Evaluation and diagnosis of brain death by Functional near-infrared spectroscopy

被引:0
|
作者
Pan, Boan [1 ]
Zhong, Fulin [1 ]
Huang, Xiaobo [2 ]
Pan, Lingai [2 ]
Lu, Sen [2 ]
Li, Ting [1 ]
机构
[1] Univ Elect Sci & Technol China, State Key Lab Elect Thin Film & Integrated Device, Chengdu 610054, Peoples R China
[2] Univ Elect Sci & Technol China, Sichuan Prov Peoples Hosp, Chengdu 610032, Peoples R China
来源
ADVANCED BIOMEDICAL AND CLINICAL DIAGNOSTIC AND SURGICAL GUIDANCE SYSTEMS XV | 2017年 / 10054卷
基金
美国国家科学基金会;
关键词
brain death; functional near infrared spectroscopy; fractional concentration of inspired oxygen; oxyhemoglobin concentration; deoxyhemoglobins concentration;
D O I
10.1117/12.2252142
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Brain death, the irreversible and permanent loss of the brain and brainstem functions, is hard to be judged precisely for some clinical reasons. The traditional diagnostic methods are time consuming, expensive and some are even dangerous. Functional near infrared spectroscopy (FNIRS), using the good scattering properties of major component of blood to NIR, is capable of noninvasive monitoring cerebral hemodynamic responses. Here, we attempt to use portable FNIRS under patients' natural state for brain death diagnosis. Ten brain death patients and seven normal subjects participated in FNIRS measurements. All of them were provided different fractional concentration of inspired oxygen (FIO2) in different time periods. We found that the concentration variation of deoxyhemoglobin concentration (Delta[Hb]) presents the trend of decrease in the both brain death patients and normal subjects with the raise of the FIO2, however, the data in the normal subjects is more significant. And the concentration variation of oxyhemoglobins concentration (Delta[HbO(2)]) emerges the opposite trends. Thus Delta[HbO(2)]/Delta[Hb] in brain death patients is significantly higher than normal subjects, and emerges the rising trend as time went on. The findings indicated the potential of FNIRS-measured hemodynamic index in diagnosing brain death.
引用
收藏
页数:6
相关论文
共 50 条
  • [21] The brain state of motor imagery is reflected in the causal information of functional near-infrared spectroscopy
    Du, Qiang
    Luo, Jingjing
    Chu, Chenxi
    Wang, Youhao
    Cheng, Qiying
    Guo, Shijie
    NEUROREPORT, 2022, 33 (03) : 137 - 144
  • [22] Evaluating Working Memory Capacity with Functional Near-Infrared Spectroscopy Measurement of Brain Activity
    Yamamoto U.
    Mashima N.
    Hiroyasu T.
    Journal of Cognitive Enhancement, 2018, 2 (3) : 217 - 224
  • [23] Bundled-Optode Method for Detection of Brain Activity in Functional Near-Infrared Spectroscopy
    Hoang-Dung Nguyen
    Keum-Shik Hong
    2016 16TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS), 2016, : 1112 - 1117
  • [24] Using Functional Near-Infrared Spectroscopy to Assess Brain Activation Evoked by Guilt and Shame
    Duan, Lian
    Feng, Qiudi
    Xu, Pengfei
    FRONTIERS IN HUMAN NEUROSCIENCE, 2020, 14
  • [25] Vigilance Detection Based on Functional Near-Infrared Spectroscopy
    Cao Yong
    Jiao Xunjun
    Jiang Jin
    Fu Jiahao
    Pan Jinjin
    ACTA OPTICA SINICA, 2018, 38 (03)
  • [26] Functional Near-Infrared Spectroscopy based Brain Activity Classification for Development of a Brain-Computer Interface
    Naseer, Noman
    Hong, Keum-Shik
    2012 INTERNATIONAL CONFERENCE ON ROBOTICS AND ARTIFICIAL INTELLIGENCE (ICRAI), 2012, : 174 - 178
  • [27] Evaluation of the Short-Term Music Therapy on Brain Functions of Preterm Infants Using Functional Near-Infrared Spectroscopy
    Ren, Haoran
    Zou, Liangyan
    Wang, Laishuan
    Lu, Chunmei
    Yuan, Yafei
    Dai, Chenyun
    Chen, Wei
    FRONTIERS IN NEUROLOGY, 2021, 12
  • [28] Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review
    Huang, Ruisen
    Hong, Keum-Shik
    Yang, Dalin
    Huang, Guanghao
    FRONTIERS IN NEUROSCIENCE, 2022, 16
  • [29] Evaluation of residual cognition in patients with disorders of consciousness based on functional near-infrared spectroscopy
    Si, Juanning
    Yang, Yi
    Xu, Long
    Xu, Tianshuai
    Liu, Hao
    Zhang, Yujin
    Jing, Rixing
    Li, Jinglian
    Wang, Dongdong
    Wu, Sijin
    He, Jianghong
    NEUROPHOTONICS, 2023, 10 (02)
  • [30] Random Subspace Ensemble Learning for Functional Near-Infrared Spectroscopy Brain-Computer Interfaces
    Shin, Jaeyoung
    FRONTIERS IN HUMAN NEUROSCIENCE, 2020, 14