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研究生:曾信輝
研究生(外文):Hsin-Hui Tseng
論文名稱:使用心率變異性分析及特徵選取於駕駛狀態及帕金森氏症病程之辨識
論文名稱(外文):Using HRV Analysis and Feature Selection for Recognizing Driving Conditions and Parkinson’s Disease Severity
指導教授:王振興王振興引用關係
指導教授(外文):Jeen-Shing Wang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:91
中文關鍵詞:帕金森氏症駕駛狀態特徵選取心率變異性
外文關鍵詞:driving conditionsfeature selectionParkinson's diseaseHRV
相關次數:
  • 被引用被引用:1
  • 點閱點閱:156
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:2
本論文的目標旨在透過心率變異性參數的分析進行生理狀態之辨識,因此本論文研究主題涵蓋以下兩大部分:1) 心率變異性趨勢圖的呈現與參數的計算及2) 基於心率變異性之分析與特徵擷取進行辨識。本論文首先提出了一心率變異性趨勢圖呈現與參數計算的方法,並將其實現於使用者圖形介面 (Graphical user interface)。使用者透過此圖形介面可讀取多重生理信號、選擇需要顯示的生理信號、輸入一段特定的時間區段來進行心率變異性趨勢圖的呈現與參數的分析與計算,並將其顯示於使用者圖形介面。此外,本論文亦提出基於心率變異性趨勢圖的辨識策略與基於心率變異性參數的辨識策略,此兩種辨識策略的流程如下: 1) 特徵值產生 2) 特徵值選取 3) 特徵值擷取 4)辨識器之選用。基於心率變異性趨勢圖的辨識策略與基於心率變異性參數的辨識策略最大的不同之處在於特徵值產生的流程。基於心率變異性趨勢圖的辨識策略的特徵產生流程是自心率變異性的趨勢圖中計算具有統計意義的特徵值,而基於心率變異性參數的辨識策略的特徵產生流程則是對五分鐘心率變異性的分析結果計算特徵值。兩種辨識策略在特徵選取流程內皆採用了 best individual N 當作特徵選取的搜尋策略,並且使用kernel-based class separability當作特徵選取的選取門檻。此兩種辨識策略皆採用主成分分析(principal component analysis)與線性鑑別分析(linear discriminant analysis)做為特徵擷取之方法。最後,皆採用k最近鄰居法(k-nearest neighbor algorithm)作為辨識器。
本論文利用了所提出的兩種辨識策略於兩個不同的實際應用上: 1) 駕駛狀態之辨識 2) 帕金森氏症病程之辨識。透過此兩個實際應用的實驗結果,所提出的辨識策略皆可以呈現出令人滿意的辨識率。此外,本論文也與不同的辨識方法作了比較,結果也驗證了本論文所提出的辨識策略之有效性。
This study presents a physiological recognition approach based on HRV trends. The focuses of this study include: 1) the development of a user-friendly interface for generating the trends of each HRV parameter and 2) the development of recognition strategies based on the HRV trends. The proposed user-friendly interface enables users to load polysomnography (PSG) signals, to select which channels to display, to specify a time interval, to execute a long-term HRV analysis program, and to inspect the trends of each HRV parameter. The developed recognition strategies include a HRV-trend-based recognition strategy and a HRV-parameter-based recognition strategy. Both strategies consist of the following processes: 1) feature generation, 2) feature selection, 3) feature extraction, and 4) classifier construction for recognition. In the feature generation processes, the trend-based strategy computes statistical features from HRV trends, while the parameter-based strategy calculates features from five-minute HRV analysis results. In the feature selection process, both strategies adopt the best individual N (BIN) as the search strategy and the kernel-based class separability (KBCS) as the selection criterion. Sequentially, principal component analysis (PCA) and linear discriminant analysis (LDA) are adopted in the feature extraction process. Finally, a k-nearest neighbor (k-NN) algorithm is used for the recognition.
The feasibility of these two recognition strategies is verified by two applications: 1) driving condition recognition and 2) severity recognition of Parkinson’s disease. The simulation results demonstrate that both proposed strategies can achieve satisfactory recognition rates in these two applications. In addition, this study compares the average recognition rates of the recognition methods with different feature extraction processes. The results show that the feature extraction process or feature selection process has respective physical meaning in the proposed strategies.
CHINESE ABSTRACT i
ABSTRACT iii
TABLES OF CONTENTS v
LIST OF TABLES vii
LIST OF FIGURES x
1 Introduction 1-1
1.1 Motivation 1-1
1.2 Literature Survey 1-2
1.3 Purpose of the Study 1-7
1.4 Organization of the Thesis 1-9
2 A Polysomnography-based HRV Analysis Software 2-1
2.1 Introduction of Heart Rate Variability 2-2
2.2 HRV Analyzer based on EDF File Viewer 2-7
2.3 R Wave Labeling Tool 2-11
2.4 HRV Results Viewer 2-13
2.5 Long-term HRV Observation Tool 2-14
3 Using HRV Indexes for Driving Conditions and Parkinson’s Disease Severity Recognition 3-1
3.1 Introduction 3-1
3.2 Application I: HRV-Trend-based and HRV-Parameter-based Recognition Strategies for Recognizing Driving Conditions 3-3
3.3 Application II: HRV-Trend-based Recognition Strategy for Recognizing Severity of Parkinson’s Disease 3-5
3.4 Feature Generation 3-8
3.4.1 Feature Generation for HRV-Trend-based Recognition Strategy 3-8
3.4.2 Feature Generation for HRV-Parameter-based Recognition Strategy 3-11
3.5 Feature Selection Methods 3-12
3.6 Feature Extraction Methods 3-14
3.6.1 Principal Component Analysis 3-15
3.6.2 Linear Discriminant Analysis 3-16
3.7 Classifier for Recognition 3-18
4 Results and Discussion 4-1
4.1 Results and Discussion for Application I 4-1
4.1.1 Results and Discussion of HRV-Trend-based Recognition Strategy for Application I 4-2
4.1.2 Results and Discussion of HRV-Parameter-based Recognition Strategy for Application I 4-4
4.2 Results and Discussion for Application II 4-7
4.2.1 Results and Discussion of HRV-Trend-based Recognition Strategy for Application II 4-7
4.2.2 Results and Discussion of Decision-Tree-based Method for Application II 4-9
5 Conclusions and Future Work 5-1
5.1 Conclusions 5-1
5.2 Future Work 5-2
References 6-1
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