42 results on '"Barbieri, Riccardo"'
Search Results
2. Quantifying multidimensional control mechanisms of cardiovascular dynamics during multiple concurrent stressors
- Author
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Ghiasi, Shadi, Greco, Alberto, Faes, Luca, Javorka, Michal, Barbieri, Riccardo, Scilingo, Enzo Pasquale, and Valenza, Gaetano
- Published
- 2021
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3. Assessment of Instantaneous Heartbeat Dynamics in amnestic Mild Cognitive Impairment
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Toschi, Nicola, Valenza, Gaetano, Citi, Luca, Guerrisi, Maria, Orsolini, Stefano, Tessa, Carlo, Diciotti, Stefano, Barbieri, Riccardo, Magjarevic, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Eskola, Hannu, editor, Väisänen, Outi, editor, Viik, Jari, editor, and Hyttinen, Jari, editor
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- 2018
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4. Instantaneous monitoring of heart beat dynamics during anesthesia and sedation
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Valenza, Gaetano, Akeju, Oluwaseun, Pavone, Kara J, Citi, Luca, Hartnack, Katharine E, Sampson, Aaron, Purdon, Patrick L, Brown, Emery N, and Barbieri, Riccardo
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- 2014
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5. Optimization of respiratory-gated auricular vagus afferent nerve stimulation for the modulation of blood pressure in hypertension.
- Author
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Garcia, Ronald G., Staley, Rachel, Aroner, Sarah, Stowell, Jessica, Sclocco, Roberta, Napadow, Vitaly, Barbieri, Riccardo, and Goldstein, Jill M.
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VAGUS nerve stimulation ,BLOOD pressure ,HEART beat ,REGULATION of blood pressure ,HYPERTENSION - Abstract
Background: The objective of this pilot study was to identify frequencydependent effects of respiratory-gated auricular vagus afferent nerve stimulation (RAVANS) on the regulation of blood pressure and heart rate variability in hypertensive subjects and examine potential differential effects by sex/gender or race. Methods: Twenty hypertensive subjects (54.55 ± 6.23 years of age; 12 females and 8 males) were included in a within-person experimental design and underwent five stimulation sessions where they received RAVANS at different frequencies (i.e., 2 Hz, 10 Hz, 25 Hz, 100 Hz, or sham stimulation) in a randomized order. EKG and continuous blood pressure signals were collected during a 10-min baseline, 30-min stimulation, and 10-min poststimulation periods. Generalized estimating equations (GEE) adjusted for baseline measures were used to evaluate frequency-dependent effects of RAVANS on heart rate, high frequency power, and blood pressure measures, including analyses stratified by sex and race. Results: Administration of RAVANS at 100 Hz had significant overall effects on the reduction of heart rate (b = 2.03, p = 0.002). It was also associated with a significant reduction of diastolic (b = 1.90, p = 0.01) and mean arterial blood pressure (b = 2.23, p = 0.002) in Black hypertensive participants and heart rate in female subjects (b = 2.83, p = 0.01) during the post-stimulation period when compared to sham. Conclusion: Respiratory-gated auricular vagus afferent nerve stimulation exhibits frequency-dependent rapid effects on the modulation of heart rate and blood pressure in hypertensive patients that may further differ by race and sex. Our findings highlight the need for the development of optimized stimulation protocols that achieve the greatest effects on the modulation of physiological and clinical outcomes in this population. [ABSTRACT FROM AUTHOR]
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- 2022
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6. Characterizing cardiac autonomic dynamics of fear learning in humans.
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Battaglia, Simone, Orsolini, Stefano, Borgomaneri, Sara, Barbieri, Riccardo, Diciotti, Stefano, and di Pellegrino, Giuseppe
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LEARNING ,HEART beat ,TRANSIENTS (Dynamics) ,AUTONOMIC nervous system ,FREQUENCY-domain analysis ,CONTEXTUAL learning - Abstract
Understanding transient dynamics of the autonomic nervous system during fear learning remains a critical step to translate basic research into treatment of fear‐related disorders. In humans, it has been demonstrated that fear learning typically elicits transient heart rate deceleration. However, classical analyses of heart rate variability (HRV) fail to disentangle the contribution of parasympathetic and sympathetic systems, and crucially, they are not able to capture phasic changes during fear learning. Here, to gain deeper insight into the physiological underpinnings of fear learning, a novel frequency‐domain analysis of heart rate was performed using a short‐time Fourier transform, and instantaneous spectral estimates extracted from a point‐process modeling algorithm. We tested whether spectral transient components of HRV, used as a noninvasive probe of sympathetic and parasympathetic mechanisms, can dissociate between fear conditioned and neutral stimuli. We found that learned fear elicited a transient heart rate deceleration in anticipation of noxious stimuli. Crucially, results revealed a significant increase in spectral power in the high frequency band when facing the conditioned stimulus, indicating increased parasympathetic (vagal) activity, which distinguished conditioned and neutral stimuli during fear learning. Our findings provide a proximal measure of the involvement of cardiac vagal dynamics into the psychophysiology of fear learning and extinction, thus offering new insights for the characterization of fear in mental health and illness. Understanding transient dynamics of the autonomic nervous system during fear learning remains a critical step to translate basic research into treatment of fear‐related disorders. Here, using a novel frequency‐based analysis to capture transient changes of heart rate variability, our findings highlight a selective modulation of the high frequency (HF) band of the power spectrum, when facing fear‐relevant stimuli. Crucially, these results reveal specific increased parasympathetic (vagal) activity during fear learning in humans. [ABSTRACT FROM AUTHOR]
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- 2022
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7. Point process time–frequency analysis of dynamic respiratory patterns during meditation practice
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Kodituwakku, Sandun, Lazar, Sara W., Indic, Premananda, Chen, Zhe, Brown, Emery N., and Barbieri, Riccardo
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- 2012
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8. Dynamic Assessment of Baroreflex Control of Heart Rate During Induction of Propofol Anesthesia Using a Point Process Method
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Chen, Zhe, Purdon, Patrick L., Harrell, Grace, Pierce, Eric T., Walsh, John, Brown, Emery N., and Barbieri, Riccardo
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- 2011
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9. Functional brain-heart interplay extends to the multifractal domain.
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Catrambone, Vincenzo, Barbieri, Riccardo, Wendt, Herwig, Abry, Patrice, and Valenza, Gaetano
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HEART beat , *SIGNAL processing , *CENTRAL nervous system , *HEART , *INTEROCEPTION , *ALPHA rhythm - Abstract
The study of functional brain-heart interplay has provided meaningful insights in cardiology and neuroscience. Regarding biosignal processing, this interplay involves predominantly neural and heartbeat linear dynamics expressed via time and frequency domain-related features. However, the dynamics of central and autonomous nervous systems show nonlinear and multifractal behaviours, and the extent to which this behaviour influences brain-heart interactions is currently unknown. Here, we report a novel signal processing framework aimed at quantifying nonlinear functional brain-heart interplay in the non-Gaussian and multifractal domains that combines electroencephalography (EEG) and heart rate variability series. This framework relies on a maximal information coefficient analysis between nonlinear multiscale features derived from EEG spectra and from an inhomogeneous point-process model for heartbeat dynamics. Experimental results were gathered from 24 healthy volunteers during a resting state and a cold pressor test, revealing that synchronous changes between brain and heartbeat multifractal spectra occur at higher EEG frequency bands and through nonlinear/complex cardiovascular control. We conclude that significant bodily, sympathovagal changes such as those elicited by coldpressure stimuli affect the functional brain-heart interplay beyond second-order statistics, thus extending it to multifractal dynamics. These results provide a platform to define novel nervous-system-targeted biomarkers. [ABSTRACT FROM AUTHOR]
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- 2021
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10. Advanced computation in cardiovascular physiology: new challenges and opportunities.
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Valenza, Gaetano, Faes, Luca, Toschi, Nicola, and Barbieri, Riccardo
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ARTIFICIAL intelligence ,PATHOLOGICAL physiology ,DEEP learning ,PHYSIOLOGY ,AUTONOMIC nervous system ,MACHINE learning ,INTEROCEPTION - Abstract
Recent developments in computational physiology have successfully exploited advanced signal processing and artificial intelligence tools for predicting or uncovering characteristic features of physiological and pathological states in humans. While these advanced tools have demonstrated excellent diagnostic capabilities, the high complexity of these computational 'black boxes' may severely limit scientific inference, especially in terms of biological insight about both physiology and pathological aberrations. This theme issue highlights current challenges and opportunities of advanced computational tools for processing dynamical data reflecting autonomic nervous system dynamics, with a specific focus on cardiovascular control physiology and pathology. This includes the development and adaptation of complex signal processing methods, multivariate cardiovascular models, multiscale and nonlinear models for central-peripheral dynamics, as well as deep and transfer learning algorithms applied to large datasets. The width of this perspective highlights the issues of specificity in heartbeat-related features and supports the need for an imminent transition from the black-box paradigm to explainable and personalized clinical models in cardiovascular research. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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11. Mortality Prediction in Severe Congestive Heart Failure Patients With Multifractal Point-Process Modeling of Heartbeat Dynamics.
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Valenza, Gaetano, Wendt, Herwig, Kiyono, Ken, Hayano, Junichro, Watanabe, Eiichi, Yamamoto, Yoshiharu, Abry, Patrice, and Barbieri, Riccardo
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HEART beat ,MULTIFRACTALS ,CONGESTIVE heart failure ,PATHOLOGICAL physiology ,AUTONOMIC nervous system - Abstract
Background: 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 interval series which has been generically performed with limited links to CHF pathophysiology. Objective: 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. Methods: 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 nonsurvivor individuals as determined after a four years follow up. Results and Discussion: Instantaneous markers of vagal and sympatho-vagal dynamics display power-law scaling for a large range of scales, from $\simeq 0.5$ to $\simeq 100$ s. Using standard support vector machine 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%). Conclusion: 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 very low frequency band. These markers might provide critical information in devising a clinical tool for individualized prediction of survivor and nonsurvivor CHF patients. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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12. Measures of sympathetic and parasympathetic autonomic outflow from heartbeat dynamics.
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Valenza, Gaetano, Citi, Luca, Saul, J. Philip, and Barbieri, Riccardo
- Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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13. Analysis of Instantaneous Linear, Nonlinear and Complex Cardiovascular Dynamics from Videophotoplethysmography.
- Author
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Valenza, Gaetano, Iozzia, Luca, Cerina, Luca, Mainardi, Luca, and Barbieri, Riccardo
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BIOLOGICAL models ,CARDIOVASCULAR system ,CHAOS theory ,ELECTROCARDIOGRAPHY ,PLETHYSMOGRAPHY ,STATISTICS ,VIDEO recording - Abstract
Background: There is a fast growing interest in the use of non-contact devices for health and performance assessment in humans. In particular, the use of non-contact videophotoplethysmography (vPPG) has been recently demonstrated as a feasible way to extract cardiovascular information. Nevertheless, proper validation of vPPG-derived heartbeat dynamics is still missing.Objective: We aim to an in-depth validation of time-varying, linear and nonlinear/complex dynamics of the pulse rate variability extracted from vPPG.Methods: We apply inhomogeneous pointprocess nonlinear models to assess instantaneous measures defined in the time, frequency, and bispectral domains as estimated through vPPG and standard ECG. Instantaneous complexity measures, such as the instantaneous Lyapunov exponents and the recently defined inhomogeneous point-process approximate and sample entropy, were estimated as well. Video recordings were processed using our recently proposed method based on zerophase principal component analysis. Experimental data were gathered from 60 young healthy subjects (age: 24±3 years) undergoing postural changes (rest-to-stand maneuver).Results: Group averaged results show that there is an overall agreement between linear and nonlinear/complexity indices computed from ECG and vPPG during resting state conditions. However, important differences are found, particularly in the bispectral and complexity domains, in recordings where the subjects has been instructed to stand up.Conclusions: Although significant differences exist between cardiovascular estimates from vPPG and ECG, it is very promising that instantaneous sympathovagal changes, as well as time-varying complex dynamics, were correctly identified, especially during resting state. In addition to a further improvement of the video signal quality, more research is advocated towards a more precise estimation of cardiovascular dynamics by a comprehensive nonlinear/complex paradigm specifically tailored to the non-contact quantification. [ABSTRACT FROM AUTHOR]- Published
- 2018
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14. Instantaneous Transfer Entropy for the Study of Cardiovascular and Cardiorespiratory Nonstationary Dynamics.
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Valenza, Gaetano, Faes, Luca, Citi, Luca, Orini, Michele, and Barbieri, Riccardo
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CARDIOPULMONARY system ,KNOWLEDGE transfer ,PHYSIOLOGY ,BIOMEDICAL engineering ,HEMODYNAMICS - Abstract
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). [ABSTRACT FROM PUBLISHER]
- Published
- 2018
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15. Predicting Bradycardia in Preterm Infants Using Point Process Analysis of Heart Rate.
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Gee, Alan H., Barbieri, Riccardo, Paydarfar, David, and Indic, Premananda
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BRADYCARDIA , *POINT processes , *PREMATURE infants , *HEART beat , *ELECTROCARDIOGRAPHY - Abstract
Objective: Episodes of bradycardia are common and recur sporadically in preterm infants, posing a threat to the developing brain and other vital organs. We hypothesize that bradycardias are a result of transient temporal destabilization of the cardiac autonomic control system and that fluctuations in the heart rate signal might contain information that precedes bradycardia. We investigate infant heart rate fluctuations with a novel application of point process theory. Methods: In ten preterm infants, we estimate instantaneous linear measures of the heart rate signal, use these measures to extract statistical features of bradycardia, and propose a simplistic framework for prediction of bradycardia. Results: We present the performance of a prediction algorithm using instantaneous linear measures (mean area under the curve = 0.79 ± 0.018) for over 440 bradycardia events. The algorithm achieves an average forecast time of 116 s prior to bradycardia onset (FPR = 0.15). Our analysis reveals that increased variance in the heart rate signal is a precursor of severe bradycardia. This increase in variance is associated with an increase in power from low content dynamics in the LF band (0.04–0.2 Hz) and lower multiscale entropy values prior to bradycardia. Conclusion: Point process analysis of the heartbeat time series reveals instantaneous measures that can be used to predict infant bradycardia prior to onset. Significance: Our findings are relevant to risk stratification, predictive monitoring, and implementation of preventative strategies for reducing morbidity and mortality associated with bradycardia in neonatal intensive care units. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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16. 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.
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Duggento, Andrea, Bianciardi, Marta, Passamonti, Luca, Wald, Lawrence L., Guerrisi, Maria, Barbieri, Riccardo, and Toschi, Nicola
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AUTONOMIC nervous system ,FUNCTIONAL magnetic resonance imaging ,HEART beat ,RESPIRATION ,BRAIN imaging - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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17. Assessment of spontaneous cardiovascular oscillations in Parkinson's disease.
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Valenza, Gaetano, Orsolini, Stefano, Diciotti, Stefano, Citi, Luca, Scilingo, Enzo Pasquale, Guerrisi, Maria, Danti, Sabrina, Lucetti, Claudio, Tessa, Carlo, Barbieri, Riccardo, and Toschi, Nicola
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CARDIOVASCULAR diseases ,EVALUATION ,DIGESTIVE system diseases ,AUTONOMIC nervous system diseases - Abstract
Parkinson's disease (PD) has been reported to involve postganglionic sympathetic failure and a wide spectrum of autonomic dysfunctions including cardiovascular, sexual, bladder, gastrointestinal and sudo-motor abnormalities. While these symptoms may have a significant impact on daily activities, as well as quality of life, the evaluation of autonomic nervous system (ANS) dysfunctions relies on a large and expensive battery of autonomic tests only accessible in highly specialized laboratories. In this paper we aim to devise a comprehensive computational assessment of disease-related heartbeat dynamics based on instantaneous, time-varying estimates of spontaneous (resting state) cardiovascular oscillations in PD. To this end, we combine standard ANS-related heart rate variability (HRV) metrics with measures of instantaneous complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra). Such measures are computed over 600-s recordings acquired at rest in 29 healthy subjects and 30 PD patients. The only significant group-wise differences were found in the variability of the dominant Lyapunov exponent. Also, the best PD vs. healthy controls classification performance (balanced accuracy: 73.47%) was achieved only when retaining the time-varying, non-stationary structure of the dynamical features, whereas classification performance dropped significantly (balanced accuracy: 61.91%) when excluding variability-related features. Additionally, both linear and nonlinear model features correlated with both clinical and neuropsychological assessments of the considered patient population. Our results demonstrate the added value and potential of instantaneous measures of heartbeat dynamics and its variability in characterizing PD-related disabilities in motor and cognitive domains. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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18. Nonlinear digital signal processing in mental health: characterization of major depression using instantaneous entropy measures of heartbeat dynamics.
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Valenza, Gaetano, Garcia, Ronald G., Citi, Luca, Scilingo, Enzo P., Tomaz, Carlos A., and Barbieri, Riccardo
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DIGITAL signal processing ,MENTAL depression risk factors ,HEART beat ,MENTAL health services ,DEPRESSED persons - Abstract
Nonlinear digital signal processing methods that address system complexity have provided useful computational tools for helping in the diagnosis and treatment of a wide range of pathologies. More specifically, nonlinear measures have been successful in characterizing patients with mental disorders such as Major Depression (MD). In this study, we propose the use of instantaneous measures of entropy, namely the inhomogeneous point-process approximate entropy (ipApEn) and the inhomogeneous point-process sample entropy (ipSampEn), to describe a novel characterization of MD patients undergoing affective elicitation. Because these measures are built within a nonlinear point-process model, they allow for the assessment of complexity in cardiovascular dynamics at each moment in time. Heartbeat dynamics were characterized from 48 healthy controls and 48 patients with MD while emotionally elicited through either neutral or arousing audiovisual stimuli. Experimental results coming from the arousing tasks show that ipApEn measures are able to instantaneously track heartbeat complexity as well as discern between healthy subjects and MD patients. Conversely, standard heart rate variability (HRV) analysis performed in both time and frequency domains did not show any statistical significance. We conclude that measures of entropy based on nonlinear point-process models might contribute to devising useful computational tools for care in mental health. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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19. Characterization of Depressive States in Bipolar Patients Using Wearable Textile Technology and Instantaneous Heart Rate Variability Assessment.
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Valenza, Gaetano, Citi, Luca, Gentili, Claudio, Lanata, Antonio, Scilingo, Enzo Pasquale, and Barbieri, Riccardo
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PEOPLE with bipolar disorder ,WEARABLE technology ,HEART rate monitoring research ,ELECTROCARDIOGRAPHY ,AUTOREGRESSION (Statistics) - Abstract
The analysis of cognitive and autonomic responses to emotionally relevant stimuli could provide a viable solution for the automatic recognition of different mood states, both in normal and pathological conditions. In this study, we present a methodological application describing a novel system based on wearable textile technology and instantaneous nonlinear heart rate variability assessment, able to characterize the autonomic status of bipolar patients by considering only electrocardiogram recordings. As a proof of this concept, our study presents results obtained from eight bipolar patients during their normal daily activities and being elicited according to a specific emotional protocol through the presentation of emotionally relevant pictures. Linear and nonlinear features were computed using a novel point-process-based nonlinear autoregressive integrative model and compared with traditional algorithmic methods. The estimated indices were used as the input of a multilayer perceptron to discriminate the depressive from the euthymic status. Results show that our system achieves much higher accuracy than the traditional techniques. Moreover, the inclusion of instantaneous higher order spectra features significantly improves the accuracy in successfully recognizing depression from euthymia. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
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20. A point process local likelihood algorithm for robust and automated heart beat detection and correction.
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Citi, Luca, Brown, Emery N, and Barbieri, Riccardo
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Robust and automated classification and correction of ECG-derived heart beats are a necessary prerequisite for an accurate real-time estimation of measures of heart rate variability and cardiovascular control. In particular, the low quality of the signal, as well as the presence of recurring arrhythmic events, may significantly affect estimation accuracy. We here present a novel point process based method for a real time R-R interval error detection and correction. Results of detection analysis over data from the benchmark MIT-BIH arrhythmia database demonstrate that the proposed algorithm achieves 99.97% accuracy (98.23% sensitivity, 99.98% specificity and 95.69% positive predictive value), outperforming state-of-the-art algorithms. Further results on simulated data demonstrate the efficacy of the detection and correction method. [ABSTRACT FROM PUBLISHER]
- Published
- 2011
21. Point process Respiratory Sinus Arrhythmia analysis during deep tissue pain stimulation.
- Author
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Kodituwakku, Sandun, Kim, Jieun, Napadow, Vitaly, Loggia, Marco L, and Barbieri, Riccardo
- Abstract
We present an analysis of autonomic nervous system responses to deep tissue pain by using an instantaneous point process assessment of Heart Rate Variability (HRV) and Respiratory Sinus Arrhythmia (RSA). Ten subjects received pressure stimuli at 8 individually calibrated intensities (7 painful) over three separate runs. An inverse Gaussian point process framework modeled the R-R interval (RR) by defining a bivariate regression incorporating both past RRs and respiration values observed at the beats. Instantaneous indices of sympatho-vagal balance and RSA were estimated combining a maximum-likelihood algorithm with time-frequency analysis. The model was validated by Kolmogorov-Smirnov goodness-of-fit and independence tests. Results show that, in comparison to the resting period, all three pain runs elicited a significant decrease in RSA by over 21% (p=0.0547, 0.0234, 0.0547) indicating a reduced parasympathetic tone during pain, with RSA estimates negatively correlated with the calibrated stimulus intensity levels (slope = −0.4123, p=0.0633). [ABSTRACT FROM PUBLISHER]
- Published
- 2011
22. Point-Process Nonlinear Models With Laguerre and Volterra Expansions: Instantaneous Assessment of Heartbeat Dynamics.
- Author
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Valenza, Gaetano, Citi, Luca, Scilingo, Enzo Pasquale, and Barbieri, Riccardo
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HEART beat ,GAUSSIAN processes ,LAGUERRE polynomials ,NONLINEAR statistical models ,VOLTERRA series ,SIMULATION methods & models ,MATHEMATICAL models - Abstract
In the last decades, mathematical modeling and signal processing techniques have played an important role in the study of cardiovascular control physiology and heartbeat nonlinear dynamics. In particular, nonlinear models have been devised for the assessment of the cardiovascular system by accounting for short-memory second-order nonlinearities. In this paper, we introduce a novel inverse Gaussian point process model with Laguerre expansion of the nonlinear Volterra kernels. Within the model, the second-order nonlinearities also account for the long-term information given by the past events of the nonstationary non-Gaussian time series. In addition, the mathematical link to an equivalent cubic input-output Wiener-Volterra model allows for a novel instantaneous estimation of the dynamic spectrum, bispectrum and trispectrum of the considered inter-event intervals. The proposed framework is tested with synthetic simulations and two experimental heartbeat interval datasets. Applications on further heterogeneous datasets such as milling inserts, neural spikes, gait from short walks, and geyser geologic events are also reported. Results show that our model improves on previously developed models and, at the same time, it is able to provide a novel instantaneous characterization and tracking of the inherent nonlinearity of heartbeat dynamics. [ABSTRACT FROM PUBLISHER]
- Published
- 2013
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23. A Real-Time Automated Point-Process Method for the Detection and Correction of Erroneous and Ectopic Heartbeats.
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Citi, Luca, Brown, Emery N., and Barbieri, Riccardo
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POINT processes ,HEART rate monitoring ,ARRHYTHMIA diagnosis ,GAUSSIAN distribution ,ALGORITHMS - Abstract
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. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
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24. Application of dynamic point process models to cardiovascular control
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Barbieri, Riccardo and Brown, Emery N.
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STOCHASTIC processes , *POINT processes , *CARDIAC contraction , *MATHEMATICAL models - Abstract
Abstract: The development of statistical models that accurately describe the stochastic structure of biological signals is a fast growing area in quantitative research. In developing a novel statistical paradigm based on Bayes’ theorem applied to point processes, we are focusing our recent research on characterizing the physiological mechanisms involved in cardiovascular control. Results from a tilt table study point at our statistical framework as a valid model for the heart beat, as generated from complex mechanisms underlying cardiovascular control. The point process analysis provides new quantitative indices that could have important implications for research studies of cardiovascular and autonomic regulation and for monitoring of heart rate and heart rate variability measures in clinical settings. [Copyright &y& Elsevier]
- Published
- 2008
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25. Enhanced Vagal Withdrawal During Mild Orthostatic Stress in Adolescents with Chronic Fatigue.
- Author
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Wyller, Vegard Bruun, Barbieri, Riccardo, Thaulow, Erik, and Saul, J. Philip
- Abstract
Background: Hemodynamic abnormalities have been documented in the chronic fatigue syndrome (CFS), indicating functional disturbances of the autonomic nervous system responsible for cardiovascular regulation. The aim of this study was to investigate autonomic heart rate control during mild orthostatic stress in adolescents with CFS. Methods: A total of 14 CFS patients and 56 healthy controls having equal distribution of age and gender underwent lower body negative pressure (LBNP) of ─20 mmHg. The RR interval (RRI) was recorded continuously, and spectral power densities were computed in the low-frequency (LF) band (0.04–0.15 Hz) and the high-frequency (HF) band (0.15–0.50 Hz) from segments of 120-second length, using an autoregressive algorithm. In addition, the time-domain indices SDNN, pNN50, and r-MSSD were computed. Results: At rest, CFS had lower RRI than controls (P < 0.05), but indices of variability were similar in the two groups. During LBNP, compared to controls, CFS patients had lower normalized and absolute HF power and r-MSSD (P < 0.05), and higher RRI (P < 0.001), normalized LF power and LF/HF (P < 0.05). Conclusions: During mild orthostatic stress, adolescents with CFS appear to have enhanced vagal withdrawal, leading to a sympathetic predominance of heart rate control compared to controls. Possible underlying mechanisms include hypovolemia and abnormalities of reflex mechanisms. [ABSTRACT FROM AUTHOR]
- Published
- 2008
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26. Analysis of Heartbeat Dynamics by Point Process Adaptive Filtering.
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Barbieri, Riccardo and Brown, Emery N.
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- *
HEART beat , *HEART failure , *CONGESTIVE heart failure , *HEART diseases , *MEDICAL research , *ALGORITHMS - Abstract
Heartbeats are a point process yet, most of the current analysis methods do not model this important characteristic of these data. We describe human heartbeat time series as a history dependent inverse Gaussian model. We present a point process adaptive filter algorithm to estimate the model's time-varying parameters, and use it to compute new measures of heart rate variability. We apply our algorithm to analyze simulated heartbeat data and actual heartbeat data from a tilt table experiment and from healthy subjects and subjects with congestive heart failure during sleep. Our results suggest a new approach for characterizing heartbeat dynamics. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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27. Static and Dynamic Autonomic Response with Increasing Nausea Perception.
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Lacount, Lauren T., Barbieri, Riccardo, Park, Kyungmo, Kim, Jieun, Brown, Emery N., Kuo, Braden, and Napadow, Vitaly
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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 sured (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 cardiôvigal modulation. Results: HR increased from baseline for all in- creasing nausea transitions, especially transition to strong nausea (15.0 ± 11.4 ppm), but decreased (-6.6 ± 4.6 bpm) once the visual stimulus ceased. SCR also increased for all increasing nausea transitions, espe- cially transition to strong nausea (1.76 ± 1.68 p.S), but continued to increase (0.52 ± 0.65 S) 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 sym- pathovagal 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 hcreasing nausea perception is associated with both increased sym- pathetic 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. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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28. A Unified Point Process Probabilistic Framework to Assess Heartbeat Dynamics and Autonomic Cardiovascular Control
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Chen, Zhe, Purdon, Patrick Lee, Brown, Emery Neal, and Barbieri, Riccardo
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autonomic cardiovascular control ,heart rate variability ,baroreflex sensitivity ,respiratory sinus arrhythmia ,point process ,Wiener-Volterra expansion ,general anesthesia - Abstract
In recent years, time-varying inhomogeneous point process models have been introduced for assessment of instantaneous heartbeat dynamics as well as 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 (baroreflex) sensitivity (BRS), are derived within a parametric framework and instantaneously updated with adaptive and local maximum likelihood estimation algorithms. Inclusion of second-order non-linearities, with subsequent bispectral quantification in the frequency domain, further allows for definition of instantaneous metrics of non-linearity. We here present a comprehensive review of the devised methods as applied to experimental recordings from healthy subjects during propofol anesthesia. Collective results reveal interesting dynamic trends across the different pharmacological interventions operated within each anesthesia session, 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, non-invasive assessment in clinical practice. We also discuss the limitations and other alternative modeling strategies of our point process approach.
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- 2012
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29. Functional assessment of bidirectional cortical and peripheral neural control on heartbeat dynamics: A brain-heart study on thermal stress.
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Candia-Rivera, Diego, Catrambone, Vincenzo, Barbieri, Riccardo, and Valenza, Gaetano
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- *
THERMAL stresses , *FUNCTIONAL assessment , *DYNAMICAL systems , *HEART beat - Abstract
• We propose a new framework to assess neural dynamics involved in heartbeat control. • The modeling is based on coupled synthetic data generators of EEG and RR series. • Cardiac sympathovagal activity is modelled through Laguerre expansions of RR series. • Time-varying directional brain-heart interplay is quantified under thermal stress. The study of functional Brain-Heart Interplay (BHI) from non-invasive recordings has gained much interest in recent years. Previous endeavors aimed at understanding how the two dynamical systems exchange information, providing novel holistic biomarkers and important insights on essential cognitive aspects and neural system functioning. However, the interplay between cardiac sympathovagal and cortical oscillations still has much room for further investigation. In this study, we introduce a new computational framework for a functional BHI assessment, namely the Sympatho-Vagal Synthetic Data Generation Model, combining cortical (electroencephalography, EEG) and peripheral (cardiac sympathovagal) neural dynamics. The causal, bidirectional neural control on heartbeat dynamics was quantified on data gathered from 26 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay sustained by EEG oscillations in the delta and gamma bands, primarily originating from sympathetic activity, whereas brain-to-heart interplay originates over central brain regions through sympathovagal control. The proposed methodology provides a viable computational tool for the functional assessment of the causal interplay between cortical and cardiac neural control. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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30. Applications of Complexity Analysis in Clinical Heart Failure
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Liu, Chengyu, Murray, Alan, Barbieri, Riccardo, editor, Scilingo, Enzo Pasquale, editor, and Valenza, Gaetano, editor
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- 2017
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31. Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability.
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Nagaraj, Sunil B., Biswal, Siddharth, Boyle, Emily J., Zhou, David W., McClain, Lauren M., Brandon Westover, M., Bajwa, Ednan K., Quraishi, Sadeq A., Akeju, Oluwaseun, Purdon, Patrick L., Barbieri, Riccardo, and Westover, M Brandon
- Subjects
- *
HEART beat measurement , *INTENSIVE care patients , *TERMINAL sedation , *HEART rate monitoring , *SUPPORT vector machines , *MACHINE learning , *ALGORITHMS , *ANESTHESIA , *ARTIFICIAL respiration , *COMPARATIVE studies , *ELECTROCARDIOGRAPHY , *HEART beat , *INTENSIVE care units , *RESEARCH methodology , *MEDICAL cooperation , *RESEARCH , *RESEARCH funding , *PILOT projects , *EVALUATION research - Abstract
Objective: To develop a personalizable algorithm to discriminate between sedation levels in ICU patients based on heart rate variability.Design: Multicenter, pilot study.Setting: Several ICUs at Massachusetts General Hospital, Boston, MA.Patients: We gathered 21,912 hours of routine electrocardiogram recordings from a heterogenous group of 70 adult ICU patients. All patients included in the study were mechanically ventilated and were receiving sedatives.Measurements and Main Results: As "ground truth" for developing our method, we used Richmond Agitation Sedation Scale scores grouped into four levels denoted "comatose" (-5), "deeply sedated" (-4 to -3), "lightly sedated" (-2 to 0), and "agitated" (+1 to +4). We trained a support vector machine learning algorithm to calculate the probability of each sedation level from heart rate variability measures derived from the electrocardiogram. To estimate algorithm performance, we calculated leave-one-subject out cross-validated accuracy. The patient-independent version of the proposed system discriminated between the four sedation levels with an overall accuracy of 59%. Upon personalizing the system supplementing the training data with patient-specific calibration data, consisting of an individual's labeled heart rate variability epochs from the preceding 24 hours, accuracy improved to 67%. The personalized system discriminated between light- and deep-sedation states with an average accuracy of 75%.Conclusions: With further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and under sedation. [ABSTRACT FROM AUTHOR]- Published
- 2017
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32. Motion sickness increases functional connectivity between visual motion and nausea-associated brain regions.
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Toschi, Nicola, Kim, Jieun, Sclocco, Roberta, Duggento, Andrea, Barbieri, Riccardo, Kuo, Braden, and Napadow, Vitaly
- Subjects
- *
MORNING sickness , *BRAIN anatomy , *CINGULATE cortex , *BRAIN physiology , *STIMULUS & response (Psychology) , *BRAIN stimulation , *ELECTROCARDIOGRAPHY , *PSYCHOLOGY - Abstract
The brain networks supporting nausea not yet understood. We previously found that while visual stimulation activated primary (V1) and extrastriate visual cortices (MT +/V5, coding for visual motion), increasing nausea was associated with increasing sustained activation in several brain areas, with significant co-activation for anterior insula (aIns) and mid-cingulate (MCC) cortices. Here, we hypothesized that motion sickness also alters functional connectivity between visual motion and previously identified nausea-processing brain regions. Subjects prone to motion sickness and controls completed a motion sickness provocation task during fMRI/ECG acquisition. We studied changes in connectivity between visual processing areas activated by the stimulus (MT +/V5, V1), right aIns and MCC when comparing rest (BASELINE) to peak nausea state (NAUSEA). Compared to BASELINE, NAUSEA reduced connectivity between right and left V1 and increased connectivity between right MT +/V5 and aIns and between left MT +/V5 and MCC. Additionally, the change in MT +/V5 to insula connectivity was significantly associated with a change in sympathovagal balance, assessed by heart rate variability analysis. No state-related connectivity changes were noted for the control group. Increased connectivity between a visual motion processing region and nausea/salience brain regions may reflect increased transfer of visual/vestibular mismatch information to brain regions supporting nausea perception and autonomic processing. We conclude that vection-induced nausea increases connectivity between nausea-processing regions and those activated by the nauseogenic stimulus. This enhanced low-frequency coupling may support continual, slowly evolving nausea perception and shifts toward sympathetic dominance. Disengaging this coupling may be a target for biobehavioral interventions aimed at reducing motion sickness severity. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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33. Relationship between cardiac vagal activity and mood congruent memory bias in major depression.
- Author
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Garcia, Ronald G., Valenza, Gaetano, Tomaz, Carlos, Barbieri, Riccardo, and Tomaz, Carlos A
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- *
MOOD (Psychology) , *MEMORY bias , *MENTAL depression , *AVERSIVE stimuli , *HEART beat , *AFFECT (Psychology) , *MEMORY , *RESEARCH funding , *VAGUS nerve , *CASE-control method , *ACOUSTIC stimulation - Abstract
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. [ABSTRACT FROM AUTHOR]- Published
- 2016
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34. Spectral analysis of heart rate variability in human fear learning
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Stefano Orsolini, Simone Battaglia, Stefano Diciotti, Giuseppe di Pellegrino, Sara Borgomaneri, Riccardo Barbieri, Battaglia, Simone, Orsolini, Stefano, Borgomaneri, Sara, Barbieri, Riccardo, Diciotti, Stefano, and Di Pellegrino, Giuseppe
- Subjects
medicine.medical_specialty ,Neurology ,medicine ,Heart rate variability ,Spectral analysis ,Neurology (clinical) ,Fear learning ,Fear Conditioning, Heart Rate Variability ,Audiology ,Psychology - Published
- 2021
35. Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis
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Indic, Premananda, Bloch-Salisbury, Elisabeth, Bednarek, Frank, Brown, Emery N., Paydarfar, David, and Barbieri, Riccardo
- Subjects
- *
CARDIOPULMONARY system , *PREMATURE infants , *AUTOREGRESSION (Statistics) , *DATA analysis , *INFANT development , *PHYSIOLOGY , *CARDIOVASCULAR system , *HEART beat - Abstract
Abstract: Background: Cardio-respiratory interactions are weak at the earliest stages of human development, suggesting that assessment of their presence and integrity may be an important indicator of development in infants. Despite the valuable research devoted to infant development, there is still a need for specifically targeted standards and methods to assess cardiopulmonary functions in the early stages of life. We present a new methodological framework for the analysis of cardiovascular variables in preterm infants. Our approach is based on a set of mathematical tools that have been successful in quantifying important cardiovascular control mechanisms in adult humans, here specifically adapted to reflect the physiology of the developing cardiovascular system. Methods: We applied our methodology in a study of cardio-respiratory responses for 11 preterm infants. We quantified cardio-respiratory interactions using specifically tailored multivariate autoregressive analysis and calculated the coherence as well as gain using causal approaches. The significance of the interactions in each subject was determined by surrogate data analysis. The method was tested in control conditions as well as in two different experimental conditions; with and without use of mild mechanosensory intervention. Results: Our multivariate analysis revealed a significantly higher coherence, as confirmed by surrogate data analysis, in the frequency range associated with eupneic breathing compared to the other ranges. Conclusions: Our analysis validates the models behind our new approaches, and our results confirm the presence of cardio-respiratory coupling in early stages of development, particularly during periods of mild mechanosensory intervention, thus encouraging further application of our approach. [Copyright &y& Elsevier]
- Published
- 2011
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36. Instantaneous Transfer Entropy for the Study of Cardiovascular and Cardio-Respiratory Nonstationary Dynamics
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Michele Orini, Riccardo Barbieri, Luca Citi, Gaetano Valenza, Luca Faes, Valenza, Gaetano, Faes, Luca, Citi, Luca, Orini, Michele, and Barbieri, Riccardo
- Subjects
Adult ,Male ,Information transfer ,History ,Heartbeat ,Databases, Factual ,Physiology ,Entropy ,0206 medical engineering ,Complex system ,Biomedical Engineering ,Heart Rate Variability ,Probability density function ,02 engineering and technology ,01 natural sciences ,Point process ,Statistics, Nonparametric ,Electrocardiography ,Young Adult ,0103 physical sciences ,Entropy (information theory) ,Humans ,Statistical physics ,Transfer Entropy ,010306 general physics ,Biomedical measurement ,Mathematics ,business.industry ,Hemodynamics ,Models, Cardiovascular ,Heart beat ,Signal Processing, Computer-Assisted ,Complexity ,Baroreflex ,020601 biomedical engineering ,Kolmogorov-Smirnov Distance ,Respiratory Sinus Arrhythmia ,Heart rate variability ,Point Process ,Discrete time and continuous time ,Point Proce ,Settore ING-INF/06 - Bioingegneria Elettronica E Informatica ,Transfer entropy ,Female ,Artificial intelligence ,business - Abstract
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).
- Published
- 2018
37. Assessment of Instantaneous Heartbeat Dynamics in amnestic Mild Cognitive Impairment
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Stefano Diciotti, Riccardo Barbieri, Stefano Orsolini, Luca Citi, Gaetano Valenza, Carlo Tessa, Maria Guerrisi, Nicola Toschi, Toschi, N., Valenza, G., Citi, L., Guerrisi, M., Orsolini, S., Tessa, C., Diciotti, S., and Barbieri, Riccardo
- Subjects
Mild Cognitive Impairment ,medicine.medical_specialty ,Support Vector Machine ,Heartbeat ,business.industry ,Settore FIS/07 ,Autonomic dysfunction ,Biomedical Engineering ,Heart Rate Variability ,Dysautonomia ,Bioengineering ,Cognition ,Autonomic Nervous System ,Audiology ,medicine.disease ,Autonomic nervous system ,Medicine ,Heart rate variability ,Dementia ,Effects of sleep deprivation on cognitive performance ,medicine.symptom ,Cognitive impairment ,business - Abstract
In this study, we employ a time-varying, probabilistic model of linear and nonlinear heartbeat dynamics to investigate the possibility of detecting subtle autonomic alterations in subjects suffering from amnestic mild cognitive impairment (aMCI) by exploiting heartbeat information alone. aMCI is a frequent form of cognitive dysfunction which increases the risk of culminating in Alzheimer's disease (AD)-related dementia, and previous studies have demonstrated that AD is accompanied by alterations in autonomic function, which in turn have been linked to cognitive performance in non-demented subjects. We compare 13 MCI patients without ouvert dysautonomia to 13 age- and gender-matched healthy controls by feeding an autonomic nervous system-related linear and nonlinear feature set into a classification framework. Our results show a satisfactory classification performance (73% balanced accuracy), which dropped to 65% when excluding cardiovascular nonlinear/complex features. This outcome confirms the presence of subtle autonomic dysfunction in aMCI (a possible prodromal condition to AD), which can only be detected through to the use of our comprehensive modeling strategy which comprises time-varying, nonlinear/complex estimates of heartbeat dynamics.
- Published
- 2017
38. Assessment of spontaneous cardiovascular oscillations in Parkinson's disease
- Author
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Maria Guerrisi, Nicola Toschi, Carlo Tessa, Riccardo Barbieri, Stefano Orsolini, Stefano Diciotti, Claudio Lucetti, Sabrina Danti, Luca Citi, Enzo Pasquale Scilingo, Gaetano Valenza, Valenza, Gaetano, Orsolini, Stefano, Diciotti, Stefano, Citi, Luca, Scilingo, Enzo Pasquale, Guerrisi, Maria, Danti, Sabrina, Lucetti, Claudio, Tessa, Carlo, Barbieri, Riccardo, and Toschi, Nicola
- Subjects
0301 basic medicine ,medicine.medical_specialty ,Support vector machine ,Parkinson's disease ,Heartbeat ,Autonomic dysfunction ,Laguerre expansion ,Bispectrum Lyapunov exponents ,Health Informatics ,Lyapunov exponent ,Point proce ,03 medical and health sciences ,symbols.namesake ,0302 clinical medicine ,Physical medicine and rehabilitation ,medicine ,Autonomic nervous system ,Heart rate variability ,Point process ,Health Informatic ,Bispectrum ,Resting state fMRI ,business.industry ,Settore FIS/07 ,Lyapunov exponents ,Neuropsychology ,Signal Processing ,Cognition ,medicine.disease ,3. Good health ,030104 developmental biology ,symbols ,Artificial intelligence ,business ,030217 neurology & neurosurgery - Abstract
Parkinson's disease (PD) has been reported to involve postganglionic sympathetic failure and a wide spectrum of autonomic dysfunctions including cardiovascular, sexual, bladder, gastrointestinal and sudo-motor abnormalities. While these symptoms may have a significant impact on daily activities, as well as quality of life, the evaluation of autonomic nervous system (ANS) dysfunctions relies on a large and expensive battery of autonomic tests only accessible in highly specialized laboratories. In this paper we aim to devise a comprehensive computational assessment of disease-related heartbeat dynamics based on instantaneous, time-varying estimates of spontaneous (resting state) cardiovascular oscillations in PD. To this end, we combine standard ANS-related heart rate variability (HRV) metrics with measures of instantaneous complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra). Such measures are computed over 600-s recordings acquired at rest in 29 healthy subjects and 30 PD patients. The only significant group-wise differences were found in the variability of the dominant Lyapunov exponent. Also, the best PD vs. healthy controls classification performance (balanced accuracy: 73.47%) was achieved only when retaining the time-varying, non-stationary structure of the dynamical features, whereas classification performance dropped significantly (balanced accuracy: 61.91%) when excluding variability-related features. Additionally, both linear and nonlinear model features correlated with both clinical and neuropsychological assessments of the considered patient population. Our results demonstrate the added value and potential of instantaneous measures of heartbeat dynamics and its variability in characterizing PD-related disabilities in motor and cognitive domains.
- Published
- 2016
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39. Instantaneous monitoring of heart beat dynamics during anesthesia and sedation
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Katharine E. Hartnack, Oluwaseun Akeju, Luca Citi, Aaron L. Sampson, Gaetano Valenza, Emery N. Brown, Kara J. Pavone, Riccardo Barbieri, Patrick L. Purdon, Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, Purdon, Patrick Lee, Brown, Emery N., and Barbieri, Riccardo
- Subjects
Heartbeat ,Sedation ,medicine ,Heart rate variability ,Autonomic nervous system ,Respiratory sinus arrhythmia ,Anesthesia ,Dexmedetomidine ,Vagal tone ,Propofol ,business.industry ,Autonomic nervous system, Electrocardiogram, Electroencephalogram, Heart rate variability, Respiratory sinus arrhythmia, Anesthesia, Sedation, Propofol, Dexmedetomidine, Instantaneous, point process monitoring ,point process monitoring ,Electrocardiogram ,Electroencephalogram ,Instantaneous ,Anesthetic ,medicine.symptom ,business ,medicine.drug - Abstract
Anesthesia-induced altered arousal depends on drugs having their effect in specific brain regions. These effects are also reflected in autonomic nervous system (ANS) outflow dynamics. To this extent, instantaneous monitoring of ANS outflow, based on neurophysiological and computational modeling, may provide a more accurate assessment of the action of anesthetic agents on the cardiovascular system. This will aid anesthesia care providers in maintaining homeostatic equilibrium and help to minimize drug administration while maintaining antinociceptive effects. In previous studies, we established a point process paradigm for analyzing heartbeat dynamics and have successfully applied these methods to a wide range of cardiovascular data and protocols. We recently devised a novel instantaneous nonlinear assessment of ANS outflow, also suitable and effective for real-time monitoring of the fast hemodynamic and autonomic effects during induction and emergence from anesthesia. Our goal is to demonstrate that our framework is suitable for instantaneous monitoring of the ANS response during administration of a broad range of anesthetic drugs. Specifically, we compare the hemodynamic and autonomic effects in study participants undergoing propofol (PROP) and dexmedetomidine (DMED) administration. Our methods provide an instantaneous characterization of autonomic state at different stages of sedation and anesthesia by tracking autonomic dynamics at very high time-resolution. Our results suggest that refined methods for analyzing linear and nonlinear heartbeat dynamics during administration of specific anesthetic drugs are able to overcome nonstationary limitations as well as reducing inter-subject variability, thus providing a potential real-time monitoring approach for patients receiving anesthesia.
- Published
- 2014
40. A Real-Time Automated Point-Process Method for the Detection and Correction of Erroneous and Ectopic Heartbeats
- Author
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Riccardo Barbieri, Luca Citi, Emery N. Brown, Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, Citi, Luca, Brown, Emery N., and Barbieri, Riccardo
- Subjects
Cardiac Complexes, Premature ,Databases, Factual ,Heartbeat ,medicine.diagnostic_test ,Computer science ,Speech recognition ,Biomedical Engineering ,Signal Processing, Computer-Assisted ,Cardiac dysrhythmia ,Article ,Beat detection ,Electrocardiography ,Heart Rate ,Heart rate ,Heart beat ,medicine ,Humans ,Heart rate variability ,Algorithms - Abstract
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., National Institutes of Health (U.S.) (Grant R01-HL084502), National Institutes of Health (U.S.) (Grant DP1-OD003646)
- Published
- 2012
41. Point process time-frequency analysis of dynamic respiratory patterns during meditation practice
- Author
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Riccardo Barbieri, Sara W. Lazar, Zhe Chen, Emery N. Brown, Premananda Indic, Sandun Kodituwakku, Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Chen, Zhe, Brown, Emery N., and Barbieri, Riccardo
- Subjects
Adult ,Male ,Biomedical Engineering ,Point processes ,Respiratory physiology ,Cardiovascular ,Autonomic Nervous System ,Point process ,Article ,Heart rate variability ,Meditation ,Respiratory sinus arrhythmia ,Time-frequency analysis ,Algorithms ,Arrhythmia, Sinus ,Female ,Humans ,Middle Aged ,Respiratory Mechanics ,Signal Processing, Computer-Assisted ,Models, Cardiovascular ,Computer Science Applications1707 Computer Vision and Pattern Recognition ,Medicine (all) ,Computer-Assisted ,Models ,Statistics ,Vagal tone ,Sinus ,Simulation ,Mathematics ,Cardiorespiratory fitness ,Pulse (music) ,Computer Science Applications ,Time–frequency analysis ,Frequency domain ,Signal Processing ,Arrhythmia - Abstract
Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heart beats. We propose a robust algorithm for quantifying instantaneous RSA as applied to heart beat intervals and respiratory recordings under dynamic breathing patterns. The blood volume pressure-derived heart beat series (pulse intervals, PIs) are modeled as an inverse Gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PIs and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated via a frequency domain transfer function evaluated at instantaneous respiratory frequency where high coherence between respiration and PIs is observed. The model is statistically validated using Kolmogorov–Smirnov goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. The presented analysis confirms the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states, reporting statistically significant increase in RSA gain as measured by our paradigm., National Institutes of Health (U.S.) (Grant R01-HL084502), National Institutes of Health (U.S.) (Grant R01-DA015644), National Institutes of Health (U.S.) (Grant DP1-OD003646), National Institutes of Health (U.S.) (Grant K01-AT00694-01)
- Published
- 2011
42. Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis
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Riccardo Barbieri, Emery N. Brown, Elisabeth Bloch-Salisbury, Frank Bednarek, David Paydarfar, Premananda Indic, Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, Brown, Emery N., and Barbieri, Riccardo
- Subjects
medicine.medical_specialty ,Pediatrics ,Respiratory rate ,Cardiography ,Surrogate data analysis ,Cardiovascular ,Cardiography, Impedance ,Article ,Surrogate data ,Electrocardiography ,Models ,Heart Rate ,Pregnancy ,Internal medicine ,Medicine ,Heart rate variability ,Humans ,Vagal tone ,Premature ,medicine.diagnostic_test ,business.industry ,Respiration ,Bivariate analysis ,Coherence ,Preterm infants ,Female ,Infant, Newborn ,Infant, Premature ,Models, Cardiovascular ,Pediatrics, Perinatology and Child Health ,Obstetrics and Gynecology ,Impedance ,Infant ,Perinatology and Child Health ,Newborn ,Impedance cardiography ,Periodic breathing ,Breathing ,Cardiology ,business - Abstract
Background: Cardio-respiratory interactions are weak at the earliest stages of human development, suggesting that assessment of their presence and integrity may be an important indicator of development in infants. Despite the valuable research devoted to infant development, there is still a need for specifically targeted standards and methods to assess cardiopulmonary functions in the early stages of life. We present a new methodological framework for the analysis of cardiovascular variables in preterm infants. Our approach is based on a set of mathematical tools that have been successful in quantifying important cardiovascular control mechanisms in adult humans, here specifically adapted to reflect the physiology of the developing cardiovascular system. Methods: We applied our methodology in a study of cardio-respiratory responses for 11 preterm infants. We quantified cardio-respiratory interactions using specifically tailored multivariate autoregressive analysis and calculated the coherence as well as gain using causal approaches. The significance of the interactions in each subject was determined by surrogate data analysis. The method was tested in control conditions as well as in two different experimental conditions; with and without use of mild mechanosensory intervention. Results: Our multivariate analysis revealed a significantly higher coherence, as confirmed by surrogate data analysis, in the frequency range associated with eupneic breathing compared to the other ranges. Conclusions: Our analysis validates the models behind our new approaches, and our results confirm the presence of cardio-respiratory coupling in early stages of development, particularly during periods of mild mechanosensory intervention, thus encouraging further application of our approach., Center for Integration of Medicine and Innovative Technology (U.S. Army Medical Research Acquisition Activity Cooperative Agreement W81XWH-07-2-0011), National Institutes of Health (U.S.) (Grant R01-HL084502), National Institutes of Health (U.S.) (Grant R01-DA015644), National Institutes of Health (U.S.) (Grant DP1-OD003646)
- Published
- 2011
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