Evidence map›Paper›PMID 35212200›Full record

ArticleJournal of biomedical optics2022

Hybrid motion artifact detection and correction approach for functional near-infrared spectroscopy measurements.

Lin Gao, Yuhui Wei, Yifei Wang, Gang Wang, Quan Zhang, Jianbao Zhang, Xiang Chen, Xiangguo Yan

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Article in Journal of biomedical optics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

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14citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Lin GaoXi'an Jiaotong University, State Key Laboratory of Manufacturing Systems Engineering, Xi'an, Shaanxi, China.
Yuhui WeiXi'an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministr, China.
Yifei WangXi'an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministr, China.
Gang WangXi'an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministr, China.
Quan ZhangMassachusetts General Hospital, Harvard Medical School, Department of Psychiatry, Charlestown, Massa, United States.
Jianbao ZhangXi'an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministr, China.
Xiang ChenXi'an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministr, China.
Xiangguo YanXi'an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Education Ministr, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

significanceFunctional near-infrared spectroscopy (fNIRS) is a promising optical neuroimaging technique, measuring the hemodynamic signals from the cortex. However, improving signal quality and reducing artifacts arising from oscillation and baseline shift (BS) are still challenging up to now for fNIRS applications.

aimConsidering the advantages and weaknesses of the different algorithms to reduce the artifact effect in fNIRS signals, we propose a hybrid artifact detection and correction approach. APPROACH: First, distinct artifact detection was realized through an fNIRS detection strategy. Then the artifacts were divided into three categories: BS, slight oscillation, and severe oscillation. A comprehensive correction was applied through three main steps: severe artifact correction by cubic spline interpolation, BS removal by spline interpolation, and slight oscillation reduction by dual-threshold wavelet-based method.

resultsUsing fNIRS data acquired during whole night sleep monitoring, we compared the performance of our approach with existing algorithms in signal-to-noise ratio (SNR) and Pearson's correlation coefficient (R). We found that the proposed method showed improvements in performance in SNR and R with strong stability.

conclusionsThese results suggest that the new hybrid artifact detection and correction method enhances the viability of fNIRS as a functional neuroimaging modality.

Indexed as

ArtifactsSpectroscopy, Near-InfraredAlgorithmsFunctional NeuroimagingMotionartifact correctionartifact detectionfunctional near-infrared spectroscopyhybrid approach

Identifiers

PMID35212200
PMCPMC8871689

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.