面向诊断的口腔气味检测系统设计与分析
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摘要
中医是中华民族的瑰宝。然而由于历史条件的制约,传统中医理论往往带有很强的主观性,其诊断手段也缺乏客观化诊断标准,故被许多人认为是一门经验科学,其科学性也屡遭质疑,严重阻碍了中医学的发展。如今,中医学的发展正强烈的呼唤着现代科学技术新方法、新手段的支持。如何在保持中医诊断自身优势基础上,通过现代技术手段改革与创新传统中医诊断学,实现中医诊断的客观化、标准化,将其纳入到现代科学技术发展的轨道是国人亟待解决的课题。
电子鼻技术是当前计算机一个非常重要的研究方向。它的基本思想是利用模式识别方法对气敏传感器获取的气味数据进行分析。计算机技术的发展及电子鼻技术的成熟,使以电子鼻技术指导中医闻诊成为可能。运用电子鼻技术对传统闻诊信息进行研究,把闻诊研究建立在可靠的数学模型基础上,使其更准确、更客观地反映人体机能状态,从而提高中医理论学术水平和临床诊断能力,是闻诊研究领域发展提高的新课题。本项目在电子鼻原理的基础上,利用传感器技术、信号处理技术、模式识别技术,并结合中医闻诊的相关理论,开发出高精度、非侵入式、无伤害、快速准确的面向中医诊断的口腔气味检测系统(简称STCM),研究基于人体口腔气味检测与分析的中医诊断技术,以推动中医闻诊的客观化、标准化进程,具有巨大的社会意义和经济意义。
为了满足闻诊诊断的要求,必须寻找能够检测口腔气味病理成分并具有足够敏感度的传感器。本文利用7个满足要求的气敏传感器组成传感器阵列;采用合适的信号调理电路和ADC、运放、CY7C68013单片机及USB模块等器件组成STCM硬件系统;用C51语言编写了单片机部分的控制软件;用Visual C++ 6.0编写了PC机部分STCM系统的应用软件,可实现实验的参数设置、实时显示、数据处理等功能。算法方面,分别在数据预处理、特征抽取和降维、BP神经网络识别方面进行了论述,并针对BP算法存在的不足,利用遗传算法对BP算法进行优化,从而建立针对病理气味数据分析的模型。
Traditional Chinese Medical (TCM) is the huge treasure of the Chinese nation. But owing to the constraints of historical conditions, TCM theory strongly depends on subjectivity, and its diagnosis methods are short of objective criterion. Therefore it is thought to be empirical and its scientificalness is often questioned, which hinders the development of TCM seriously. Nowadays the development of TCM science is strongly calling for supports coming from new methods and new means of modern science and technology. It is a pressing problem to objectify and standardized TCM diagnosis and put it into the development of modern science and technology through innovating methods of modern technology and TCM diagnosis, maintaining the advantages of TCM diagnosis.
At present, electronic nose technology (Enose) is one of the important researches in computer science and technology. Its basic idea is to use pattern recognition approaches to analyze the data gathered by gas sensors. The developments of computer and Enose technology provide a prerequisite for its steering breath diagnosis. When you do research on information of traditional smelling diagnosis by using Enose technology and mathematical module, it will reflect the state of physical function of human body and consequently improve both the academic standards theory and clinical diagnostic capacity of TCM. It is a new task in the field of developing and improving smelling diagnosis. This project, based on Enose theory, using sensor technology, signal processing technology, and pattern recognition technology, combining the correlated theory of TCM breath diagnosis, is aiming at developing high-precision, non-invasive, non-injury, fast and accurate breath odor detection system (STCM). It also plan to do research on TCM diagnosis technology based on breath odor detection and analysis, and accordingly promotes the objective and standardizing process TCM breath diagnosis. This also has great social and economic significance.
Taking into account the special nature of smell, sensors, which are able to detect of oral pathology component and sensitive enough, are needed. An array of chemical sensors has been formed with seven gas sensors. Hardware of STCM system etc consists of preprocessing circuit, ADC, amplifier, CY7C68013 and USB etc. Software compiled with C51 in single chip, and software compiled with Visual C++ 6.0 in PC has the capability of experiment control, real-time display, and data processing, etc. In algorithm, areas of Data pre-processing, Feature extraction, BP neural network are discussed. BP algorithm is optimized by genetic algorithm, which greatly avoided local minimum of energy function by BP algorithm. At the final we established the analysis model of pathological odor data. .
电子鼻技术是当前计算机一个非常重要的研究方向。它的基本思想是利用模式识别方法对气敏传感器获取的气味数据进行分析。计算机技术的发展及电子鼻技术的成熟,使以电子鼻技术指导中医闻诊成为可能。运用电子鼻技术对传统闻诊信息进行研究,把闻诊研究建立在可靠的数学模型基础上,使其更准确、更客观地反映人体机能状态,从而提高中医理论学术水平和临床诊断能力,是闻诊研究领域发展提高的新课题。本项目在电子鼻原理的基础上,利用传感器技术、信号处理技术、模式识别技术,并结合中医闻诊的相关理论,开发出高精度、非侵入式、无伤害、快速准确的面向中医诊断的口腔气味检测系统(简称STCM),研究基于人体口腔气味检测与分析的中医诊断技术,以推动中医闻诊的客观化、标准化进程,具有巨大的社会意义和经济意义。
为了满足闻诊诊断的要求,必须寻找能够检测口腔气味病理成分并具有足够敏感度的传感器。本文利用7个满足要求的气敏传感器组成传感器阵列;采用合适的信号调理电路和ADC、运放、CY7C68013单片机及USB模块等器件组成STCM硬件系统;用C51语言编写了单片机部分的控制软件;用Visual C++ 6.0编写了PC机部分STCM系统的应用软件,可实现实验的参数设置、实时显示、数据处理等功能。算法方面,分别在数据预处理、特征抽取和降维、BP神经网络识别方面进行了论述,并针对BP算法存在的不足,利用遗传算法对BP算法进行优化,从而建立针对病理气味数据分析的模型。
Traditional Chinese Medical (TCM) is the huge treasure of the Chinese nation. But owing to the constraints of historical conditions, TCM theory strongly depends on subjectivity, and its diagnosis methods are short of objective criterion. Therefore it is thought to be empirical and its scientificalness is often questioned, which hinders the development of TCM seriously. Nowadays the development of TCM science is strongly calling for supports coming from new methods and new means of modern science and technology. It is a pressing problem to objectify and standardized TCM diagnosis and put it into the development of modern science and technology through innovating methods of modern technology and TCM diagnosis, maintaining the advantages of TCM diagnosis.
At present, electronic nose technology (Enose) is one of the important researches in computer science and technology. Its basic idea is to use pattern recognition approaches to analyze the data gathered by gas sensors. The developments of computer and Enose technology provide a prerequisite for its steering breath diagnosis. When you do research on information of traditional smelling diagnosis by using Enose technology and mathematical module, it will reflect the state of physical function of human body and consequently improve both the academic standards theory and clinical diagnostic capacity of TCM. It is a new task in the field of developing and improving smelling diagnosis. This project, based on Enose theory, using sensor technology, signal processing technology, and pattern recognition technology, combining the correlated theory of TCM breath diagnosis, is aiming at developing high-precision, non-invasive, non-injury, fast and accurate breath odor detection system (STCM). It also plan to do research on TCM diagnosis technology based on breath odor detection and analysis, and accordingly promotes the objective and standardizing process TCM breath diagnosis. This also has great social and economic significance.
Taking into account the special nature of smell, sensors, which are able to detect of oral pathology component and sensitive enough, are needed. An array of chemical sensors has been formed with seven gas sensors. Hardware of STCM system etc consists of preprocessing circuit, ADC, amplifier, CY7C68013 and USB etc. Software compiled with C51 in single chip, and software compiled with Visual C++ 6.0 in PC has the capability of experiment control, real-time display, and data processing, etc. In algorithm, areas of Data pre-processing, Feature extraction, BP neural network are discussed. BP algorithm is optimized by genetic algorithm, which greatly avoided local minimum of energy function by BP algorithm. At the final we established the analysis model of pathological odor data. .
引文
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[2] Daniel Cozzolino, et al. Usefulness of chemometrics and mass spectrometry-based electronic nose to classify Australian white wines by their varietal origin [J]. Talanta. 2005, 68:382-387.
[3] J. Lozano, et al. Classification of white wine aromas with an electronic nose [J]. Talanta. 2005, 67:610-616.
[4] M. Garcia, et al. Differentiation of red wines using an electronic nose based on surface acoustic wave devices [J]. Talanta. 2006, 68:1162-1165.
[5] S. Panigrahi, et al. Neural-network-integrated electronic nose system for identification of spoiled beef [J]. LWT. 2006, 39:135-145.
[6] M. Garcia, et al. Electronic nose for ham discrimination [J].Sensors and Actuators B 2006, 114:418-422.
[7] H.L. Gan, et al. Characterisation of vegetable oils by surface acoustic wave sensing electronic nose [J]. Food Chemistry. 2005, 89:507-518.
[8] M.S. Cosio, et al. Evaluation of different storage conditions of extra virgin olive oils with an innovative recognition tool built by means of electronic nose and electronic tongue. Food Chemistry, 2006, 3:1-7.
[9] Lakshmi P. Pathange, et al. Non-destructive evaluation of apple maturity using an electronic nose system. Journal of Food Engineering, 2006, 7(4):1018-1023.
[10] Antihus Hernandez Gomez, et al. Electronic nose technique potential monitoring mandarin maturity [J]. Sensors and Actuators B, 2006, 113: 347-353.
[11] Margaret Amy Ryan, et al. Monitoring space shuttle air quality using the Jet Propulsion Laboratory electronic nose [J]. IEEE Sensors Journal, 2004, 4(3):337-347.
[12] O. Canhoto, N. Magan. Electronic nose technology for the detection of microbial and chemical contamination of potable water [J]. Sensors and Actuators B. 2005, 106:3-6.
[13] J. Goschnick, et al. Water pollution recognition with the electronic noseKAMINA [J]. Sensors and Actuators B, 2005, 106:182-180.
[14] A. Catarina Bastos, Naresh Magan. Potential of an electronic nose for the early detection and differentiation between Streptomyces in potable water [J]. Sensors and Actuators B, 2006, 116:151-155.
[15] Emsley J, Crystal nose sniffs out nasty diseases [J], New scientist, 1995, 11(3): 21-22.
[16] Yuh-Jiuan Lin, et al. Application of the electronic nose for uremia diagnosis [J], Sensors and Actuators B, 2001, 76:177-180.
[17] Alexandros K. Pavlou, et al. Use of an electronic nose system for diagnoses of urinary tract infections [J], Biosensors and Bioelectronics, 2002, 17:893-899.
[18] Reinhard Fend, et al. Monitoring haemodialysis using electronic nose and chemometrics [J], Biosensors and Bioelectronics, 2004, 119:1581-1590.
[19]李冀.姚哲刚.王平.呼吸气体诊断系统的最新进展[J].中国医疗器械杂志. 2000.6
[20] Wang Ping, Tan Yi, et al. A novel method for diabetes diagnosis based on electronic nose [J], Biosensors & Bioelectronics, 1997, 12:1031-1036.
[21] Nakisimovich N, et al. Adsorption semiconductor sensor for diabetic ketoacidosis diagnosis [J] Sensors and Actuators B, 1996, 35-36:419-421.
[22] S.V. Ryabtsev, et al. Application of semiconductor gas sensors for medical diagnostics [J], Sensors and Actuators B, 1999, 59:26-29.
[23] Corrado Di Natale, et al. Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors [J]. Biosensors and Bioelectronics, 2003, 18:1209-1218.
[24] http://hbrb.hebeidaily.com.cn/20030518/ca274633.htm
[25] Alexandros K. Pavlou. Naresh Magana, et al. Detection of Mycobacterium tuberculosis (TB) in vitro and in situ using an electronic nose in combination with a neural network system [J]. Biosensors and Bioelectronics. 2004, 20: 538-544.
[26] J.W. Gardner, et al. The prediction of bacteria type and culture growth phase by an electronic nose with a multi-layer perceptron network [J], Meas. Sci. Technol. 1997, 14(9):120-127.
[27] Julian W. Gardner, et al. An electronic nose system to diagnose illness [J]. Sensors and Actuators B, 2000, 70:19-24.
[28] http://tech.enorth.com.cn/system/2003/06/28/000587627.shtml.
[29] Dutta, R. et al. ENT bacteria classification using a neural network based Cyranose 320 electronic nose. Proceedings of IEEE Sensors. 2004. 324-325.
[30] http://www.cycnet.com/encyclopedia/exploration/events/000712018.htm.
[31] Anna Aronzon, et al. Differentiation Between Cerebrospinal Fluid and Serum with Electronic Nose [J]. Otolaryngology - Head and Neck Surgery, 2005, 133(1): 16-19.
[32] Pardo M, Sberveglieri G, Sisk B, Lewis N, Classifier comparison and sensor selection fore-noses, Sensors. 2003, 24(4): 32-36.
[33]郭兰静,卢红霞等,一种提高电阻式传感器灵敏度的双传感器电路,传感器技术. 2004.1
[34]张良谊,温丽箐等,用于测定空气中甲醛的电子鼻,高等学校化学学报. 2003.8
[35]高旭昇,王平,电子鼻信号处理方法的研究进展,国外医学生物医学工程分册. 2001.1
[36]周建辉,王磊,姚成虎,模糊神经网络在电子鼻系统定量识别中的应用,计算机测量与控制. 2005.2
[37]殷勇,嗅觉模拟技术,化学工业出版社. 2005:122-131
[38]王平,陈裕泉等,人工嗅觉―电子鼻的研究,中国生物医学工程学报. 1996.4
[39] Wang Ping, Xie Jun, A novel recognition method for electronic nose using artificial neural network and fuzzy recognition, Sensors and Actuators, 1996. B(37):169-174
[40] Patrick Mielle, Florence Marquis, An alternative way to improve the sensitivity of electronic Sensors and Actuators. 1999:343-347
[41] R. Martin Negri,S. Reich. Identification of Pollutant gases and its concentrations with a multi-sensor array.2001. 75(3):172-178.
[42] Martin Holmberg, Fredrik Winquist, I. Lundstrom et al. Dritf counteraction for an electronic nose. Sensors and Acruators. 1996, 36(l-3):528-535.
[43] Gardner J. W, Bartlett P. N, Electronic noses principles and applications, Oxford University Press Inc, New York. 1999, 22(8):154-159
[44] Harrevald, A. P, P. Heeres and H. Harssema, A review of 20 years of standardization of odor concentrations measurement by dynamic olfactometry in Europe,Air and Waste Manage.1999:29-33
[45] Bartlett P. N, Eliott J.M etc,Electronic nose and their application in the food industry, Food Technology. 1997, 11(23):107-110
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