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

Wickramasuriya, Dilranjan S.; Faghih, Rose T.

doi:10.1109/tbme.2019.2945579

Cited by 33 publications

(47 citation statements)

References 69 publications

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“…We assign m k = 1 or m k = 0 based on whether or not an SCR occurred at the k th time index using a threshold of 0.015 μS. The 0.015 μS threshold is selected similar to our previous work in [42] to provide a balance between detecting SCR peaks and avoiding the detection of noise as SCRs. The occurrence of SCRs follows a Bernoulli distribution with a density function p m k k ð1 À p k Þ 1À m k where p k is the probability that m k = 1.…”

Section: Skin Conductancementioning

confidence: 99%

“…For instance, in behavioral learning experiments involving correct/incorrect responses, p 0 is the probability that a subject gets an answer correct at the very outset prior to any learning occurring (i.e., the chance probability) [51,52]. In our prior work on Bayesian filtering using skin conductance data, taking p 0 as the average probability of an SCR occurring in the whole experiment provided good results [42,45,82,83].…”

Section: Plos Onementioning

confidence: 99%

“…In a recent work [42], we developed a state-space model and corresponding Bayesian filter for estimating sympathetic arousal from the three skin conductance features just referred to.…”

Section: Introductionmentioning

confidence: 99%

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

Wickramasuriya

Faghih

2020

PLoS ONE

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

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Section: Skin Conductancementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

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

Wickramasuriya

Faghih

2020

PLoS ONE

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“…The rate at which SCRs occur, their amplitudes and the tonic level are three of the most common skin conductance-related measures of autonomic arousal used in the literature [41]. Our previous work in [42], [43] and [44] presented some of the first methods to estimate sympathetic arousal from skin conductance explicitly making use of the point process state-space approach. In our first approach described in [42], [43], we related an unobserved arousal state to the occurrence of neural impulses underlying SCR events.…”

Section: Related Researchmentioning

confidence: 99%

“…The method was successfully able to track arousal in subjects across periods of relaxation, and cognitive and emotional stress. Our later method in [44] sought to incorporate features from both the phasic and tonic components into the estimation scheme as well. Here, estimation was based on two additional continuous-valued features-the raw tonic values and a set of phasic-derived values that were interpolations over the log-transformed SCR peak amplitudes [44].…”

Section: Related Researchmentioning

confidence: 99%

A Marked Point Process Filtering Approach for Tracking Sympathetic Arousal From Skin Conductance

Wickramasuriya

Faghih

2020

IEEE Access

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Human emotion represents a complex neural process within the brain. The ability to automatically recognize emotions from physiological signals has the potential to impact humanity in multiple ways through applications in human-machine interaction, remote health monitoring, smart living environments and entertainment. We present a marked point process-based Bayesian filtering approach to track sympathetic arousal from skin conductance features. The rate at which individual skin conductance responses (SCRs) occur and their respective amplitudes encode important information regarding an individual's psychological arousal level. We develop a state-space model relating a latent neuropsychological arousal state to the rate at which neural impulses underlying SCRs occur and the impulse amplitudes. We simultaneously estimate both arousal and the state-space model parameters within an expectation-maximization framework. We evaluate our method on both simulated and experimental data. Results on simulated data indicate the method's ability to accurately estimate an unobserved state from marked point process observations. The experimental data include studies involving mental stress artificially-induced in laboratory environments, real-world driver stress and Pavlovian fear conditioning. Results on experimental data outperform previous Bayesian filtering approaches in terms of a lower sensitivity to small impulses and avoiding overfitting. The filter is thus able to estimate emotional arousal from skin conductance features and is a promising approach for everyday emotion recognition applications.

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Introduction

Wickramasuriya,

Faghih

2023

Bayesian Filter Design for Computational Medicine

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The human body is an intricate network of multiple functioning sub-systems. Many unobserved processes quietly keep running within the body even while we remain largely unconscious of them. For decades, scientists have sought to understand how different physiological systems work and how they can be mathematically modeled. Mathematical models of biological systems provide key scientific insights and also help guide the development of technologies for treating disorders when proper functioning no longer occurs. One of the challenges encountered with physiological systems is that, in a number of instances, the quantities we are interested in are difficult to observe directly or remain completely inaccessible. This could be either because they are located deep within the body or simply because they are more abstract (e.g., emotion). Consider the heart, for instance. The left ventricle pumps out blood through the aorta to the rest of the body. Blood pressure inside the aorta (known as central aortic pressure) has been considered a useful predictor of the future risk of developing cardiovascular disease, perhaps even more useful than the conventional blood pressure measurements taken from the upper arm (McEniery et al. (Eur Heart J 35(26):1719–1725, 2014)). However, measuring blood pressure inside the aorta is difficult. Consequently, researchers have had to rely on developing mathematical models with which to estimate central aortic pressure using other peripheral measurements (e.g., Ghasemi et al. (J Dyn Syst Measur Control 139(6):061003, 2017)). The same could be said regarding the recovery of CRH (corticotropin-releasing hormone) secretion timings within the hypothalamus—a largely inaccessible structure deep within the brain—using cortisol measurements in the blood based on mathematical relationships (Faghih (System identification of cortisol secretion: Characterizing pulsatile dynamics, Ph.D. dissertation, Massachusetts Institute of Technology, 2014)). Emotions could also be placed in this same category. They are difficult to measure because of their inherently abstract nature. Emotions, however, do cause changes in heart rate, sweating, and blood pressure that can be measured and with which someone’s feelings can be estimated. What we have described so far, in a sense, captures the big picture underlying this book. We have physiological quantities that are difficult to observe directly, we have measurements that are easier to acquire, and we have the ability to build mathematical models to estimate those inaccessible quantities.

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

Cited by 33 publications

References 69 publications

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

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

A Marked Point Process Filtering Approach for Tracking Sympathetic Arousal From Skin Conductance

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