利用心率變異度進行充血性心臟衰竭之短時間與長時間分析

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研究生：	張庭瑜 Chang, Ting Yu
論文名稱：	利用心率變異度進行充血性心臟衰竭之短時間與長時間分析 A Short- and Long-Term Analysis of Congestive Heart Failure Based on Heart Rate Variability
指導教授：	馬席彬 Ma, Hsi Pin
口試委員:	蔡佩芸 Tsai, Pei Yun 楊家驤 Yang, Chia Hsiang 黃元豪 Huang, Yuan Hao 林澂 Lin, Chen
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2016
畢業學年度：	105
語文別：	英文
論文頁數：	80
中文關鍵詞：	充血性心臟衰竭、心律變異度
外文關鍵詞：	CHF, HRV
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本篇論文提出了一個基於心率變異性的充血性心臟衰竭辨識系統。其中，我們著重於短時間與長時間的分析與探討，因此在長時間分析的部分我們所使用的資料長度為四小時；而短時間分析使用的資料長度為五分鐘。所有的心電圖紀錄都是來自於國立台灣大學醫學院的二十四小時臨床實驗。

我們的辨識系統主要可以分成三個部分：特徵擷取、特徵選取、以及分類。為了萃取出適當的特徵，我們使用了不只是基本的線性特徵，還有一些非線性的方法像是多尺度熵分析、去趨勢波動分析，皆運用於我們的論文當中。為了更進一步地找出特徵與疾病間的關聯，我們使用了統計方法來驗證每一個特徵在充血性心臟衰竭與對照組間是否有顯著差異。接著在排除一些對於辨識沒有充分有效的特徵後，我們使用了前饋式特徵選取方法分別為短期與長期的分析選擇出最佳的特徵組合。最後會運用支持向量機的原理來尋找最好的支持超平面，獨立出一個模糊區塊，並將其設為第三類別再進一步做分類辨識。

在我們的論文中也討論了短期與長期分析的比較，另外根據留一法(leave-one-out method)來驗證此辨識系統的效能。最後得到長時間分析的辨識率高達100%，而短時間分析的正確率為95.83%。

In this thesis, a recognition system of congestive heart failure (CHF) based on heart rate variability is proposed. Therein, we attach great importance to the discussion of short- and long-term analysis. For the long-term analysis, we accessed 4-hour data; for the short-term analysis, we retrieved 5-minute data. Furthermore, all the ECG data is derived from the 24-hour recording of the clinical trial in National Taiwan University Hospital.

This system is primarily divided into three parts: feature extraction, feature selection, and classification. To extract appropriate features, not only the basic linear features but also the non-linear methods such as the multiscale entropy, and detrended fluctuation analysis, are applied in our research. To further find out the correlation between the feature and disease, we employed the statistical method to verify whether there is a significant difference of features between CHF and Control or not. After excluding the features that are not sufficiently effective, we chose the sequential forward selection (SFS) to search for the best feature set for the short- and long-term analysis respectively. Afterwards, the support vector machine (SVM) is used to look for the best support hyperplanes to hive off the unclear group and then classify with the three groups.

The comparisons of short- and long-term analysis are also discussed in this thesis. According to the leave-one-out method, we verified the performance of this recognition system. Finally, the recognition accuracy for long-term analysis is up to 100% and for short-term analysis attained 95.83%.

Abstract i

Introduction 1
1 Backgrounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
3 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
4 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Electrocardiography and Congestive Heart Failure 5
1 Overview of ECG Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1 Electrophysiology . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Standard 12-Lead ECG . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 ECG Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Congestive Heart Failure (CHF) . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1 Cardiac Arrhythmia . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Congestive Heart Failure . . . . . . . . . . . . . . . . . . . . . . . . 17
3 Clinical Database of CHF and Control . . . . . . . . . . . . . . . . . . . . . 18
3.1 Source of the Clinical Data . . . . . . . . . . . . . . . . . . . . . . . 18
3.2 Information of the Database . . . . . . . . . . . . . . . . . . . . . . 19
4 Overview of Biomedical Signal Analysis for CHF . . . . . . . . . . . . . . . 19
4.1 Feature Selector Based on SVM . . . . . . . . . . . . . . . . . . . . 20
4.2 Classiﬁcation and Regression Tree . . . . . . . . . . . . . . . . . . . 20
4.3 Short-Term HRV Measures . . . . . . . . . . . . . . . . . . . . . . . 22
4.4 Statistical Methods with Short RR Interval Time Series . . . . . . . . 23
4.5 Comparisons of Studies . . . . . . . . . . . . . . . . . . . . . . . . 24

Heart Rate Variability and Feature Extraction 27
1 Heart Rate Variability (HRV) . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2 Linear Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.1 Time-Domain Features . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2 Frequency-Domain Features . . . . . . . . . . . . . . . . . . . . . . 30
3 Non-Linear Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.1 Multiscale Entropy (MSE) . . . . . . . . . . . . . . . . . . . . . . . 30
3.2 Detrended Fluctuation Analysis (DFA) . . . . . . . . . . . . . . . . 33

Proposed Methodology of Recognizing CHF 35
1 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
1.1 Prerequisite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
1.2 RR Interval Extraction . . . . . . . . . . . . . . . . . . . . . . . . . 37
1.3 Short- and Long-term Data . . . . . . . . . . . . . . . . . . . . . . . 40
2 Feature Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.1 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.2 Feature Normalization . . . . . . . . . . . . . . . . . . . . . . . . . 43
3 Curve of Multiscale Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.1 Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2 Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3 Atrial Fibrillation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.4 PAC & PVC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.5 4-hour vs. 5-minute . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.1 Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2 Sequential Forward Selection (SFS) . . . . . . . . . . . . . . . . . . 53
5 Classiﬁcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.1 Support Vector Machine (SVM) . . . . . . . . . . . . . . . . . . . . 54
5.2 Cross Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.3 The Unclear Group . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Implementation Results and Discussion 63
1 Statistical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
2 Feature Combination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3 Sequential Forward Selection Results . . . . . . . . . . . . . . . . . . . . . 69
4 Results of Excluding the Unclear Group . . . . . . . . . . . . . . . . . . . . 71
5 Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Conclusion and Future Works 75
1 Overview and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
                                

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