Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network

被引:20
作者
Lee, Gihyoun [1 ]
Jin, Sang Hyeon [1 ]
An, Jinung [1 ]
机构
[1] DGIST, Convergence Res Ctr Wellness, Daegu 42988, South Korea
基金
新加坡国家研究基金会;
关键词
functional near-infrared spectroscopy; motion artifact; artificial neural network; signal entropy; wavelet transform; CEREBRAL-BLOOD; FMRI; GAIT; IMPROVEMENT; ACTIVATION;
D O I
10.3390/s18092957
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map.
引用
收藏
页数:16
相关论文
共 44 条
[1]   Real-time imaging of human brain function by near-infrared spectroscopy using an adaptive general linear model [J].
Abdelnour, A. Farras ;
Huppert, Theodore .
NEUROIMAGE, 2009, 46 (01) :133-143
[2]   Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms [J].
Abibullaev, Berdakh ;
An, Jinung .
MEDICAL ENGINEERING & PHYSICS, 2012, 34 (10) :1394-1410
[3]   DISCRETE COSINE TRANSFORM [J].
AHMED, N ;
NATARAJAN, T ;
RAO, KR .
IEEE TRANSACTIONS ON COMPUTERS, 1974, C 23 (01) :90-93
[4]  
Antoniadis A., 2012, Wavelets and statistics, V103
[5]  
Boashash B., 2015, TIME FREQUENCY SIGNA
[6]   NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy [J].
Chul, Jong ;
Tak, Sungho ;
Jang, Kwang Eun ;
Jung, Jinwook ;
Jang, Jaeduck .
NEUROIMAGE, 2009, 44 (02) :428-447
[7]   Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons [J].
Cigizoglu, HK .
ADVANCES IN WATER RESOURCES, 2004, 27 (02) :185-195
[8]   SYSTEM FOR LONG-TERM MEASUREMENT OF CEREBRAL BLOOD AND TISSUE OXYGENATION ON NEWBORN-INFANTS BY NEAR-INFRARED TRANS-ILLUMINATION [J].
COPE, M ;
DELPY, DT .
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 1988, 26 (03) :289-294
[9]   Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics [J].
Cui, Xu ;
Bray, Signe ;
Reiss, Allan L. .
NEUROIMAGE, 2010, 49 (04) :3039-3046
[10]   DE-NOISING BY SOFT-THRESHOLDING [J].
DONOHO, DL .
IEEE TRANSACTIONS ON INFORMATION THEORY, 1995, 41 (03) :613-627