利用樣式識別實現電子鼻肺炎偵測

Pattern Recognition Approaches for Pneumonia Detection by an Electronic Nose

王家銘(Chia-Ming Wang)

指導教授 ： 劉奕汶

國立清華大學/電機資訊學院/電機工程學系/碩士(2013年)

https://doi.org/10.6843/NTHU.2013.00793

摘要

加護病房中存在高抗藥性肺炎病菌的威脅，若病人感染肺炎，則需要診斷為何種肺炎菌種才能對症下藥，而目前普遍採取菌種培養的診斷方法，但此方法需要約五到六天才能得知結果，往往緩不濟急，病人就去世了。因此，想將具肺炎菌種辨認功能的電子鼻晶片安裝於人工呼吸器上，隨時偵測病人呼出的氣體中是否含有肺炎菌種氣體成分，以幫助醫生作早期診斷並對症下藥，把握治療的黃金時期。本論文研究過程中使用奈米複合材料陣列感測器，蒐集病人呼出的氣體訊號，使用樣式識別方法來分析資料。此論文的資料分析方面，使用KNN作為分類器，並且使用循序的特徵選擇方法挑出對肺炎辨識具有影響力的感測器以提高辨識率。實驗結果顯示，肺炎偵測使用特徵選擇方法，正確率從73%稍微上升至75%；而肺炎菌種辨認正確率則從66%明顯提高至73%。
論文中也提出基於構成成分作決策的混合氣體辨識方法(Individual Constituent-Decision Method, ICDM)，此方法不只將混合的涵義包含在內，且可以針對各構成成分的辨識作最佳化，將所有的構成成分決策結果綜合起來，即為混合氣體的辨識結果。由於本研究尚未有肺炎菌種氣體的主要構成成分資料，所以使用市售的混合果汁氣體資料來驗證此方法。此論文將ICDM和一次把混合後的所有組合作分類的方法比較，實驗結果發現ICDM有比較高的辨識率，並且各構成成分的辨識模型(最佳特徵子集合、分類器參數)皆有所不同，表示ICDM能針對各構成成分的辨識作最佳化。

關鍵字

電子鼻 ； 特徵選擇 ； 肺炎 ； 樣式識別

並列摘要

There is a serious threat of high-resistance pneumonia bacteria in the Intensive Care Unit. If a doctor finds out patients infected with pneumonia, he needs to determine which types of bacteria causes the trouble in order to prescribe the right antibiotic medicine. Up to now bacteria culture is the common way of diagnosis, but it takes five to six days to get the results. This is usually slow for emergency and some patients do not survive during the wait. Therefore, the idea of installing an electronic nose with pneumonia bacteria recognition function on artificial respiration is born. The electronic nose aims to detect whether the constituents of the gas exhaled by the patient imply bacterial infection in the lung. The monitoring can be done continually, helping physicians to perform early diagnosis and prescribe the right medicine, grasping the prime-time on saving the patient’s life.
Nano composite-array sensors are used here to get the signal from exhaled gas by the patients. Pattern recognition approaches were adopted to analyze the data; in this thesis, we choose the K nearest neighbor method (KNN) as our classifier and use sequential feature selection to obtain features that are most effective in discriminating between different types of pneumonia bacteria. The results show that the recognition rate of pneumonia detection increased slightly from 73% to 75% and the recognition rate of pneumonia bacteria recognition improved from 66% to 73%, thanks to the sequential feature selection.
This thesis also proposes a novel mixture gas recognition method, which we call the Individual Constituent-Decision Method (ICDM). The method utilizes the physical meaning of mixture, and it can be optimized separately to detect each constituent of interest. Results from all constituent-decision makers can be combined so as to produce a final result of mixture gas recognition. To validate ICDM in this thesis, because the constituting gases are unknown in the gas exhaled from pneumonia patients, we use fruit juice mixtures instead, to emulate the scenario of mixture-gas sensing. We compare ICDM with the traditional method that aims to classify all mixture combinations at one shot. Results show that ICDM has a better performance because it can find different recognition models (the best feature subset and parameters of the classifiers) for each individual constituent. This validates the idea that ICDM should be able to optimize on each individual constituent-decision.

並列關鍵字

Electronic Nose ； Feature Selection ； Pneumonia ； Pattern Recognition

參考文獻

[5] Y. Yin, “Discrimination between Chinese Jing Wine and Counterfeit Using Different Signal Features of an Electronic Nose,” Journal of Sensor Technology, vol. 02, no. 03, pp. 109–115, 2012.

[43] 許柏安, 快速混合氣體辨識方法之研究, 國立清華大學資訊工程研究所, 碩士論文, 2012.

[2] K. Persaud and G. Dodd, “Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose,” Nature, vol. 299, no. 5881, pp. 352–355, Sep. 1982.

[4] P. C. Jain and R. Kushwaha, “Wireless gas sensor network for detection and monitoring of harmful gases in utility areas and industries,” in 2012 Sixth International Conference on Sensing Technology (ICST), 2012, pp. 642–646.

[6] J. W. Gardner, H. V. Shurmer, and T. T. Tan, “Application of an electronic nose to the discrimination of coffees,” Sensors and Actuators B: Chemical, vol. 6, no. 1–3, pp. 71–75, Jan. 1992.