Sparse Deconvolution of Electrodermal Activity via Continuous-Time System Identification

Amin, Md. Rafiul; Faghih, Rose T.

doi:10.1109/tbme.2019.2892352

Cited by 45 publications

(48 citation statements)

References 36 publications

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“…Similarly, another group proposed using the Hartley modulating function to assure convexity of the optimization formulation for estimating the number, timing, and amplitudes of underlying neural firing from EDA signals, and using Kaiser windows with different shape parameters to emphasize the significant spectral components [60]. This approach was meant to maintain a balance between noise filtering and enhancing the relevant information in the EDA data.…”

Section: Tonic/phasic Decomposition Of Edamentioning

confidence: 99%

Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review

Posada-Quintero

Chon

2020

Sensors

235

158

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The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research.

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Section: Tonic/phasic Decomposition Of Edamentioning

confidence: 99%

Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review

Posada-Quintero

Chon

2020

Sensors

235

158

View full text Add to dashboard Cite

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“…The M-step updates are likewise obtained for δ 0 and δ 1 by replacing r k with s k in Eq (23). The update for s 2 w can be obtained similarly by making the corresponding changes to γ 0 , γ 1 and r k in Eq (24).…”

Section: Plos Onementioning

confidence: 99%

“…The phasic component comprises of a series of individual skin conductance responses (SCRs). Each SCR occurs due to the expulsion of sweat in response to a burst of sudomotor neural activity [24,25] which in turn may occur due to arousing stimuli [26]. In a typical fear conditioning experiment, the CS+ cues increasingly begin to elicit SCRs (which are typically elicited by the US) causing a rise in the skin conductance signal [27].…”

Section: Introductionmentioning

confidence: 99%

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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“…A skin conductance signal comprises of a slow-varying tonic part and a fast-varying phasic part [39]. The phasic component consists of individual skin conductance responses (SCRs), each of which is typically modeled as being generated by a burst of neuroelectric stimuli to the sweat glands [40]. 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].…”

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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Sparse Deconvolution of Electrodermal Activity via Continuous-Time System Identification

Cited by 45 publications

References 36 publications

Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review

Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review

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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