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研究生:李臺育
研究生(外文):Tai-yu Li
論文名稱:利用線性與非線性參數分類糖尿病自律神經病變之嚴重程度
論文名稱(外文):Classification of Severity in Diabetic Autonomic Neuropathy Using Linear and Nonlinear Parameters
指導教授:邱創乾邱創乾引用關係
指導教授(外文):Chuang-Chien Chiu
學位類別:碩士
校院名稱:逢甲大學
系所名稱:生醫資訊暨生醫工程碩士學位學程
學門:工程學門
學類:生醫工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:57
中文關鍵詞:非線性分析糖尿病自律神經病變線性分析支援向量機
外文關鍵詞:support vector machine.linear analysisnonlinear analysisautonomic neuropathyDiabetic
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近十年以來糖尿病高居國人十大死因不下,成為現代社會中最常見的慢性病之一。神經病變為糖尿病患者最常見之慢性併發症之一,且與罹患糖尿病的時間長短有關,然而糖尿病神經病變的死亡率與是否罹患自律神經病變有關,有鑒於此,若能將糖尿病自律神經病變患者之嚴重程度加以區分,則可以預防與延緩糖尿病神經病變的併發與加重。由於自律神經檢測相當的繁瑣且診斷時間較長,因此本研究主要目的為結合線性與非線性分析來萃取平均血壓與平均血流速之特徵參數,希望藉由這些特徵參數能簡易且快速有效的辨識患者是否罹患自律神經病變與其嚴重程度,本研究利用支援向量機做為分類工具,分類72例資料:14例正常人、15例糖尿病無神經病變、25例糖尿病輕度神經病變及18例糖尿病重度神經病變,並將其生理訊號平均血壓與平均血流速分為極低頻、低頻及高頻三個頻帶,經由交越相關函數分析取其最大峰值與相對應之標準差與位置,共九個線性參數做為特徵參數,並藉由交叉比對測試,其辨識結果為70.8%,進而與經由交越近似熵分析後之非線性參數共十個參數做為特徵參數,同樣利用交叉比對測試則辨識率提高至73.6%,其結果表示本研究的方法能快速且有效的分類不同的神經病變嚴重程度,並顯示了結合線性與非線性的特徵參數有助於提升辨識效果及有助於無神經病變的分類。
Diabetes has been one of the top ten death results in Taiwan in recent ten years, and it becomes one of the most common chronic diseases in modern society. Neuropathy is one of the most common chronic complication of diabetes, and it is related to the time of suffering from diabetes. For this reason, if we can distinguish between the severity of diabetic autonomic neuropathy, then we will retard the disease increased. In the past, the inspection of autonomic nervous system was quite cumbersome and required a longer time. Therefore, the aim of this study is to extract the parameters from arterial blood pressure and mean cerebral blood flow which are, expected to be helpful to distinguish the severity of autonomic neuropathy. In this study, the support vector machine is used to classify 14 cases of normal subjects and 58 patients with diabetes in which 15 patients with out neuropathy, 25 patients with mild neuropathy, and 18 patients with severe neuropathy. The mean arterial pressure and mean cerebral blood flow are first filtered into very low frequency, low frequency, and high frequency bands. The cross-correlation function of the maximum peak and the corresponding standard deviation and index are calculated. A total of nine parameters is applied to be the linear feature parameters. The classification result of the leave-one-out is 70.8%. The classification combined with the nonlinear feature parameter is 73.6%. These results indicate that the approach of this study can be helpful to improve the recognition results and can be used to classify the severity in diabetic autonomic neuropathy.
摘要 ii
Abstract iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 前言 1
1.2 研究動機 1
1.3 研究目的 2
1.4 文獻回顧 3
第二章 研究背景 5
2.1 糖尿病 5
2.2 糖尿病神經病變 6
2.3 自律神經檢測 8
2.4 腦血流調控機制 11
第三章 材料與方法 13
3.1 受測者 13
3.1.1 對照組 13
3.1.2 實驗組 13
3.2 檢測流程和分析資料 14
3.3 資料擷取之系統架構 14
3.4 數據分析 15
3.5 線性分析方法 15
3.5.1 平均值與標準差 16
3.5.2 交越相關函數 16
3.6 非線性分析方法 17
3.6.1 混沌理論 17
3.6.2 重構方法 17
3.6.3 關聯維 18
3.6.4 Kolmogorov熵 19
3.6.5 獨立複雜性[35] 20
3.6.6 獨立可預測性[35] 20
3.6.7 交越近似熵 20
3.7 支援向量機理論 21
3.7.1 支援向量機之概念 22
3.7.2 線性支援向量機 22
3.7.3 非線性SVM問題 26
3.7.4 資料不可分隔SVM問題 27
第四章 結果與討論 30
4.1 線性分析結果 30
4.1.1 峰值分析結果 30
4.1.2 峰值相對應之標準差分析結果 32
4.1.3 峰值相對應之位置分析結果 33
4.2 非線性分析結果 34
4.2.1 關聯維分析結果 34
4.2.2 K2分析結果 35
4.2.3 獨立複雜性 36
4.2.4 獨立可預測性 37
4.2.5 交越近似熵 38
4.3 分類結果 40
第五章 結論及未來展望 43
參考文獻 44
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