从功能性近红外光谱法的光学信号中提取心动和呼吸特征

潘津津; 焦学军; 姜国华; 焦典; 姜劲; 张朕; 曹勇; 徐凤刚

doi:doi:10.3788/aos201535.0917001

光学学报, 2015, 35 (9): 0917001, 网络出版: 2015-09-01

从功能性近红外光谱法的光学信号中提取心动和呼吸特征下载： 624次

Extracting Cardiac and Respiratory Features from Optical Signal of Functional Near-Infrared Spectroscopy

论文大纲

潘津津 ^1,*焦学军 ¹姜国华 ¹焦典 ²姜劲 ¹张朕 ¹曹勇 ¹徐凤刚 ¹

作者单位

¹ 中国航天员科研训练中心人因工程重点实验室, 北京 100094

² 天津大学精密仪器与光电子工程学院, 天津 300072

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摘要

认知神经科学的快速发展，使得各种生理参数的客观测量得以实现，其中功能性近红外光谱(fNIRS)是一种新兴的脑成像方法，可以检测经过人体皮肤组织的血液动力学指标，包括含氧血红蛋白(HbO)、脱氧血红蛋白(Hb)和总血红蛋白(tHb)含量。心电图(ECG)、呼吸波(RSP)则是常用的生理参数检测方法。研究目的是尝试利用fNIRS 方法测得心率(HR)和呼吸率(BR)特征，利用时域形态学特征法、频域带通滤波法以及小波分解与重构方法提取心率及呼吸率，并与ECG、RSP 真实信号的HR(77.0199)、BR(22.9153)进行对比。结果发现三种方法均可从fNIRS 信号中提取出HR 信号，其中用频域带通滤波器方法得到的HR 为76.8807，偏差最小为-0.1392，利用相同方法提取的BR 为21.7039，偏差为-1.2114。基本实现了从fNIRS信号中提取心率和呼吸率的目标。

Abstract

The rapid development of cognitive neuroscience makes objective determination of various physiological parameters possible. Functional near-infrared spectroscopy (fNIRS) is an emerging brain imaging tool that can detect human skin tissue hemodynamic indices including oxygenated hemoglobin (HbO), deoxygenated hemoglobin (Hb) and total hemoglobin (tHb). Electrocardiograph (ECG) and respiration wave (RSP) are also two important physiological parameter determination methods. In order to obtain multiple physiological parameters using fNIRS only, three algorithms, including time-domain waveform characteristic analysis, frequency-domain band-pass filtering and wavelet decomposition and reconstruction, are used to calculate heart rate (HR) and breath rate (BR) based on the HbO data collected by fNIRS. The calculated HR and BR results are compared with the real HR (77.0199) surveyed by ECG and BR (22.9153) surveyed by RSP. The results show that the three methods can all extract HR from fNIRS effectively, wherein the band-pass filtering can extract the most accurate HR (76.8807) with the deviation of -0.1392. The BR (21.7039) with the deviation of -1.2114 is also calculated by the same algorithm. Extracting HR and BR features using fNIRS signal is realized.

参考文献

[1] 潘津津, 焦学军, 姜劲, 等. 利用功能性近红外光谱成像方法评估脑力负荷[J]. 光学学报, 2014, 34(11): 1130002.

Pan Jinjin, Jiao Xuejun, Jiang Jing, et al.. Mental workload assessment based on functional near- infrared spectroscopy[J]. Acta Optica Sinica, 2014, 34(11): 1130002.

[2] 周振宇, 杨宏宇, 龚辉, 等. 基于希尔伯特-黄变换的近红外脑功能成像信号分析[J]. 光学学报, 2007, 27(2): 307-312.

Zhou Zhenyu, Yang Hongyu, Gong Hui, et al.. Brain signal analysis of functional near-infrared imaging based on Hilbert-Huang transform[J]. Acta Optica Sinica, 2007, 27(2): 307-312.

[3] Durantin G, Gagnon J F, Tremblay S, et al.. Using near infrared spectroscopy and heart rate variability to detect mental overload[J]. Behavioural Brain Research, 2014, 259(2): 16-23.

[4] Elmes D, Kantowitz B, Roediger III H. Research Methods in Psychology[M]. Belmont: Cengage Learning, 2011: 221-230.

[5] 葛列众, 李宏汀, 王笃明. 工程心理学[M]. 北京：中国人民大学出版社, 2012: 185-190.

Ge Liezhong, Li Hongting, Wang Duming. Engineering Psychology [M]. Beijing: Renmin University of China Press, 2012: 185-190.

[6] Strait M, Scheutz M. What we can and cannot (yet) do with functional near infrared spectroscopy[J]. Frontiers in Neuroscience, 2014, 8(5):117-125.

[7] Pan J J, Jiao X J. New application, development and aerospace prospect of fNIRS[J]. Engineering, 2013, 5(5): 47-52.

[8] 朱丹, 吴龟灵, 骆清铭, 等. 生物组织中光子微区扩散理论研究[J]. 光学学报, 2006, 25(5): 638-642.

Zhu Dan, Wu Guiling, Luo Qingming, et al.. Photonics diffusion for small source- detector separations of tissue[J]. Acta Optica Sinica, 2006, 25(5): 638-642.

[9] Gu Y, Song Y, Constantinescu A. Comparison of tumor vascular blood volume measured by near infrared spectroscopy and 19F NMR spectroscopy[J]. Chinese Optics Letters, 2005, 3(101): S179-S181.

[10] Xia H, Liu W Q, Zhang Y J, et al.. An approach of open-path gas sensor based on tunable diode laser absorption spectroscopy[J]. Chinese Optics Letters, 2008, 6(6): 437-440.

[11] Villringer A, Chance B. Non-invasive optical spectroscopy and imaging of human brain function[J]. Trends in Neurosciences, 1997, 20(10): 435-442.

[12] Jiao X J, Bai J, Chen S G, et al.. Monitoring mental fatigue in analog space environment using optical brain imaging[J]. Engineering, 2013, 5(5): 53-57.

[13] 吴春阳, 卢启鹏, 丁海泉, 等. 利用人体组织液进行近红外无创血糖测量[J]. 光学学报, 2013, 33 (11): 1117001.

Wu Chunyang，Lu Qipeng，Ding Haiquan，et al.. Near-infrared non-invasive blood glucose measurement using human tissue fluid[J]. Acta Optica Sinica, 2013, 33(11): 1117001.

[14] Halperin H R, Berger R D. ECG artifact reduction system[P]. US Patent 8666480, 2014.

[15] Kwon S, Kang S, Lee Y, et al.. Unobtrusive monitoring of ECG-derived features during daily smartphone use[C]. 2014 36th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBC), 2014: 4964-4967.

[16] Okawai H, Yajima T, Imamatsu T, et al.. Sophisticated rate control of respiration and pulse during sleep studied by body motion wave[C]. XIII Mediterranean Conference on Medical and Biological Engineering and Computing, 2014: 1895-1898.

[17] 高铎瑞, 钟刘军, 赵昭, 等. 基于Matlab 的变焦光学系统设计[J]. 中国激光, 2014, 41(4): 0416002.

Gao Duorui, Zhong Liujun, Zhao Zhao, et al.. Zoom optical system design based on Matlab[J]. Chinese J Lasers, 2014, 41(4): 0416002.

[18] Gayda M, Gremeaux V, Drigny J, et al.. Muscle VO2 and forearm blood flow repeatability during venous and arterial occlusions in healthy and coronary heart disease subjects[J]. Clinical Hemorheology and Microcirculation, 2015, 59(2): 177-183.

[19] Baker W B, Parthasarathy A B, Busch D R, et al.. Modified Beer-Lambert law for blood flow[J]. Biomedical Optics Express, 2014, 5(11): 4053-4075.

[20] Dixit A, Sharma P. A comparative study of wavelet thresholding for image denoising[J]. Image, Graphics, and Signal Processing, 2014, 12: 39-46.

[21] Upadhyay P, Kar R, Mandal D, et al.. An efficient differential evolution with wavelet mutation algorithm for optimal IIR filter design[J]. International Journal of Bio-Inspired Computation, 2014, 6(5): 350-367.

[22] Withayachumnankul W, O'Hara J F, Cao W, et al.. Lower bound of sample thickness in terahertz time-domain spectroscopy[C]. 2014 39th International Conference on Infrared, Millimeter, and Terahertz Waves, IEEE, 2014: 1-2.

[23] Targum S D, Burch D J, Asgharnejad M, et al.. Use of band-pass filter analysis to evaluate outcomes in an antidepressant trial for treatment resistant patients[J]. European Neuropsychopharmacology, 2014, 24(8): 1188-1195.

[24] Kamma A, Reddy G S, Parmar R S, et al.. Dual-band filter for WiMAX and WLAN with improved upper stop band performance[J]. Progress in Electromagnetics Research C, 2014, 50: 131-138.

潘津津, 焦学军, 姜国华, 焦典, 姜劲, 张朕, 曹勇, 徐凤刚. 从功能性近红外光谱法的光学信号中提取心动和呼吸特征[J]. 光学学报, 2015, 35(9): 0917001. Pan Jinjin, Jiao Xuejun, Jiang Guohua, Jiao Dian, Jiang Jing, Zhang Zhen, Cao Yong, Xu Fenggang. Extracting Cardiac and Respiratory Features from Optical Signal of Functional Near-Infrared Spectroscopy[J]. Acta Optica Sinica, 2015, 35(9): 0917001.

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