基于渦輪式氣體流量傳感器的用力呼氣容量計(jì)算方法

王辰碩; 何光強(qiáng); 李玥琪; 趙榮建; 陳賢祥; 杜利東; 趙湛; 方震

doi:10.11999/JEIT190051

基于渦輪式氣體流量傳感器的用力呼氣容量計(jì)算方法

doi: 10.11999/JEIT190051

王辰碩^{1, 2},
何光強(qiáng)^{1, 2},
李玥琪^{1, 2},
趙榮建^{1, 2},
陳賢祥¹,
杜利東¹,
趙湛^{1, 2},
方震^{1, 2, ,}

1.
中國(guó)科學(xué)院電子學(xué)研究所傳感技術(shù)國(guó)家重點(diǎn)實(shí)驗(yàn)室 ??北京 ??100190
2.
中國(guó)科學(xué)院大學(xué) 北京 100049

基金項(xiàng)目: 北京市自然科學(xué)基金重點(diǎn)研究專(zhuān)項(xiàng)(Z16003)，國(guó)家重點(diǎn)研發(fā)計(jì)劃(2016YFC1304302)

詳細(xì)信息

作者簡(jiǎn)介:
王辰碩：男，1993年生，博士生，研究方向?yàn)樯畔⒏兄c計(jì)算

何光強(qiáng)：男，1995年生，碩士生，研究方向?yàn)榭纱┐魇郊夹g(shù)

李玥琪：女，1990年生，博士生，研究方向?yàn)榭纱┐魇郊夹g(shù)

趙榮建：男，1985年生，博士生，研究方向?yàn)樯畔⒏兄夹g(shù)

陳賢祥：男，1979年生，副研究員，碩士生導(dǎo)師，研究方向?yàn)榭纱┐魇郊夹g(shù)

杜利東：男，1981年生，助理研究員，研究方向?yàn)槲⒓{制造技術(shù)

趙湛：男，1958年生，研究員，博士生導(dǎo)師，研究方向?yàn)槲⒓{制造技術(shù)，無(wú)線(xiàn)傳感器網(wǎng)絡(luò)，生命信息感知與計(jì)算

方震：男，1976年生，研究員，博士生導(dǎo)師，研究方向?yàn)榭纱┐魇郊夹g(shù)

通訊作者:
方震　zfang@mail.ie.ac.cn

中圖分類(lèi)號(hào): TN051
計(jì)量
- 文章訪(fǎng)問(wèn)數(shù): 2443
- HTML全文瀏覽量: 1279
- PDF下載量: 67
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2009-01-18
- 修回日期: 2019-05-14
- 網(wǎng)絡(luò)出版日期: 2019-06-04
- 刊出日期: 2019-10-01

Calculation of Forced Vital Capacity Based on Turbine Air Flow Sensor

Chenshuo WANG^{1, 2},
Guangqiang HE^{1, 2},
Yueqi LI^{1, 2},
Rongjian ZHAO^{1, 2},
Xianxiang CHEN¹,
Lidong DU¹,
Zhan ZHAO^{1, 2},
Zhen FANG^{1, 2
, ,}

1.
State Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The Key Project of Beijing Municipal Natural Science Foundation(Z16003), The National Key Research and Development Project(2016YFC1304302)

摘要

摘要: 渦輪式氣體流量傳感器在用力肺功能測(cè)試中用于記錄人體呼氣信號(hào)，由于旋轉(zhuǎn)慣性，對(duì)于相同用力呼氣容量(FVC)值，測(cè)量結(jié)果因呼出氣體流量而異，且差異值通常不可接受。針對(duì)該問(wèn)題，該文通過(guò)在傳統(tǒng)穩(wěn)態(tài)渦輪流量計(jì)算模型的基礎(chǔ)上引入速度懲罰項(xiàng)，構(gòu)建一種FVC速度懲罰模型，與此同時(shí)，提出使用過(guò)幅降采樣渦輪旋轉(zhuǎn)周數(shù)算法，二者結(jié)合，提高了FVC測(cè)試結(jié)果的可接受性。利用國(guó)際通用的標(biāo)準(zhǔn)3 L定標(biāo)桶，模擬真實(shí)用力肺功能測(cè)試過(guò)程，對(duì)算法的有效性進(jìn)行驗(yàn)證。實(shí)驗(yàn)結(jié)果表明：所提方法能夠有效降低前述差異，在一定程度上滿(mǎn)足美國(guó)胸科協(xié)會(huì)(ATS)和歐洲呼吸學(xué)會(huì)(ERS)所提出的用力肺功能測(cè)試可接受標(biāo)準(zhǔn)和準(zhǔn)確度要求。
- 用力肺功能測(cè)試 /
- 渦輪傳感器 /
- 呼氣信號(hào)處理 /
- 用力呼氣容量 /
- 慢性阻塞性肺病
Abstract: Currently, the turbine air flow sensors are widely used to record the human exhalation signals in spirometry, but test results vary due to different expiratory flow for the same Forced Vital Capacity(FVC) measurements, and the differences are usually not in an acceptable range. To address this issue, a FVC velocity penalty model is proposed by introducing speed penalty items to the traditional mathematical model of turbine. Moreover, an over-amplitude drop sampling approach is used to calculate the rotations of the turbine due to the needs for the velocity penalty model to be able to accurately obtain the number of turbine rotations. The performance of the proposed approach is evaluated by using a syringe dispenser of 3L capacity, and results demonstrate that it can reduce the differences and meet the acceptable and accuracy criteria of the American Thoracic Society(ATS) and the European Respiratory Society(ERS) to some extent.
- Spirometry /
- Turbine sensors /
- Respiratory signal processing /
- Forced Vital Capacity(FVC) /
- Chronic Obstructive Pulmonary Disease(COPD)

HTML全文

圖 1 呼氣信號(hào)采集系統(tǒng)框圖

下載: 全尺寸圖片幻燈片

圖 2 低頻信號(hào)檢測(cè)

下載: 全尺寸圖片幻燈片

圖 3 高頻信號(hào)檢測(cè)

下載: 全尺寸圖片幻燈片

圖 4 3種峰值流速下對(duì)不同體積的20次測(cè)試結(jié)果

下載: 全尺寸圖片幻燈片

圖 5 Bland-Altman圖

下載: 全尺寸圖片幻燈片

圖 6 測(cè)量結(jié)果與真實(shí)值的相關(guān)性

下載: 全尺寸圖片幻燈片

表 1 10次隨機(jī)氣體推進(jìn)實(shí)驗(yàn)對(duì)渦輪旋轉(zhuǎn)周數(shù)測(cè)量值與真實(shí)值對(duì)比

實(shí)驗(yàn)(次) 1 2 3 4 5 6 7 8 9 10

真實(shí)值(周) 256 150 178 358 321 89 205 316 56 124
測(cè)量值(周) 253 148 172 354 316 87 204 310 56 122

下載: 導(dǎo)出CSV

參考文獻(xiàn)(19)

VAN DER HEIJDEN M, LUCAS P J F, LIJNSE B, et al. An autonomous mobile system for the management of COPD[J]. Journal of Biomedical Informatics, 2013, 46(3): 458–469. doi: 10.1016/j.jbi.2013.03.003

POLVERINO F and CELLI B. The challenge of controlling the COPD epidemic: Unmet needs[J]. The American Journal of Medicine, 2018, 131(9 Suppl 1): 1–6.

WANG Chenshuo, CHEN Xianxiang, ZHAO Rongjian, et al. Predicting Forced Vital Capacity (FVC) using Support Vector Regression (SVR)[J]. Physiological Measurement, 2019, 40(2): 025010. doi: 10.1088/1361-6579/ab031c

World Health Organization 2018. Chronic Obstructive Pulmonary Disease (COPD)[EB/OL]. https://www.who.int/respiratory/copd/en/, 2018.

SHARAN R V, ABEYRATNE U R, SWARNKAR V R, et al. Predicting spirometry readings using cough sound features and regression[J]. Physiological Measurement, 2018, 39(9): 095001. doi: 10.1088/1361-6579/aad948

ZHONG Nanshan, WANG Chen, YAO Wanzhen, et al. Prevalence of chronic obstructive pulmonary disease in china: A large, population-based survey[J]. American Journal of Respiratory and Critical Care Medicine, 2007, 176(8): 753–760. doi: 10.1164/rccm.200612-1749OC

JOHNSON J D and THEURER W M. A stepwise approach to the interpretation of pulmonary function tests[J]. American Family Physician, 2014, 89(5): 359–366.

MILLER M R, HANKINSON J, BRUSASCO V, et al. Standardisation of spirometry[J]. European Respiratory Journal, 2005, 26(2): 319–338. doi: 10.1183/09031936.05.00034805

WAN E S, FORTIS S, REGAN E A, et al. Longitudinal phenotypes and mortality in preserved ratio impaired spirometry in the COPDgene study[J]. American Journal of Respiratory and Critical Care Medicine, 2018, 198(11): 1397–1405. doi: 10.1164/rccm.201804-0663OC

RAO M V A, KAUSTHUBHA N K, YADAV S, et al. Automatic prediction of spirometry readings from cough and wheeze for monitoring of asthma severity[C]. The 25th European Signal Processing Conference, Kos, Greece, 2017: 41–45.

SAHIN D, üBEYLI E D, ILBAY G, et al. Diagnosis of airway obstruction or restrictive spirometric patterns by multiclass support vector machines[J]. Journal of Medical Systems, 2010, 34(5): 967–973. doi: 10.1007/s10916-009-9312-7

何子軍. 呼氣信號(hào)分析方法及其在肺功能檢查中的應(yīng)用研究[D]. [博士論文], 中國(guó)科學(xué)技術(shù)大學(xué), 2014: 17–22.

HE Zijun. The method for analyzing expiratory signals and its application in pulmonary function testing[D]. [Ph.D. dissertation], University of Science and Technology of China, 2014: 17–22.

CHEN Xiaoying, WANG Na, CHEN Yue, et al. Costs of chronic obstructive pulmonary disease in urban areas of China: A cross-sectional study in four cities[J]. International Journal of Chronic Obstructive Pulmonary Disease, 2016, 11(1): 2625–2632.

LOU P, ZHU Yanan, CHEN Peipei, et al. Vulnerability, beliefs, treatments and economic burden of chronic obstructive pulmonary disease in rural areas in China: A cross-sectional study[J]. BMC Public Health, 2012, 12: 287. doi: 10.1186/1471-2458-12-287

BOSHNYAK L L, BYZOV L N, KAZNACHEEV B A, et al. Calibration of turbo-tachometric flowmeters[J]. Measurement Techniques, 1962, 5(7): 592–596. doi: 10.1007/BF00989035

LOPATA V, POPOV A, ELSHABBAH M, et al. Dynamic errors of forced expiration measurements by spirometers[C]. The 35th IEEE International Conference on Electronics and Nanotechnology, Kiev, Ukraine, 2015: 406–408.

SOKOL Y I, TOMASHEVSKY R S, and KOLISNYK K V. Turbine spirometers metrological support[C]. 2016 International Conference on Electronics and Information Technology, Odessa, Ukraine, 2016: 1–4.

趙榮建, 周旺, 湯敏芳, 等. 四線(xiàn)渦輪式慢阻肺健康監(jiān)護(hù)系統(tǒng)研究[J]. 電子與信息學(xué)報(bào), 2019, 41(2): 469–476.

ZHAO Rongjian, ZHOU Wang, TANG Minfang, et al. Research of chronic obstructive pulmonary disease monitoring system based on four-line turbine-type[J]. Journal of Electronics &Information Technology, 2019, 41(2): 469–476.

文紅燕. 渦輪流量計(jì)在線(xiàn)監(jiān)測(cè)系統(tǒng)的研究[D]. [碩士論文], 中國(guó)計(jì)量大學(xué), 2016: 8–14.

WEN Hongyan. Research on the online monitoring system of turbine flow meter[D]. [Master dissertation], China Jiliang University, 2016: 8–14.

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪(fǎng)問(wèn)統(tǒng)計(jì)