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

Posada-Quintero, Hugo F.; Chon, Ki H.

doi:10.3390/s20020479

Cited by 244 publications

(205 citation statements)

References 72 publications

(84 reference statements)

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“…Interestingly, the results of both studies show the importance of EDA. We conclude that in the context of seizure prediction and detection, a closer analysis of EDA signals can spur novel research ideas, even with less frequently used analytical approaches 22 .…”

Section: Discussionmentioning

confidence: 95%

Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures

Vieluf

Reinsberger

Atrache

et al. 2020

Sci Rep

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A better understanding of the early detection of seizures is highly desirable as identification of an impending seizure may afford improved treatments, such as antiepileptic drug chronotherapy, or timely warning to patients. While epileptic seizures are known to often manifest also with autonomic nervous system (ANS) changes, it is not clear whether ANS markers, if recorded from a wearable device, are also informative about an impending seizure with statistically significant sensitivity and specificity. Using statistical testing with seizure surrogate data and a unique dataset of continuously recorded multi-day wristband data including electrodermal activity (EDA), temperature (TEMP) and heart rate (HR) from 66 people with epilepsy (9.9 ± 5.8 years; 27 females; 161 seizures) we investigated differences between inter-and preictal periods in terms of mean, variance, and entropy of these signals. We found that signal mean and variance do not differentiate between inter-and preictal periods in a statistically meaningful way. EDA signal entropy was found to be increased prior to seizures in a small subset of patients. Findings may provide novel insights into the pathophysiology of epileptic seizures with respect to ANS function, and, while further validation and investigation of potential causes of the observed changes are needed, indicate that epilepsy-related state changes may be detectable using peripheral wearable devices. Detection of such changes with wearable devices may be more feasible for everyday monitoring than utilizing an electroencephalogram. The current inability to assess when a seizure is most likely to occur constitutes a major burden for people with epilepsy (PWE) 1. From a clinical perspective, this inability precludes the development of better treatments, such as antiepileptic drug chronotherapy adapted to personalized risk profiles, or timely, closed-loop intervention strategies to acutely avert impending seizures 2. Hence, a better understanding of the informative biomarkers underlying the transition to seizures is needed. Most research devoted to the study of seizure onset mechanisms and prior warning signals has traditionally focused on electroencephalogram (EEG). Continuous EEG, however, is impractical for monitoring over extended periods of time, especially when used in the ambulatory setting 3. Wearable devices might offer a promising alternative, as these afford easy-to-use, close monitoring of autonomic nervous system (ANS) function without being invasive or restraining to PWE 4. Alterations of ANS activity are known to occur frequently within multiple domains, such as electrodermal, thermal and cardiac subsystems, in relation to seizures, and show specific patterns across these parameters 5-7. However, further research on these subsystems of the ANS and whether they may afford statistically meaningful identification of preictal periods in terms of sensitivity and specificity is needed.

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Section: Discussionmentioning

confidence: 95%

Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures

Vieluf

Reinsberger

Atrache

et al. 2020

Sci Rep

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“…It is one of the most used tools to detect and characterize SCRs. However, the feasibility of applying this toolbox to signals from wearable devices is not intensively studied [ 46 ].…”

Section: Resultsmentioning

confidence: 99%

“…A possible solution to deal with this would be to remove all high-frequency noise by using a moving average or by applying a low-pass filter on the EDA signal, as has been recommended in earlier works [ 32 , 40 ], before extracting SCRs using Ledalab. However, there is no consensus on the smoothing parameter (window size), filter type (finite impulse response (FIR)/infinite impulse response), order (2nd- to 32nd-order), and cutoff frequency (0.4–3 Hz) [ 46 ]. It is important to realize that the various filters will not only suppress the high-frequency noise but also reduce the amplitude of SCR in lower frequency ranges.…”

Section: Resultsmentioning

confidence: 99%

A Usability Study of Physiological Measurement in School Using Wearable Sensors

Thammasan

Stuldreher

Schreuders

et al. 2020

Sensors

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Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students’ physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps.

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“…One way of quantifying and subsequently relating the signals obtained from EDA to each of the different musical stimuli is by using a self-assessment manikin (SAM) questionnaire [23,24]. This questionnaire is widely used in psychology to measure the subjectively felt intensity of emotions to compare with the emotional connotation of the different physiological signals captured by electrophysiological devices [42][43][44]. The questionnaire consists of a series of manikins representing different values of valence, activation and dominance [45].…”

Section: Self-assessment Manikinsmentioning

confidence: 99%

Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

Bartolomé-Tomás

Sánchez-Reolid

Fernández-Sotos³

et al. 2020

Sensors

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The detection of emotions is fundamental in many areas related to health and well-being. This paper presents the identification of the level of arousal in older people by monitoring their electrodermal activity (EDA) through a commercial device. The objective was to recognize arousal changes to create future therapies that help them to improve their mood, contributing to reduce possible situations of depression and anxiety. To this end, some elderly people in the region of Murcia were exposed to listening to various musical genres (flamenco, Spanish folklore, Cuban genre and rock/jazz) that they heard in their youth. Using methods based on the process of deconvolution of the EDA signal, two different studies were carried out. The first, of a purely statistical nature, was based on the search for statistically significant differences for a series of temporal, morphological, statistical and frequency features of the processed signals. It was found that Flamenco and Spanish Folklore presented the highest number of statistically significant parameters. In the second study, a wide range of classifiers was used to analyze the possible correlations between the detection of the EDA-based arousal level compared to the participants’ responses to the level of arousal subjectively felt. In this case, it was obtained that the best classifiers are support vector machines, with 87% accuracy for flamenco and 83.1% for Spanish Folklore, followed by K-nearest neighbors with 81.4% and 81.5% for Flamenco and Spanish Folklore again. These results reinforce the notion of familiarity with a musical genre on emotional induction.

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

Cited by 244 publications

References 72 publications

Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures

Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures

A Usability Study of Physiological Measurement in School Using Wearable Sensors

Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

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