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研究生:魏英傑
研究生(外文):Ying-ChiehWei
論文名稱:以微控制器為基礎之即時心率變異度量測系統
論文名稱(外文):A Real-time Measurement System for Heart Rate Variability Based on Microcontrollers
指導教授:張凌昇
指導教授(外文):Ling-Sheng Jang
學位類別:博士
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:2013
畢業學年度:101
語文別:英文
論文頁數:67
中文關鍵詞:心率變異度R-R間期R波偵測演算法心電圖心電圖產生器
外文關鍵詞:Heart rate variabilityBeat-to-beat (RR) intervalR-wave detection algorithmElectrocardiogramECG generator
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心率變異度(HRV)是非常重要且一個非侵入式評估自律神經系統(ANS)的工具。其中又以短時(5分鐘)頻率域心率變異度分析使用最為廣泛。分析交感神經與副交感神經系統不僅能評估人們心裡是處於緊張或放鬆的狀態,也可以使用來診斷疾病。本論文的研究目的是發展以微控制器為基礎來設計一個即時的心率變異度量測系統。經由量測心電圖訊號可立即得到心率變異度資料,並評估出自律神經系統功能。
然而欲發展這類的即時的心率變異度量測系統,並不能使用一般的心電圖產生器進行測試,因此在這研究中也設計並發展一個具有頻率域參數的心率變異度的可程式心電圖產生器,能被使用於測試演算法的效能,且能校準與維護心電描記器。我們簡化並修改McSharry的三組聯立的微分方程式成為一個微分方程式來產生合成的ECG訊號。可程式心電圖產生器不僅能調整訊號振幅,心跳速率,QRS-complex的斜率, P波與T波的位置,也能頻率域參數的心率變異度的超低頻,低頻與高頻參數。ECG訊號的振幅能被設定範圍為0 mV ~ 330 mV,解析度為0.005 mV。我們的系統可以同時產生三種不同的合成ECG訊號可用於開發即時的心率變異度量測系統。
為了即時計算出心率變異度參數,我們發展了一個低複雜的R波偵測演算法來計算出RR時間間隔。這個演算法只顯出R波波峰的位置,因此演算法不僅可更快計算出RR時間間隔,更可容易的實現在微控制器上。由實驗結果得出我們的演算法於微控制器上量測心跳速率在20到200 BPM時,產生之錯誤小於1 BPM,量測與實際心跳速率之間的相關係數 r〉 0.99。由Bland-Altman 統計分析心率變異度量測系統每一項的心率變異度參數都有狹窄的一致性界線(limit of agreement, LoA),且皆有高的相關係數(r 〉 0.91),證明心率變異度量測系統所量測的心率變異度參數有足夠的正確性。這個系統可以即時分析出心率變異度參數,可讓有關自律神經系統的實驗與研究,即時了解交感神經與副交感的活性調節與變化。
Heart rate variability (HRV) is a crucial measurement used as a non-invasive tool to assess the function of the autonomic nervous system (ANS). A short-term (5 min) frequency domain analysis of HRV is the most popular type of analysis. An analysis of the sympathetic and parasympathetic nervous systems not only can be used to gauge whether a person’s mental status is stressed or relaxed but also can be used to diagnose disorders. The purpose of this study is the development of a real-time HRV measurement system based on microcontrollers. Through measuring ECG signal can immediately obtain the parameters of HRV and assessment of the autonomic nervous system function.
However, the development of real-time HRV measurement system can not use the general ECG generator to test. Therefore, in this study is also designed and developed a programmable electrocardiogram (ECG) generator with frequency domain characteristics of HRV which can be used to test the efficiency of ECG algorithms and to calibrate and maintain ECG equipment. We simplified and modified the three coupled ordinary differential equations in McSharry’s model to a single differential equation to obtain the ECG signal. This system not only allows the signal amplitude, heart rate, QRS-complex slopes, and P- and T-wave position parameters to be adjusted, but also can be used to adjust the very low frequency, low frequency, and high frequency components of HRV frequency domain characteristics. The amplitude of the ECG signal can be set from 0 to 330 mV at a resolution of 0.005 mV. Our system can generate three different types of synthetic ECG signals for the development of real-time HRV measurement system.
To calculate real-time HRV parameters, we developed a low-complexity R-peak detection algorithm to determine the RR intervals. This algorithm only highlights the R peak position; thus, it can quickly calculate RR intervals and easily be implemented in a microcontroller. Experimental results show that the rate of heartbeat error of our algorithm is less than 1 BPM. In addition, a high correlation coefficient (r〉 0.99) exists between the measured and actual heart rates in the tested range of 20 to 200 BPM. The HRV parameters of each subject were calculated using Bland-Altman statistical analysis and had a narrow LoA, and all parameters exhibited good correlation (r 〉 0.91). Thus, the results provide evidence that our system can generate adequately reliable HRV parameters. Importantly, the system can generate real-time HRV parameters, thereby facilitating autonomic nervous system research to elucidate the modulation and changes in sympathetic and parasympathetic neural activities.
摘 要 IV
ABSTRACT VI
誌 謝 IX
CONTENTS X
LIST OF TABLES XII
LIST OF FIGURES XIII
CHAPTER 1 Introduction 1
CHAPTER 2 Heart Rate Variability 7
CHAPTER 3 A Three-lead and Programmable Electrocardiogram Generator with Frequency Domain Characteristics of Heart Rate Variability 11
3.1. McSharry’s ECG Dynamical Model 11
3.2. Simplified Algorithm 12
3.3. The Generation of the Short-term HRV Spectrum 16
3.4. System Implementation 17
3.4.1. Hardware Design 17
3.4.2. Software Design 20
CHAPTER 4 Real-time Heart Rate Variability Measurement System Using a Low-complexity R-peak Detection Algorithm 22
4.1. The low-complexity R-peak detection algorithm 22
4.2. System Implementation 26
4.2.1. Hardware Design 26
4.2.2. Software Design 31
CHAPTER 5 Results and Discussion 34
5.1. The performance of ECG Generator Algorithm in the MCU 34
5.2. ECG signal generated from the synthetic ECG generator 35
5.3. The Synthetic ECG Generator Operated with the Frequency Domain HRV Function 43
5.4. The Heartbeat Detection Accuracy of the Low-complexity R-peak Detection Algorithm 48
5.5. Real-time Analysis of HR and HRV Parameters 52
5.6. The Measurement Results and the Relative Errors of the HRV System 54
CHAPTER 6 Conclusions 60
REFERENCES 63
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