artifact removal
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2022 ◽  
Vol 151 ◽  
pp. 106936
Author(s):  
Tessa J.H. Krause ◽  
Troy R. Allen ◽  
James M. Fraser

2022 ◽  
Vol 72 ◽  
pp. 103301
Author(s):  
Ruisen Huang ◽  
Kunqiang Qing ◽  
Dalin Yang ◽  
Keum-Shik Hong

Author(s):  
Velu Prabhakar Kumaravel ◽  
Elisabetta Farella ◽  
Eugenio Parise ◽  
Marco Buiatti

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 13
Author(s):  
Tamara Škorić

The development of smart cars with e-health services allows monitoring of the health condition of the driver. Driver comfort is preserved by the use of capacitive electrodes, but the recorded signal is characterized by large artifacts. This paper proposes a method for reducing artifacts from the ECG signal recorded by capacitive electrodes (cECG) in moving subjects. Two dominant artifact types are coarse and slow-changing artifacts. Slow-changing artifacts removal by classical filtering is not feasible as the spectral bands of artifacts and cECG overlap, mostly in the band from 0.5 to 15 Hz. We developed a method for artifact removal, based on estimating the fluctuation around linear trend, for both artifact types, including a condition for determining the presence of coarse artifacts. The method was validated on cECG recorded while driving, with the artifacts predominantly due to the movements, as well as on cECG recorded while lying, where the movements were performed according to a predefined protocol. The proposed method eliminates 96% to 100% of the coarse artifacts, while the slow-changing artifacts are completely reduced for the recorded cECG signals larger than 0.3 V. The obtained results are in accordance with the opinion of medical experts. The method is intended for reliable extraction of cardiovascular parameters to monitor driver fatigue status.


Author(s):  
Mingqi Zhao ◽  
Gaia Bonassi ◽  
Roberto Guarnieri ◽  
Elisa Pelosin ◽  
Alice Nieuwboer ◽  
...  

Abstract Objective. Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g., ocular, myogenic) and non-biological (e.g., movement-related) artifacts, which -depending on their extent- may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts. Approach. In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during auditory stimulation, as well as event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches. Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts. Significance. Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contribute to further development of mobile brain/body imaging applications.


2021 ◽  
Vol 54 (6) ◽  
Author(s):  
Hiroki Ogawa ◽  
Shunsuke Ono ◽  
Yuki Watanabe ◽  
Yukihiro Nishikawa ◽  
Shotaro Nishitsuji ◽  
...  

Small-angle X-ray scattering (SAXS) coupled with computed tomography (CT), denoted SAXS-CT, has enabled the spatial distribution of the characteristic parameters (e.g. size, shape, surface, length) of nanoscale structures inside samples to be visualized. In this work, a new scheme with Tikhonov regularization was developed to remove the effects of artifacts caused by streak scattering originating from the reflection of the incident beam in the contour regions of the sample. The noise due to streak scattering was successfully removed from the sinogram image and hence the CT image could be reconstructed free from artifacts in the contour regions.


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