A Compressed Sensing Based Decomposition of Electrodermal Activity Signals

Jain, Swayambhoo; Oswal, Urvashi; Xu, Kevin S.; Eriksson, Brian; Haupt, Jarvis

doi:10.1109/tbme.2016.2632523

Cited by 33 publications

(19 citation statements)

References 26 publications

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“…The explicit incorporation of motion artifacts into the tonic component, called the baseline signal, to produce a more realistic EDA model was proposed later [59] to overcome the difficulty where changes in the position of wearable sensors due to movement may lead to rapid changes in signals. The decomposition of EDA into the baseline, the phasic component, and noise was achieved by using a sparse deconvolution and a proposed compressed sensing-based decomposition.…”

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

301

191

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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

301

191

View full text Add to dashboard Cite

show abstract

“…In Eq. 3, ∈ {0,1} chooses either a tonic or a phasic atom, ∈ and ∈ (1) or ∈ (2) or ∈ (3) were the parameters of ℎ atom. Then, the phasic atoms were grouped according to their location with a histogram of values to capture one SCR as:…”

Section: Overview Of Existing Models I) Sparse Decomposition Modelmentioning

confidence: 99%

“…The SCR events signal detection was achieved by decomposing the EDA observation signal based on the compressed sensing method [3]. The observed signal was linearly transformed by using the difference matrix which has Toeplitz matrix ℎ constructed from a vector ℎ ∈ ℝ .…”

Section: Iii) Compressed Sensing-based Decomposition (Csd) Modelmentioning

confidence: 99%

“…Over the past decades, different EDA analysis-based human stress monitoring system has been designed by analyzing the physiological parameters such as heart rate, skin conductivity, respiration rate, electrocardiogram, skin temperature and electroencephalography data. Among different methods, Jain et al [3] proposed a Compressed Sensing-based Decomposition (CSD) of EDA signals to alleviate the effects of unwanted noise components and exposing the underlying physiological signal. By using the compressed sensing-based decomposition with pre-processing, the baseline signal was modeled and facilitated to recover the user's responses.…”

Section: Introductionmentioning

confidence: 99%

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A Modified Compressed Sensing-Based Decomposition Approach to Analyze Electrodermal Activity Signals Using Matrix-Free Convex Optimization

Prabhakaran¹,

Ayyamperumal²

2020

IJIES

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In modern decades, a human socioeconomic lifestyle has been disrupted by their stress levels. Normally, the stress levels were measured based on the Electrodermal Activity (EDA) analysis that analyses the changes in the Skin Conductance Response (SCR) level. As a result, different sensors were designed to analyze the changes in human emotional levels using the EDA signals. During analysis, the unwanted noise components in the signals were removed by using a Compressed Sensing-based Decomposition (CSD) approach. Conversely, this approach has high computational complexity. Therefore in this article, a Modified CSD (MCSD) for EDA signal is proposed that may vary the shape of the impulse response with time and varied noise models. The key aim of this model is to improve the recovery accuracy of EDA signal decomposition and enhance the human stress monitoring system. In this modified model, a computationally efficient decomposition method is proposed by using matrix-free convex optimization modeling that exploits the Toeplitz structure to allow decomposition of EDA signals with provable bounds on the recovery of true SCR events by using the computationally efficient algorithm and guarantee a better recovery accuracy. Finally, this approach is tested on Wearable Stress and Affect Detection (WESAD) dataset to exhibit that the proposed MCSD approach achieves a recovery accuracy of 92.4% which is 2.55% higher than the existing CSD approach.

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“…In this paper, a matrix of biometric data such as Electrodermal Activity (EDA) is collected from users reacting to external stimuli, e.g., watching video content. In prior work, EDA has shown to be useful for a variety of user analytics tasks to assess the reaction of viewers [14,8]. In this setting, m is the number of users and n corresponds to the number of time samples for which biometric reaction is collected.…”

Section: Introductionmentioning

confidence: 99%

Block CUR: Decomposing Matrices Using Groups of Columns

Oswal

Jain

et al. 2019

Machine Learning and Knowledge Discovery in Databases

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A common problem in large-scale data analysis is to approximate a matrix using a combination of specifically sampled rows and columns, known as CUR decomposition. Unfortunately, in many real-world environments, the ability to sample specific individual rows or columns of the matrix is limited by either system constraints or cost. In this paper, we consider matrix approximation by sampling predefined blocks of columns (or rows) from the matrix. We present an algorithm for sampling useful column blocks and provide novel guarantees for the quality of the approximation. This algorithm has application in problems as diverse as biometric data analysis to distributed computing. We demonstrate the effectiveness of the proposed algorithms for computing the Block CUR decomposition of large matrices in a distributed setting with multiple nodes in a compute cluster, where such blocks correspond to columns (or rows) of the matrix stored on the same node, which can be retrieved with much less overhead than retrieving individual columns stored across different nodes. In the biometric setting, the rows correspond to different users and columns correspond to users' biometric reaction to external stimuli, e.g., watching video content, at a particular time instant. There is significant cost in acquiring each user's reaction to lengthy content so we sample a few important scenes to approximate the biometric response. An individual time sample in this use case cannot be queried in isolation due to the lack of context that caused that biometric reaction. Instead, collections of time segments (i.e., blocks) must be presented to the user. The practical application of these algorithms is shown via experimental results using real-world user biometric data from a content testing environment.

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A Compressed Sensing Based Decomposition of Electrodermal Activity Signals

Cited by 33 publications

References 26 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 Modified Compressed Sensing-Based Decomposition Approach to Analyze Electrodermal Activity Signals Using Matrix-Free Convex Optimization

Block CUR: Decomposing Matrices Using Groups of Columns

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