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系統識別號 U0026-1807201315385700
論文名稱(中文) 基於心電訊號之自動情緒辨識演算法之開發
論文名稱(英文) Development of an Automatic ECG-based Emotion Classification Algorithm
校院名稱 成功大學
系所名稱(中) 電機工程學系碩博士班
系所名稱(英) Department of Electrical Engineering
學年度 101
學期 2
出版年 102
研究生(中文) 洪千涵
研究生(英文) Chien-Han Hung
學號 n26004189
學位類別 碩士
語文別 英文
論文頁數 85頁
口試委員 指導教授-王振興
口試委員-林梅鳳
口試委員-呂景民
中文關鍵字 即時R波偵測  心電訊號  自動情緒辨識演算法  音樂觸發 
英文關鍵字 real-time R-wave detection  ECG  automatic emotion classification algorithm  musical induction 
學科別分類
中文摘要 本論文主旨在於開發基於心電訊號形態學之即時R波偵測演算法與基於心電訊號之自動情緒辨識演算法於即時R波偵測及人類情緒辨識。首先,我們在無任何特殊考量的實驗設置中採用音樂觸發的方式來加以誘發受測者的真實情緒狀態並收集其心電訊號。接著,我們開發一即時R波偵測演算法即時地利用心電訊號形態學特徵值來加以偵測受測者心電訊號中的R波。此外,本論文亦提出一基於心電訊號之自動情緒辨識演算法來加以辨識受測者被音樂所觸發的情緒。在此演算法中,我們首先利用經由時域、頻域及非線性分析所產生的心電訊號特徵找出與各情緒狀態較相關的特徵及建立其關係性。然後,我們開發一基於循序前進浮動搜尋為搜尋策略與基於核函數類別分辨率為選取門檻之特徵選取演算法來加以選取較為顯著的心電訊號特徵;並利用廣義鑑別分析來加以降低特徵維度。最後,我們利用最小平方支持向量機分類器來進行情緒正負向、激昂度之高低程度與四類情緒(喜悅、緊張、悲傷及平靜)的分類任務。實驗結果顯示,在R波偵測方面可達到99.97%之敏感度、99.89%之陽性預測率與0.14%之錯誤率;而在情緒正負向、激昂度之高低程度與四類情緒之情緒辨識則分別得到82.78%、72.91%與61.52%之正確辨識率。
英文摘要 This thesis presents a real-time ECG morphology feature based R-wave detection algorithm and an automatic ECG-based emotion classification algorithm for R-wave detection and human emotion classification, respectively. At first, we adopt a musical induction method to collect participants’ ECG signals without any deliberate laboratory setting, which can induce participants’ real emotional states. Next, the proposed real-time R-wave detection algorithm is presented to detect R-waves in ECG signal based on the ECG morphological features. Afterward, we develop an automatic ECG-based emotion classification algorithm to classify human emotions elicited by listening to music. Physiological ECG features generated from time-, frequency-domain, and nonlinear analyses are utilized to find the emotion-relevant features and to correlate them with emotional states. Subsequently, we develop a sequential forward floating selection-kernel-based class separability based (SFFS-KBCS-based) feature selection algorithm and utilize the generalized discriminant analysis (GDA) to effectively select significant ECG features associated with emotions and to reduce the dimensions of the selected features, respectively. Classifications of positive/negative valence, high/low arousal, and four types of emotion (Joy, Tension, Sadness, and Peacefulness) are performed by least squares support vector machine (LS-SVM) classifiers. The results show that the sensitivity, positive predictive value, and detection error rate of the real-time R-wave detection algorithm can achieve 99.97%, 99.89%, and 0.14%, respectively, and the average delay time of the proposed algorithm is only 15.1ms. Furthermore, the correct classification rates of the positive/negative valence, high/low arousal, and four types of emotion classification tasks are 82.78%, 72.91%, and 61.52%, respectively.
Keywords: real-time R-wave detection, ECG, automatic emotion classification algorithm, musical induction.
論文目次 CHINESE ABSTRACT i
ABSTRACT ii
ACKNOWLEDGEMENT iv
TABLES OF CONTENTS v
LIST OF TABLES vii
LIST OF FIGURES ix
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Literature Survey 3
1.2.1 Dimensional Emotion Models 3
1.2.2 ECG Signal and Emotion 4
1.2.3 Music and Emotion 5
1.2.4 Approaches to Emotion Classification Using Biosignals 6
1.3 Purpose of the Study 7
1.4 Organization of the Thesis 8
Chapter 2 A Real-time ECG Morphology Feature Based R-wave Detection Algorithm 9
2.1 Introduction 10
2.2 ECG Morphology Feature Parameter Initialization 12
2.3 QR-wave Candidate Selection 13
2.4 Feature Generation 14
2.5 R-wave Detection 15
2.6 Dynamic Threshold Update Process 17
Chapter 3 Automatic ECG-based Emotion Classification Algorithm 18
3.1 Signal Preprocessing 20
3.1.1 Baseline Wander Removal 21
3.1.2 Z-score Normalization Method 22
3.2 R-wave Detection 23
3.3 Windowing 24
3.4 Incorrect Epoch Rejection 24
3.5 Feature Generation 25
3.5.1 Time-domain Analysis 25
3.5.2 Frequency-domain Analysis 27
3.5.3 Nonlinear Analysis 30
3.6 Feature Normalization 39
3.7 Feature Selection 39
3.8 Feature Extraction 44
3.8.1 Principal Component Analysis 44
3.8.2 Linear Discriminant Analysis 45
3.8.3 Generalized Discriminant Analysis 47
3.9 Classifier Construction 50
Chapter 4 Experimental Setup and Results 53
4.1 Experimental Setup 53
4.1.1 Materials and Setup 53
4.1.2 Experimental Protocol 54
4.1.3 Participant Self-assessment 56
4.2 Results of the Real-time R-wave Detection Algorithm 57
4.3 Results of the Automatic ECG-based Emotion Classification Algorithm 59
4.3.1 Positive/Negative Valence Classification 61
4.3.2 High/Low Arousal Classification 67
4.4 Discussions 72
4.4.1 Comparison of the Proposed Method with Other Existing Approach for the MAHNOB-HCI Database 72
4.4.2 Comparison of the Proposed Method with Other Existing Approaches Using Biosignals 73
Chapter 5 Conclusions and Future Work 75
5.1 Conclusions 75
5.2 Future Work 77
References 79
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