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研究生: 張效煌
Hsiao-Huang Chang
論文名稱: 運用全息希爾伯譜於生理訊號分析
Biomedical Signal Analysis Using Holo-Hilbert Spectral Analysis
指導教授: 李柏磊
Po-Lei Lee
口試委員:
學位類別: 博士
Doctor
系所名稱: 資訊電機學院 - 電機工程學系
Department of Electrical Engineering
論文出版年: 2019
畢業學年度: 107
語文別: 英文
論文頁數: 55
中文關鍵詞: 穿戴式裝置心血管疾病全息希爾伯譜分析法
外文關鍵詞: Wearable Device, Cardiovascular Disease, Holo-Hilbert Spectral Analysis
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  • 隨著工商業發展愈來愈快,人類工作壓力愈來愈大;加上飲食不均衡,罹患心血管疾病的人口愈來愈多。根據2008~2012行政院衛生署統計的國人十大死因,多項死因與心血管疾病有關。近五年臺灣十大死因中,有將近一半的項目為心血管相關疾病;例如心臟病、腦血管疾病、糖尿病、高血壓性疾病以及腎臟方面疾病;另外,心血管相關疾病的死亡人數佔2012臺灣總死亡人數的30.3%,且心臟疾病以及腦血管疾病為心血管相關疾病致死的兩大主因。根據WHO的統計,單是心血管疾病的死亡人數佔2008全球總死亡人數的31%。很多患者都在病況嚴重時才就醫檢查;更何況在很多地區醫療資源嚴重不足。如何在醫院外的居家自我檢查、早期發現心血管有關疾病是一項重要課題。在目前的臨床生命特徵監測儀器中,主要以心電圖、呼吸偵測、血氧偵測、血壓偵測為主,在心電圖與血氧偵測已經有方便連續監控的儀器,但是呼吸偵測與血壓偵測卻沒有方便且連續監測的裝置。本研究開發穿戴式的生命特徵量測裝置,藉由心電圖(ECG)與脈波血容積量測器(PPG)的脈波傳遞速度(PWV)進行連續血壓波估測,並且引用全息希爾伯譜分析法(HHSA)經由手腕的脈波血容積量測器推測連續呼吸的數值。本研究所開發的裝置將來可以連續的紀錄受試者的生命特徵,將來可以應用這些獲得的生命特徵推估其他的生物指標預防心血管疾病。


    With the rapid developments of industrial society, heavy work load and irregular dietary can cause risky factors of cardiovascular diseases for people. According to the survey reported by department of health, Executive Yuan in Taiwan, most leading causes (~30.3%) of death during year 2008 ~2012 were related to cardiovascular disease. In addition, about half of the leading causes of death, such as heart disease, cerebrovascular disease, diabetes, hypertension, kidney disease, etc., in Taiwan, are all related to cardiovascular diseases. Besides, in accordance with the survey of World Health Organization (WHO) in 2008, ~31% of fatality rate in the world was related to cardiovascular diseases. Many patients consult doctors and have physical examinations only when conditions are serious, not to mention that people cannot detect their diseases by means of physical self-examinations, because of a severe shortage of medical supports in many districts. Therefore, how to achieve self-examinations at home and implement early detections of cardiovascular related diseases is an important issue. Current clinical monitoring systems measures electrocardiogram (ECG), respiration, blood oxygen, and blood pressure. Among the four vital signs, instruments for continuously monitoring of ECG and blood oxygen have been developed. Nevertheless, continuous recording system for long-term monitoring of respiration and blood pressure have not been developed yet. Accordingly, this study aims to develop convenient wearable devices for continuous monitoring of blood pressure and respiration. For blood pressure, we utilized the pulse wave velocity (PWV) measured from the time difference between ECG and PPG to estimate continuous blood pressure. Regarding respiration, the Holo-Hilbert Spectral Analysis (HHSA) was adopted to transform wrist PPG signals into Holo-Hilbert spectrum, so that the respiration rate can be found from the modulation frequency in Holo-Hilbert spectrum. The study results of this thesis propose a prototype form continuous monitoring of blood pressure and respiration, and the vital signs recorded from the proposed system might be able to derive new biomarker for early detection of cardiovascular diseases.

    Contents Pages 論文摘要.................................................................................ii Abstract................................................................................iii List of Figures........................................................................v List of Tables........................................................................vii Chapter 1 Introduction...........................................................1 Chapter 2 Materials and Methods..........................................9 2.1 Electrocardiogram (ECG) and photoplethy-smography (PPG) systems.............................................................9 2.2 Hilbert-Huang Transfrom (HHT)................................10 2.3 Holo-Hilbert Spectral Analysis (HHSA) for respiration detection....................................................................12 Chapter 3 Cuffless Blood Pressure Estimation using Pulse-Transit Time (PTT).................................................................16 Chapter 4 Respiration Estimation using Holo-Hilbert Spectral Analysis (HHSA)....................................................................21 Chapter 5 Conclusions and Future Works.............................38 References............................................................................40

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