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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林世明(Shiming Lin) | |
| dc.contributor.author | Ru-Yin Yang | en |
| dc.contributor.author | 楊茹茵 | zh_TW |
| dc.date.accessioned | 2021-07-10T21:47:19Z | - |
| dc.date.available | 2021-07-10T21:47:19Z | - |
| dc.date.copyright | 2020-03-13 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-02-12 | |
| dc.identifier.citation | 1. MOWF. Statistics on the cause of death in Taiwan in 2018. 2019; Available from: https://www.mohw.gov.tw/cp-16-48057-1.html.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77118 | - |
| dc.description.abstract | 與心血管有關的疾病，如腦血管疾病、心臟病與高血壓是常見的死亡原因。相較於其他病症，心血管疾病較無明顯的病徵，心血管阻塞不易察覺，容易受到忽略而錯過治療時間，若能及早發現異狀可以降低心血管疾病的發生機率。
血壓是評估心血管疾病一項重要的指標。然而，現今被視為黃金標準的水銀血壓計，其量測血壓方式需由他人協助聽診，也容易因為壓脈帶的鬆緊度造成量測誤差，更無法長時間連續監測血壓。 為了開發連續監測血壓之方法，本研究利用壓力感測器與心電圖所測量之訊號，透過撰寫之峰值擷取方法計算心電圖訊號與橈動脈脈波訊號的波峰時間差距來獲得脈波傳導時間，再推算出脈搏波速度，最後將脈搏波速度與水銀血壓計量測之血壓數據進行線性迴歸模型的分析，建立推估血壓的演算法。 未來若能配合合適的機構，開發成穿戴式設備，並透過臨床試驗，收集更多數據分析後，將可以更有效的將此方法運用於血壓量測，達到長期追蹤血壓的功能。 | zh_TW |
| dc.description.abstract | Cardiovascular-related diseases such as cerebrovascular disease, heart disease, and hypertension are common causes of death. Compared with other diseases, cardiovascular disease has less obvious symptoms, cardiovascular obstruction is imperceptible, and it is easy to be ignored and miss the treatment time. As soon as possible find abnormalities that can reduce the incidence of cardiovascular disease. Blood pressure is an important evaluation indicator of cardiovascular disease. However, the mercury sphygmomanometer, which is now regarded as the gold standard, requires the assistance of auscultation by others. It is also easy to cause measurement errors due to the tightness of the cuff, and it is impossible to continuously monitor blood pressure for a long time.
In order to develop a method for continuous monitoring of blood pressure, this study uses signals measured by force sensor and electrocardiograms and calculates the peak time difference between ECG signal and radial arterial pulse wave signal by the peak detection method written to obtain the pulse wave transit time. Then calculate the pulse wave velocity. The blood pressure data of mercury sphygmomanometer and the pulse wave velocity we obtained were analyzed by a linear regression model to establish an algorithm for estimating blood pressure. In the future, if we can use the right mechanism to develop a wearable device, and collect more data analysis through clinical trials, we will be able to apply this method to blood pressure measurement more effectively and achieve long-term blood pressure tracking. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-10T21:47:19Z (GMT). No. of bitstreams: 1 ntu-109-R06458005-1.pdf: 11919224 bytes, checksum: 5c7b057433068dd85dad670471e5d4a2 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 誌謝 I
中文摘要 II Abstract III Contents IV List of Figures VII List of Tables IX Chapter PAGE 1. Introduction 1 1.1. Background 1 1.2. Cardiovascular disease and cerebrovascular diseases 3 1.2.1. Heart diseases 3 1.2.2. Stroke 5 1.2.3. Symptoms of cardiovascular diseases 6 1.2.4. Symptoms of cerebrovascular diseases 8 1.2.5. Blood pressure 10 1.2.6. Measuring blood pressure 12 1.3. Electrocardiography(ECG) 13 1.4. Pulse wave transit time(PWTT) 15 1.5. Research motivations and objectives 17 1.6. Thesis organization 19 2. Methodology 20 2.1. ECG modules 20 2.2. Force sensor modules 24 2.3. System integration 28 2.3.1. Force sensor module and ECG module integration 28 2.3.2. Development board 29 2.4. Mercury sphygmomanometers 30 2.5. Experimental setup and procedure 32 2.6. Signal processing 33 2.6.1. Signal acquisition 34 2.6.2. Signal analysis 34 2.6.3. Peak detection 35 2.6.4. Pulse wave transit time calculation 35 2.6.5. Pulse wave velocity estimate 35 2.7. Statistical analysis 36 3. Results 37 3.1. Characterization of study subjects 37 3.2. The obtained signal from PCB 38 3.3. Data recording 39 3.4. Blood pressure measured by mercury sphygmomanometers 40 3.5. Signal processing for the system of force sensor and the ECG 42 3.6. Parameter calculation in the system 44 3.6.1. Heart rate calculation 44 3.6.2. Pulse wave transit time calculation 46 3.6.3. Pulse wave velocity estimate 47 3.7. The linear regression between the pulse wave velocity and blood pressure 49 3.7.1. Pulse wave velocity and systolic blood pressure 49 3.7.2. Pulse wave velocity and diastolic blood pressure 51 3.7.3. Pulse wave velocity and mean arterial pressure 53 4. Discussion 56 4.1. The clinical measurement 56 4.2. Clinical parameters of subjects 56 4.3. Estimation of pulse wave velocity 57 4.4. The linear regression between mercury sphygmomanometers and this measuring system 58 4.5. Limitation of blood pressure measuring system 58 5. Conclusion and Future Prospect 60 Reference 62 | |
| dc.language.iso | en | |
| dc.subject | 血壓量測 | zh_TW |
| dc.subject | 心電圖訊號 | zh_TW |
| dc.subject | 壓力感測器 | zh_TW |
| dc.subject | 峰值擷取方法 | zh_TW |
| dc.subject | 脈波傳導時間 | zh_TW |
| dc.subject | 脈搏波速度 | zh_TW |
| dc.subject | ECG signal | en |
| dc.subject | blood pressure measurement | en |
| dc.subject | pulse wave velocity | en |
| dc.subject | pulse wave transit time | en |
| dc.subject | peak detection method | en |
| dc.subject | force sensor | en |
| dc.title | 一種連續監測動脈壓之方法 | zh_TW |
| dc.title | A Method for Continuously Monitoring the Artery Pressure | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 洪錦堂(Jim-Tong Horng),鄧致剛(Petrus Tang),賴信志(Hsin-Chih Lai) | |
| dc.subject.keyword | 心電圖訊號,壓力感測器,峰值擷取方法,脈波傳導時間,脈搏波速度,血壓量測, | zh_TW |
| dc.subject.keyword | ECG signal,force sensor,peak detection method,pulse wave transit time,pulse wave velocity,blood pressure measurement, | en |
| dc.relation.page | 69 | |
| dc.identifier.doi | 10.6342/NTU202000428 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-02-12 | |
| dc.contributor.author-college | 醫學院 | zh_TW |
| dc.contributor.author-dept | 醫療器材與醫學影像研究所 | zh_TW |
| 顯示於系所單位： | 醫療器材與醫學影像研究所 | |
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