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系統識別號 U0026-1311201519153100
論文名稱(中文) 基於心律變異性與心電圖波形之睡眠呼吸中止事件偵測演算法開發
論文名稱(英文) Development of Obstructive Sleep Apnea Event Detection Algorithms Based on Heart Rate Variability and ECG Morphology Features
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 104
學期 1
出版年 104
研究生(中文) 高子平
研究生(英文) Tzu-Ping Kao
學號 N26964169
學位類別 碩士
語文別 英文
論文頁數 57頁
口試委員 指導教授-王振興
口試委員-徐崇堯
口試委員-楊延光
中文關鍵字 心電圖  心律變異性  阻塞型睡眠呼吸中止症 
英文關鍵字 Electrocardiography (ECG)  heart rate variability  obstructive sleep apnea (OSA) 
學科別分類
中文摘要 近年來,睡眠醫學已成為醫學界注目且強調的重點之一,本論文針對睡眠呼吸中止事件偵測發展兩套分析演算法。首先,基於心電圖訊號,提出一個基於心律變異性的阻塞型睡眠呼吸中止症偵測演算法,應用於阻塞型睡眠呼吸中止症的早期偵測及篩檢。此演算法萃取出HRV、EDR與CPC等等ECG參數,並利用AdaBoost Bootstrap k-dimension tree k-nearest neighbor (KDKNN) 演算法做呼吸中止事件偵測。透過此演算法,成功的區分了患者的阻塞型睡眠呼吸中止症事件。此外,本論文亦提出了基於波形特徵及與隨機森林演算法的阻塞型睡眠呼吸中止症辨識演算法。此演算法提出一套ECG波行偵測演算法,可用於偵測ECG中PQRST等波形的位置,並產生相對應的波形特徵參數,整合於以CART演算法為基礎的隨機森林演算法偵測呼吸中止事件。最後本論文成功的量測出此演算法的時間消耗,期待將來可將其應用至居家照護與阻塞型睡眠呼吸中止症的前期篩檢。
英文摘要 Sleep medicine has become a salient issue in health and medical industry in the past decade. This thesis proposes two electroencephalography (ECG) signal analysis algorithms for obstructive sleep apnea (OSA) detection. The first algorithm is an ECG feature-based AdaBoost Bootstrap k-dimension tree k-nearest neighbor algorithm for OSA events recognition. The proposed method processes single-lead ECG recordings to generate heart rate variability, ECG-derived respiratory signals, and cardiopulmonary coupling features for detecting the occurrence of sleep apnea, and then provides a minute-by-minute analysis of disordered breathing. The second algorithm is an ECG waveform detection method to locate the PQRST position of ECG signals. After generating the ECG morphological features from the PQRST position, a Classification and Regression Tree-based Random Forest algorithm was used to detect the OSA events. The effectiveness and time consumption of the algorithms have been successfully validated by experimental results. In the future, we hope these algorithms can be applied to home care and obstructive sleep apnea early screening.
論文目次 中 文 摘 要 I
Abstract II
Acknowledgment IV
Contents V
List of Tables VIII
List of Figures IX
Chapter 1. Introduction 1
1.1 Introduction and Motivation 1
1.2 Research Goals 2
1.3 Literature Review 4
1.4 Thesis Organization 5
Chapter 2. ECG Indices for OSA Detection and Their Clinical Interpretation 6
2.1. Heart Rate Variability 6
2.2. Relationship between ECG and OSA 7
2.2.1. The Impact of OSA on Heart Rate Variability 7
2.2.2. ECG Morphological Changes Caused by OSA 8
Chapter 3. Proposed HRV Feature-Based OSA Event Detection Algorithm 9
3.1. Algorithm Flow and Architecture 9
3.2. Feature Generation Process 11
3.3. Feature Selection and Transformation 16
3.4. OSA Event Recognition Using BA-KDKNN Classifier 17
Chapter 4. Obstructive Sleep Apnea Event Detection Algorithm using ECG Morphology Features and Random Forest Classifier 20
4.1. Algorithm Flow and Architecture 20
4.2. ECG Waveform Detection Process 21
4.3. Morphology-Feature Extraction Process 30
4.4. Feature Selection Process 32
4.5. Classification Process 33
Chapter 5. Experimental Results 34
5.1. Experimental Context 34
5.2. Results for HRV Feature-Based Obstructive Sleep Apnea Detection Algorithm 36
5.2.1. Parameter Determination for BA-KDKNN 36
5.2.2. Physionet Cross-Validation Results 38
5.2.3. Subject Independent System Result 39
5.3. Results for Obstructive Sleep Apnea Event Detection Algorithm using ECG Morphology Features and Random Forest Classifier 39
5.3.1. Sleep Apnea Detection Algorithm Based on ECG Morphology Features 39
5.3.2. Time Consumption of the Sleep Apnea Detection Mobile Application 42
Chapter 6. Discussion 44
6.1. HRV Feature-Based Obstructive Sleep Apnea Detection Algorithm 44
6.1.1. Performance comparison between BA-KDKNN and traditional KDKNN 44
6.1.2. Performance Comparison with other Classifiers 44
6.2. Obstructive Sleep Apnea Event Detection Algorithm using ECG Morphology Features and Random Forest Classifier 45
6.2.1. Number of CART Classifiers for Random Forests Classification 45
6.2.2. Appropriateness of Feature Selection Approaches 46
6.2.3. Computational Time Considerations 47
Chapter 7. Conclusions and Future Work 49
7.1. Conclusions 49
7.2. Future Work 50
References 52
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