Wavelet-based motion artifact removal for electrodermal activity

Chen, Weixuan; Jaques, Natasha; Taylor, Sara; Sano, Akane; Fedor, Szymon; Picard, Rosalind W.

doi:10.1109/embc.2015.7319814

Cited by 38 publications

(23 citation statements)

References 33 publications

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“…A method for removing motion artifacts from EDA using a stationary wavelet transform was proposed [76]. The wavelet coefficients are modeled as a Gaussian mixture distribution corresponding to the underlying tonic and phasic components of EDA.…”

Section: Motion Artifacts Detection and Correctionmentioning

confidence: 99%

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

Posada-Quintero

Chon

2020

Sensors

311

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: Motion Artifacts Detection and Correctionmentioning

confidence: 99%

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

Posada-Quintero

Chon

2020

Sensors

311

191

View full text Add to dashboard Cite

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“…Therefore, it is feasible to use variance values calculated from past data as priori distributed variance values. Chen et al [23] proposed a processing strategy similar to Molvai. However, they directly converted the probability threshold into the screening threshold for specific wavelet decomposition coefficient to deal with motion artifact.…”

Section: Discussionmentioning

confidence: 99%

“…This characteristic can be used to carry out discrimination and processing head movement interference signals. We adopted Molavi’s wavelet processing strategy to manage the movement interferences [23].…”

Section: Methodsmentioning

confidence: 99%

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An on-line processing strategy for head movement interferences removal of dynamic brain electrical impedance tomography based on wavelet decomposition

Zhang

et al. 2019

BioMed Eng OnLine

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Background Head movement interferences are a common problem during prolonged dynamic brain electrical impedance tomography (EIT) clinical monitoring. Head movement interferences mainly originate from body movements of patients and nursing procedures performed by medical staff, etc. These body movements will lead to variation in boundary voltage signals, which affects image reconstruction. Methods This study employed a data preprocessing method based on wavelet decomposition to inhibit head movement interferences in brain EIT data. Mixed Gaussian models were applied to describe the distribution characteristics of brain EIT data. We identified head movement signal through the differences in distribution characteristics of corresponding wavelet decomposition coefficients between head movement artifacts and normal signals, and then managed the contaminated data with improved on-line wavelet processing methods. Results To validate the efficacy of the method, simulated signal experiments and human data experiments were performed. In the simulation experiment, the simulated movement artifact was significantly reduced and data quality was improved with indicators’ increase in PRD and correlation coefficient. Human data experiments demonstrated that this method effectively suppressed head movement in signals and reduce artifacts resulting from head movement artifacts in images. Conclusion In this paper, we proposed an on-line strategy to manage the head movement interferences from the brain EIT data based on the distribution characteristics of wavelet coefficients. Our strategy is capable of reducing the movement interference in the data and improving the reconstructed images. This work would improve the clinical practicability of brain EIT and contribute to its further promotion.

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“…Other studies have used wavelet stationary transform models to remove the artifacts in EDA signals automatically. Chen et al 32 modeled the wavelet coefficients using a Gaussian mixture distribution. This model requires the estimation of three parameters using the expectation-maximization algorithm.…”

Section: Previous Workmentioning

confidence: 99%

Automatic Artifact Recognition and Correction for Electrodermal Activity in Uncontrolled Environments

Llanes-Jurado

Carrasco-Ribelles

Alcañíz

et al. 2021

Preprint

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Scholars are increasingly using electrodermal activity (EDA) to assess cognitive-emotional states in laboratory environments, while recent applications have recorded EDA in uncontrolled settings, such as daily-life and virtual reality (VR) contexts, in which users can freely walk and move their hands. However, these records can be affected by major artifacts stemming from movements that can obscure valuable information. Previous work has analyzed signal correction methods to improve the quality of the signal or proposed artifact recognition models based on time windows. Despite these efforts, the correction of EDA signals in uncontrolled environments is still limited, and no existing research has used a signal manually corrected by an expert as a benchmark. This work investigates different machine learning and deep learning architectures, including support vector machines, recurrent neural networks (RNNs), and convolutional neural networks, for the automatic artifact recognition of EDA signals. The data from 44 subjects during an immersive VR task were collected and cleaned by two experts as ground truth. The best model, which used an RNN fed with the raw signal, recognized 72% of the artifacts and had an accuracy of 87%. An automatic correction was performed on the detected artifacts through a combination of linear interpolation and a high degree polynomial. The evaluation of this correction showed that the automatically and manually corrected signals did not present differences in terms of phasic components, while both showed differences to the raw signal. This work provides a tool to automatically correct artifacts of EDA signals which can be used in uncontrolled conditions, allowing for the development of intelligent systems based on EDA monitoring without human intervention.

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Wavelet-based motion artifact removal for electrodermal activity

Cited by 38 publications

References 33 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

An on-line processing strategy for head movement interferences removal of dynamic brain electrical impedance tomography based on wavelet decomposition

Automatic Artifact Recognition and Correction for Electrodermal Activity in Uncontrolled Environments

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