more discriminant procedure with respect to the TAT elicitation protocol.

Conclusions: A significant pattern of instantaneous heartbeat features was found in depressive and euthymic states despite the inter-subject variability. The presented point-process Heart Rate Variability (HRV) nonlinear methodology provides a promising application in the field of mood assessment in bipolar patients.

Heartbeat Complexity Modulation in Bipolar Disorder during Daytime and Nighttime

This study reports on the complexity modulation of heartbeat dynamics in patients affected by bipolar disorder. In particular, a multiscale entropy analysis was applied to the R-R interval series, that were derived from electrocardiographic (ECG) signals for a group of nineteen subjects comprised of eight patients and eleven healthy control subjects. They were monitored using a textile-based sensorized t-shirt during the day and overnight for a total of 47 diurnal and 27 nocturnal recordings. Patients showed three different mood states: depression, hypomania and euthymia. Results show a clear loss of complexity during depressive and hypomanic states as compared to euthymic and healthy control states. In addition, we observed that a more significant complexity modulation among healthy and pathological mood states occurs during the night. These findings suggest that bipolar disorder is associated with an enhanced sleep-related dysregulation of the Autonomic Nervous System (ANS) activity, and that heartbeat complex dynamics may serve as a viable marker of pathological conditions in mental health.

Heart rate variability analysis during muscle fatigue due to prolonged isometric contraction

Fatigue can be defined as the muscular condition occurring before the inability to perform a task. It can be assessed through the evaluation of the median and mean frequency of the spectrum of the surface electromyography series. Previous studies investigated the relationship between heartbeat dynamics and muscular activity. However, exploitation of such cardiovascular measures to automatically identify muscle fatigue during fatiguing exercises is still missing. To this extent, HRV signals were gathered from 32 subjects during an isometric contraction task, and features defined in the time, frequency and nonlinear domains were investigated. We used surface electromyography to label the occurrence of muscle fatigue. Statistically significant differences were observed by comparing features related to fatigued subjects with the non-fatigued ones. Moreover, a pattern recognition system capable to achieve an average accuracy of 78.24% was implemented. These results confirmed the hypothesis that a relationship between heartbeat dynamics and muscle fatigue might exist.

Validation of instantaneous bispectral high-frequency power of heartbeat dynamics as a marker of cardiac vagal activity

Nonlinear analysis has been advocated as a very powerful methodological framework to study physiological signals, particularly when applied to heartbeat dynamics. To this extent, estimation of high-frequency (0.15-0.40 Hz) power from bispectra of cardiovascular variability series has been engaged as a marker of nonlinear vagal activity. Nevertheless, a proper validation of this specific measure has not been yet performed. In this study, we estimate instantaneous, nonlinear bispectral indices during postural changes under sympathetic and parasympathetic nervous system blockade. The analysis was performed on data from 14 healthy subjects undergoing a control supine-to-upright routine where they were selectively administered either atropine or propanolol. Instantaneous bispectra were obtained through Laguerre-transformed, linear and nonlinear kernels of a Wiener-Volterra model applied to heartbeat dynamics, embedded into a recently proposed inhomogeneous point-process framework. Results demonstrate that the integration of bispectra accounting for nonlinear cardiovascular control dynamics within the high-frequency band provides potentially reliable markers of vagal activity.

Assessment of instantaneous cardiovascular dynamics from video plethysmography

Although there is growing interest in estimating cardiovascular information using contactless video plethysmography (VP), an in-depth validation of time-varying, nonlinear dynamics of the related pulse rate variability is still missing. In this study we estimate the heartbeat through VP and standard ECG, and employ inhomogeneous point-process nonlinear models to assess instantaneous heart rate variability measures defined in the time, frequency, and bispectral domains. Experimental data were gathered from 60 young healthy subjects (age: 24±3 years) undergoing postural changes (rest-to-stand maneuver). Video recordings are processed using our recently proposed method based on zero-phase component analysis. Results show that, at a group level, there is an overall agreement between linear and nonlinear indices computed from ECG and VP during resting state conditions. However, significant differences are found, especially in the bispectral domain, when considering data gathered while standing. Although significant differences exist between cardiovascular estimates from VP and ECG, results can be considered very promising as instantaneous sympatho-vagal changes were correctly identified. More research is indeed needed to improve on the precise estimation of nonlinear sympatho-vagal interactions.

Nonlinear analysis of heart rate variability for the assessment of Dysphoria

Dysphoric patients show symptoms associated with Major Depression, although within a narrowed symptomatology spectrum. In prevailing practice, clinicians assess Dysphoria through psychological scores and questionnaires exclusively, therefore without taking into account objective biomarkers. In this study, we investigated heartbeat linear and nonlinear dynamics aiming to an objective assessment of Dysphoria. To this end, we derived standard and nonlinear measures from heart rate variability (HRV) series gathered from dysphoric (n=14) and nondysphoric (n=17) undergraduate students during 5 minutes of resting state. We performed both statistical and pattern recognition analyses in order to discern the two groups. Results showed significant group-wise differences in HRV nonlinear metrics exclusively, suggesting a crucial role of nonlinear sympatho-vagal dynamics in Dysphoria. Furthermore, we achieved a classification accuracy of 77.52% for the automatic identification of Dysphoria at a single-subject level.

Estimating directed brain-brain and brain-heart connectivity through globally conditioned Granger causality approaches

While a large body of research has focused on the study of within-brain physiological networks (i.e. brain connectivity) as well as their disease-related aberration, few investigators have focused on estimating the directionality of these brain-brain interaction which, given the complexity of brain networks, should be properly conditioned in order to avoid the high number of false positives commonly encountered when using bivariate approaches to brain connectivity estimation. Additionally, the constituents of a number of brain subnetworks, and in particular of the central autonomic network (CAN), are still not completely determined. In this study we present and validate a global conditioning approach to reconstructing directed networks using complex synthetic networks of nonlinear oscillators. We then employ our framework, along with a probabilistic model for heartbeat generation, to characterize the directed functional connectome of the human brain and to establish which parts of this connectome effect the directed central modulation of peripheral autonomic cardiovascular control. We demonstrate the effectiveness of our conditioning approach and unveil a top-down directed influence of the default mode network on the salience network, which in turn is seen to be the strongest modulator of directed autonomic cardiovascular control.

Multiscale properties of instantaneous parasympathetic activity in severe congestive heart failure: A survivor vs non-survivor study

Multifractal analysis of cardiovascular variability series is an effective tool for the characterization of pathological states associated with congestive heart failure (CHF). Consequently, variations of heartbeat scaling properties have been associated with the dynamical balancing of nonlinear sympathetic/vagal activity. Nevertheless, whether vagal dynamics has multifractal properties yet alone is currently unknown. In this study, we answer this question by conducting multifractal analysis through wavelet leader-based multiscale representations of instantaneous series of vagal activity as estimated from inhomogeneous point process models. Experimental tests were performed on data gathered from 57 CHF patients, aiming to investigate the automatic recognition accuracy in predicting survivor and non-survivor patients after a 4 years follow up. Results clearly indicate that, on both CHF groups, the instantaneous vagal activity displays power-law scaling for a large range of scales, from ≃ 0.5s to ≃ 100s. Using standard SVM algorithms, this information also allows for a prediction of mortality at a single-subject level with an accuracy of 72.72%.

A novel Heart Rate Variability analysis using Lagged Poincaré plot: A study on hedonic visual elicitation

This paper reports on a novel method for the analysis of Heart Rate Variability (HRV) through Lagged Poincaré Plot (LPP) theory. Specifically a hybrid method, LPP_symb, including LPP quantifiers and related symbolic dynamics was proposed. LPP has been applied to investigate the autonomic response to pleasant and unpleasant pictures extracted from the International Affective Picture System (IAPS). IAPS pictures are standardized in terms of level of arousal, i.e. the intensity of the evoked emotion, and valence, i.e. the level of pleasantness/unpleasantness, according to the Circumplex model of Affects (CMA). Twenty-two healthy subjects were enrolled in the experiment, which comprised four sessions with increasing arousal level. Within each session valence increased from positive to negative. An ad-hoc pattern recognition algorithm using a Leave-One-Subject-Out (LOSO) procedure based on a Quadratic Discriminant Classifier (QDC) was implemented. Our pattern recognition system was able to classify pleasant and unpleasant sessions with an accuracy of 71.59%. Therefore, we can suggest the use of the LPP_symb for emotion recognition.

Characterization of doctor-patient communication using heartbeat nonlinear dynamics: A preliminary study using Lagged Poincaré Plots

Emphatic doctor-patient communication has been associated with an improved psycho-physiological well-being involving cardiovascular and neuroendocrine responses. Nevertheless, a comprehensive assessment of heartbeat linear and nonlinear/complex dynamics throughout the communication of a life-threatening disease has not been performed yet. To this extent, we here study heart rate variability (HRV) series gathered from 17 subjects while watching a video where an oncologist discloses the diagnosis of a cancer metastasis to a patient. Further 17 subjects watched the same video including additional affective emphatic contents. For the assessment of the two groups, linear heartbeat dynamics was quantified through measures defined in the time and frequency domains, whereas nonlinear/complex dynamics referred to measures of entropy, and combined Lagged Poincare Plots (LPP) and symbolic analyses. Considering differences between the beginning and the end of the video, results from non-parametric statistical tests demonstrated that the group watching emphatic contents showed HRV changes in the LF/HF ratio exclusively. Conversely, the group watching the purely informative video showed changes in vagal activity (i.e., HF power), LF/HF ratio, as well as LPP measures. Additionally, a Support Vector Machine algorithm including HRV nonlinear/complex information was able to automatically discern between groups with an accuracy of 76.47%. We therefore propose the use of heartbeat nonlinear/complex dynamics to objectively assess the empathy level of healthy women.

Instantaneous Transfer Entropy for the Study of Cardiovascular and Cardiorespiratory Nonstationary Dynamics

OBJECTIVE

Measures of transfer entropy (TE) quantify the direction and strength of coupling between two complex systems. Standard approaches assume stationarity of the observations, and therefore are unable to track time-varying changes in nonlinear information transfer with high temporal resolution. In this study, we aim to define and validate novel instantaneous measures of TE to provide an improved assessment of complex nonstationary cardiorespiratory interactions.

METHODS

We here propose a novel instantaneous point-process TE (ipTE) and validate its assessment as applied to cardiovascular and cardiorespiratory dynamics. In particular, heartbeat and respiratory dynamics are characterized through discrete time series, and modeled with probability density functions predicting the time of the next physiological event as a function of the past history. Likewise, nonstationary interactions between heartbeat and blood pressure dynamics are characterized as well. Furthermore, we propose a new measure of information transfer, the instantaneous point-process information transfer (ipInfTr), which is directly derived from point-process-based definitions of the Kolmogorov-Smirnov distance.

RESULTS AND CONCLUSION

Analysis on synthetic data, as well as on experimental data gathered from healthy subjects undergoing postural changes confirms that ipTE, as well as ipInfTr measures are able to dynamically track changes in physiological systems coupling.

SIGNIFICANCE

This novel approach opens new avenues in the study of hidden, transient, nonstationary physiological states involving multivariate autonomic dynamics in cardiovascular health and disease. The proposed method can also be tailored for the study of complex multisystem physiology (e.g., brain-heart or, more in general, brain-body interactions).

Quantitative heartbeat coupling measures in human-horse interaction

We present a study focused on a quantitative estimation of a human-horse dynamic interaction. A set of measures based on magnitude and phase coupling between heartbeat dynamics of both humans and horses in three different conditions is reported: no interaction, visual/olfactory interaction and grooming. Specifically, Magnitude Squared Coherence (MSC), Mean Phase Coherence (MPC) and Dynamic Time Warping (DTW) have been used as estimators of the amount of coupling between human and horse through the analysis of their heart rate variability (HRV) time series in a group of eleven human subjects, and one horse. The rationale behind this study is that the interaction of two complex biological systems go towards a coupling process whose dynamical evolution is modulated by the kind and time duration of the interaction itself. We achieved a congruent and consistent statistical significant difference for all of the three indices. Moreover, a Nearest Mean Classifier was able to recognize the three classes of interaction with an accuracy greater than 70%. Although preliminary, these encouraging results allow a discrimination of three distinct phases in a real human-animal interaction opening to the characterization of the empirically proven relationship between human and horse.

Automatic recognition of pleasant content of odours through ElectroEncephaloGraphic activity analysis

This study presents a machine learning approach applied to ElectroEnchephaloGraphic (EEG) response in a group of subjects when exposed to a controlled olfactory stimulation experiment. In the literature, in fact, there are controversial results on EEG response to odorants. This study proposes a robust leave-one-subject-out classification method to recognize features extracted from EEG signals belonging to pleasant or unpleasant olfactory stimulation classes. An accuracy of 75% has been achieved in a group of 32 subjects. Moreover a set of features extracted from lateral electrodes emphasized that right and left hemispheres behave differently when the subjects are exposed to pleasant or unpleasant odours stimuli.

Causal brain-heart information transfer during visual emotional elicitation in healthy subjects: Preliminary evaluations and future perspectives

Complex heartbeat dynamics is known to reflect subject's emotional state, thanks to numerous links to brain cortical and subcortical regions. Likewise, specific brain regions are deeply involved in vagally-mediated emotional processing and regulation. Nevertheless, although the brain-heart interplay has been studied during visual emotion elicitation, directional interactions have not been investigated so far. To fill this gap, in this study we investigate brain-heart dynamics during emotional elicitation in healthy subjects through measures of Granger causality (GC) between the two physiological systems. Data were gathered from 22 healthy volunteers who underwent pleasant/ unpleasant affective elicitation using pictures from the International Affective Picture System. Neutral emotional stimuli were elicited as well. High density electroencephalogram (EEG) signals were processed to obtain time-varying maps of cortical activation, whereas the associated instantaneous cardiovascular dynamics was estimated through inhomogeneous point-process models. Concerning the information transfer brain-to-heart, GE highlighted significant valence-dependent lateralization with respect to resting states. Furthermore, as a proof of concept, the study of heart-to-brain dynamics considering EEG oscillations in the γ band (30-45 Hz) highlighted differential information transfer between neutral and positive elicitations directed to the prefrontal cortex.

Monitoring voluntary blink magnitude through a wearable eye-tracking system: A preliminary study

This study proposes a novel approach to measure the contractile force of eye blink that is generally obtained from the orbicularis oculi activity through Ocular ElectroMyo-Graphy (O-EMG). Here, O-EMG is compared with the eye information acquired through a wearable head-mounted eye-tracking system in order to investigate the possibility of using the eye-tracking in place of the O-EMG. Eight subjects were simultaneously monitored through an O-EMG and the eye-tracker while they were performing a structured protocol implying a variation in the blink contractile strength. Results showed that eye-tracking features were able to statistically discriminate three kinds of contractile forces similarly to EMG features. The consequent correlation analysis revealed that all the EMG-related features were significantly correlated with the eye-tracking ones with a p-value <;10^-6. Moreover, considering the extracted eye-tracking features, i.e. Integrated Gaze Path (IGP) and Eye-closed Duration (ECD), IGP reported a higher Spearman's correlation values with eye-blink reflex magnitude (EBM) than ECD. These encouraging results suggest that the ocular information extracted from the eye-tracking could be profitably used in non-invasive ecological environments where wearability and comfortability play a crucial role in detecting spontaneous response.

Perspective: It's All About Time

New knowledge on multi-scale temporal dynamics linking nanobio-time series, seasonal changes, immune response, and gut mictobiota can milestone (neuro) science soon.

Reliability of Lagged Poincaré Plot Parameters in Ultrashort Heart Rate Variability Series: Application on Affective Sounds

The number of studies about ultrashort cardiovascular time series is increasing because of the demand for mobile applications in telemedicine and e-health monitoring. However, the current literature still needs a proper validation of heartbeat nonlinear dynamics assessment from ultrashort time series. This paper reports on the reliability of the Lagged Poincaré Plot (LPP) parameters-calculated from ultrashort cardiovascular time series. Reliability is studied on simulated as well as on real RR series. Simulated RR series are generated and LPP parameters estimated for ultrashort time series (from 15 to 60 s) are compared to those estimated from 1 h. All LPP parameters estimated from time series longer than 35 s presented a Spearman's correlation coefficient higher than 0.99. RR series acquired from 32 healthy subjects during 5-min resting state sessions are used to test the LPP approach in experimental data. The usefulness of ultrashort term parameters in real data is accomplished also studying their ability to discriminate positive and negative valence of auditory stimuli taken from the International Affective Digitized Sound System (IADS) dataset. The achieved accuracies in the recognition of elicitation along the valence dimension, using only the LPP parameters, were of 77.78% for 1 min 28 s series, and of 79.17% for 35 s series.

Disentanglement of sympathetic and parasympathetic activity by instantaneous analysis of human heartbeat dynamics

Spectral analysis of heart rate variability (HRV) is one of the most effective techniques for the assessment of the influence of the autonomic nervous system (ANS) on the heartbeat. Despite its widespread use, it has been demonstrated that HRV subdivision in the low frequency (LF) and high frequency (HF) bands does not accurately reflect separate sympathetic and parasympathetic influences, respectively, mainly due to overlap of the two branches in the low frequencies. Here we propose two novel indices, namely the instantaneous sympathetic autonomic index (SAI) and parasympathetic autonomic index (PAI), that are able to separately assess the time-varying ANS synergic functions. The application of the paradigm is presented here by associating proper combinations of orthonormal Laguerre functions defined within the heartbeat point-process continuous model. Preliminary results from ten subjects recorded during a tilt-table protocol show that the proposed methodology, differently than the traditional spectral parameters, is able to separately track the independent changes associated with the two ANS branches.

Stochastic modeling of spontaneous bursting activity to simulate neural responses of in-vitro networks on multielectrode arrays

The study of neuronal bursting activity, observed in cell-culture, is physiologically important because is correlated with synaptic transmission, plasticity, and information processing. However, besides strong ethical issues related to the use of animal models, there are many limitations due to experimental setup and neural signaling acquisition. In this study, we propose a stochastic modeling approach to simulate neural dynamics observed in networks of neocortical neurons from an ex vivo normal mouse. Specifically, we devised a stochastic version of the Izhichevich's model of cortical neurons, and simulated a network of excitatory and inhibitory neurons also accounting for cell signaling delays. No specific learning rules were used throughout the simulation time. Results show spontaneous bursting activity while mimicking balanced and hyper-excitable networks through modulation of the inhibitory synaptic weights. Furthermore, we validate our findings comparing the simulated cumulative probability functions of the firing spike histograms with the ones obtained in cultured networks of dissociated cortical neurons from ex vivo mice.

Brain-heart linear and nonlinear dynamics during visual emotional elicitation in healthy subjects

This study investigates brain-heart dynamics during visual emotional elicitation in healthy subjects through linear and nonlinear coupling measures of EEG spectrogram and instantaneous heart rate estimates. To this extent, affective pictures including different combinations of arousal and valence levels, gathered from the International Affective Picture System, were administered to twenty-two healthy subjects. Time-varying maps of cortical activation were obtained through EEG spectral analysis, whereas the associated instantaneous heartbeat dynamics was estimated using inhomogeneous point-process linear models. Brain-Heart linear and nonlinear coupling was estimated through the Maximal Information Coefficient (MIC), considering EEG time-varying spectra and point-process estimates defined in the time and frequency domains. As a proof of concept, we here show preliminary results considering EEG oscillations in the θ band (4-8 Hz). This band, indeed, is known in the literature to be involved in emotional processes. MIC highlighted significant arousal-dependent changes, mediated by the prefrontal cortex interplay especially occurring at intermediate arousing levels. Furthermore, lower and higher arousing elicitations were associated to not significant brain-heart coupling changes in response to pleasant/unpleasant elicitations.