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

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Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics

Background:The neural mechanisms of anesthetic vapors have not been studied in depth. However, modeling and experimental studies on the intravenous anesthetic propofol indicate that potentiation of γ-aminobutyric acid receptors leads to a state of thalamocortical synchrony, observed as coherent fron

/pdf/effects-of-sevoflurane-and-propofol-on-frontal-3tj6rebcfq.pdf

Effects of sevoflurane and propofol on frontal electroencephalogram power and coherence.

Background:The neural mechanisms of anesthetic vapors have not been studied in depth. However, modeling and experimental studies on the intravenous anesthetic propofol indicate that potentiation of &ggr;-aminobutyric acid receptors leads to a state of thalamocortical synchrony, observed as coherent frontal alpha oscillations, associated with unconsciousness. Sevoflurane, an ether derivative, also potentiates &ggr;-aminobutyric acid receptors. However, in humans, sevoflurane-induced coherent frontal alpha oscillations have not been well detailed. Methods:To study the electroencephalogram dynamics induced by sevoflurane, the authors identified age- and sex-matched patients in which sevoflurane (n = 30) or propofol (n = 30) was used as the sole agent for maintenance of general anesthesia during routine surgery. The authors compared the electroencephalogram signatures of sevoflurane with that of propofol using time-varying spectral and coherence methods. Results:Sevoflurane general anesthesia is characterized by alpha oscillations with maximum power and coherence at approximately 10 Hz, (mean ± SD; peak power, 4.3 ± 3.5 dB; peak coherence, 0.73 ± 0.1). These alpha oscillations are similar to those observed during propofol general anesthesia, which also has maximum power and coherence at approximately 10 Hz (peak power, 2.1 ± 4.3 dB; peak coherence, 0.71 ± 0.1). However, sevoflurane also exhibited a distinct theta coherence signature (peak frequency, 4.9 ± 0.6 Hz; peak coherence, 0.58 ± 0.1). Slow oscillations were observed in both cases, with no significant difference in power or coherence. Conclusions:The study results indicate that sevoflurane, like propofol, induces coherent frontal alpha oscillations and slow oscillations in humans to sustain the anesthesia-induced unconscious state. These results suggest a shared molecular and systems-level mechanism for the unconscious state induced by these drugs.

Effects of Sevoflurane and Propofol on Frontal Electroencephalogram Power and Coherence

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.

Point-Process Nonlinear Models With Laguerre and Volterra Expansions: Instantaneous Assessment of Heartbeat Dynamics

Cardiac baroreflex is a fundamental component of the cardiovascular control. The continuous assessment of baroreflex sensitivity (BRS) from spontaneous heart period (HP) and systolic arterial pressure (SAP) variations during general anesthesia provides relevant information about cardiovascular regulation in physiological conditions. Unfortunately, several difficulties including unknown HP-SAP causal relations, negligible SAP changes, small BRS values, and confounding influences due to mechanical ventilation prevent BRS monitoring from HP and SAP variabilities during general anesthesia. We applied a model-based causal closed-loop approach aiming at BRS assessment during propofol anesthesia in 34 patients undergoing coronary artery bypass graft (CABG) surgery. We found the following: 1) traditional time and frequency domain approaches (i.e., baroreflex sequence, cross-correlation, spectral, and transfer function techniques) exhibited irremediable methodological limitations preventing the assessment of the B...

https://www.physiology.org/doi/pdf/10.1152/japplphysiol.00537.2013

Model-based causal closed-loop approach to the estimate of baroreflex sensitivity during propofol anesthesia in patients undergoing coronary artery bypass graft

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

/pdf/dynamic-assessment-of-baroreflex-control-of-heart-rate-1i0dbbwkab.pdf

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

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

/pdf/linear-and-nonlinear-quantification-of-respiratory-sinus-1e2hzlarfi.pdf

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

Evaluation of baroreflex control of heart rate (HR) has important implications in clinical practice of anesthesia and postoperative care. In this paper, we present a point process method to assess the dynamic baroreflex gain using a closed-loop model of the cardiovascular system. Specifically, the inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by a linear or bilinear bivariate regression on the previous R-R intervals and blood pressure (BP) measures. The instantaneous baroreflex gain is estimated in the feedback loop with a point process filter, while the RR→BP feedforward frequency response is estimated by a Kalman filter. In addition, the instantaneous cross-spectrum and cross-bispectrum (as well as their ratio) can also be estimated. All statistical indices provide a valuable quantitative assessment of the interaction between heartbeat dynamics and hemodynamics during general anesthesia.

/pdf/assessment-of-baroreflex-control-of-heart-rate-during-30cs85fugo.pdf

Grace Harrell

Papers

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

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

Assessment of baroreflex control of heart rate during general anesthesia using a point process method

Dynamic Assessment of Baroreflex Control of Heart Rate During Induction of Propofol Anesthesia Using a Point Process Method