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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王兆麟(Jaw-Lin Wang) | |
| dc.contributor.author | I-Cho Tsai | en |
| dc.contributor.author | 蔡易倬 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:26:06Z | - |
| dc.date.available | 2021-11-08 | |
| dc.date.available | 2022-11-24T03:26:06Z | - |
| dc.date.copyright | 2021-11-08 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-14 | |
| dc.identifier.citation | [1]衛生福利部國民健康署 「全民身體活動指引手冊」2020.08.14 [2]衛生福利部國民健康署 「高血壓不是中老年人專利!36萬青壯高血壓大軍，近24萬自己不知道」 2019.09.25 [3]Augusto, JF., Teboul, JL., Radermacher, P. et al. Interpretation of blood pressure signal: physiological bases, clinical relevance, and objectives during shock states. Intensive Care Med 37, 411–419 (2011). [4]Westerhof, N., Lankhaar, JW. Westerhof, B.E. The arterial Windkessel. Med Biol Eng Comput 47, 131–141 (2009). [5]Gary F. Mitchell, Lemuel A. Moyé, Eugene Braunwald, Jean-Lucien Rouleau, Victoria Bernstein, Edward M. Geltman, Greg C. Flaker, Marc A. Pfeffer. Sphygmomanometrically Determined Pulse Pressure Is a Powerful Independent Predictor of Recurrent Events After Myocardial Infarction in Patients With Impaired Left Ventricular Function. Circulation. 1997;96:4254–4260 [6]Tasbiraha Athaya and Sunwoong Choi,Kyung-Ah Sohn, Academic Editor. An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach. Sensors ;1(5): 1867. (2021) [7]Rundo F., Conoci S., Ortis A., Battiato S. (2018). An advanced bio-inspired PhotoPlethysmoGraphy (PPG) and ECG pattern recognition system for medical assessment. Sensors 18:405. 10.3390/s18020405. [8]Teng X.F., Zhang Y.T. Continuous and noninvasive estimation of arterial blood pressure using a photoplethysmographic approach; Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Cancun, Mexico. 17–21 September 2003; pp. 3153–3156. [9]McCombie D., Asada H., Reisner A. Identification of Vascular Dynamics and Estimation of the Cardiac Output Waveform from Wearable PPG Sensors. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2005;4:3490–3493. [10]Yinji Ma, Jungil Choi, Aurélie Hourlier-Fargette, Yeguang Xue, Ha Uk Chung, Jong Yoon Lee, Xiufeng Wang, Zhaoqian Xie, Daeshik Kang, Heling Wang, Seungyong Han, Seung-Kyun Kang, Yisak Kang, Xinge Yu, Marvin J. Slepian, Milan S. Raj, Jeffrey B. Model, Xue Feng, Roozbeh Ghaffari, John A. Rogers, Yonggang Huang. Relation between blood pressure and pulse wave velocity for human arteries. Proceedings of the National Academy of Sciences Oct 2018, 115 (44) 11144-11149; DOI: 10.1073/pnas.1814392115 [11]Pickering TG, et al.(2005) Recommendations for blood pressure measurement in humans and experimental animals: Part 1: Blood pressure measurement in humans: A statement for professionals from the subcommittee of professional and public education of the American Heart Association council on high blood pressure research. Circulation 111:697–716. [12]Lee, Joonnyong et al. “Novel blood pressure and pulse pressure estimation based on pulse transit time and stroke volume approximation.” Biomedical engineering online vol. 17,1 81. 18 Jun. 2018, doi:10.1186/s12938-018-0510-8 [13]Winokur ES, He DD, Sodini CG. A wearable vital signs monitor at the ear for continuous heart rate and pulse transit time measurements. In: 2012 annual international conference of the IEEE engineering in medicine and biology society: Aug. 28 2012-Sept. 1 2012 2012; 2012: 2724–7. [14]Mukkamala R, Hahn JO, Inan OT, Mestha LK, Kim CS, Töreyin H, Kyal S. Toward ubiquitous blood pressure monitoring via pulse transit time: theory and practice. IEEE rans Biomed Eng. 2015;62(8):1879–1901. doi: 10.1109/TBME.2015.2441951. [15]Dongseok Lee, Hyunbin Kwon, Dongyeon Son, Heesang Eom, Cheolsoo Park, Yonggyu Lim, Chulhun Seo, and Kwangsuk Park. Beat-to-Beat Continuous Blood Pressure Estimation Using Bidirectional Long Short-Term Memory Network. Sensors (Basel). 2021 Jan; 21(1): 96. [16]Gesche, H., Grosskurth, D., Küchler, G. et al. Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method. Eur J Appl Physiol 112, 309–315 (2012). [17]Stergiopulos N, Segers P, Westerhof N. Use of pulse pressure method for estimating total arterial compliance in vivo. Am J Physiol Heart Circ Physiol. 1999;276(2):H424–H428. doi: 10.1152/ajpheart.1999.276.2.H424. [18]Chemla D, Hébert J-L, Coirault C, Zamani K, Suard I, Colin P, Lecarpentier Y. Total arterial compliance estimated by stroke volume-to-aortic pulse pressure ratio in humans. Am J Physiol Heart Circ Physiol. 1998;274(2):H500–H505. doi: 10.1152/ajpheart.1998.274.2.H500. [19]R. Shriram, A. Wakankar, N. Daimiwal and D. Ramdasi, 'Continuous cuffless blood pressure monitoring based on PTT,' 2010 International Conference on Bioinformatics and Biomedical Technology, 2010, pp. 51-55, doi: 10.1109/ICBBT.2010.5479013. [20]White W.B., Berson A.S., Robbins C., Jamieson M.J., Prisant L.M., Roccella E., Sheps S.G. National standard for measurement of resting and ambulatory blood pressures with automated sphygmomanometers. Hypertension. 1993;21:504–509. doi: 10.1161/01.HYP.21.4.504. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81012 | - |
| dc.description.abstract | 近年來台灣擁有高血壓的人口越來越多，年齡層也越來越年輕化的趨勢，而諸如血壓及心率的這類生理資訊是跟健康息息相關的，透過密集或規律的量測血壓可以讓自己及醫生對自己生理狀況有更佳的掌握以及更好的診斷依據。市面上有著各式各樣的血壓儀，但是卻還沒有一款專為運動時間測血壓而打造的血壓儀。這些血壓計通常有著巨大的機器外殼或是充氣阻斷血流的臂帶，而它們使用來量測血壓的方法也讓它們不適合作為運動時量測血壓的工具。因此一個能夠連續且能在運動時使用的血壓計是一個還沒被滿足的需求。基於以上所述，本論文使用光學量測的方法來獲取心血管的生理資訊，根據血管的彈性反應來透過兩個光學探頭計算脈波傳遞時間來架構血壓的預測模型。本論文透過訓練深度學習模型做為預測血壓之模型，只需輸入脈波傳遞時間就能得到一定準確率之估計血壓。希望透過製作出一套新型血壓計的原型系統及流程，提供高血壓患者輕便及可用的運動用血壓計。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:26:06Z (GMT). No. of bitstreams: 1 U0001-3108202103430300.pdf: 3798974 bytes, checksum: dadd3dacdf99d95d56e475e040eaa767 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 摘要 i ABSTRACT ii 內容 iii 圖目錄 vi 第一章 簡介 1 1.1 研究目的 1 1.2 動機 2 第二章 研究背景 3 2.1 血壓的生理意義 3 2.2 血壓的連續波型 3 2.3 現行血壓計之量測原理及方法 5 2.4 系統理論 6 2.4.1 光體積變化描記圖法(Photoplethysmography) 6 2.4.2 本研究之血壓系統 9 2.4.3 脈搏壓 12 第三章 實現 13 3.1 系統架構 13 3.1.1 系統概述 13 3.2 硬體系統 14 3.2.1 感測元件 – MAX30102 14 3.2.2 感測元件 – XD-58C 15 3.2.3 血壓校正設備 19 3.2.4 軟體功能 20 3.2.5 使用者圖形介面 21 3.2.6 Arduino 處理器 24 3.3 訊號處理 26 3.3.1 特徵提取 26 3.3.2 移動平均值 27 3.4 以深度學習做為預測模型 28 第四章 實驗結果 30 4.1 實驗測試方法 30 4.1.1 跑步機測試 30 4.1.2 單次實驗 31 4.1.3 連續測試 33 4.1.4 重複性測試 34 第五章 藉由深度學習之多層感知器架構預測模型 36 5.1.1 資料前處理 36 5.1.2 模型架構 40 5.1.3 預測結果 43 第六章 討論 45 第七章 結論 47 簡寫參照 48 參考文獻 49 | |
| dc.language.iso | zh-TW | |
| dc.subject | 血壓計 | zh_TW |
| dc.subject | 運動 | zh_TW |
| dc.subject | 光體積變化描記法 | zh_TW |
| dc.subject | Blood pressure | en |
| dc.subject | Sphygmomanometer | en |
| dc.subject | Photoplethysmography | en |
| dc.title | 非銬式運動用血壓計原型 | zh_TW |
| dc.title | Prototype of Cuff-less Sphygmomanometer For Exercise | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 趙福杉(Hsin-Tsai Liu),施博仁(Chih-Yang Tseng) | |
| dc.subject.keyword | 血壓計,運動,光體積變化描記法, | zh_TW |
| dc.subject.keyword | Sphygmomanometer,Blood pressure,Photoplethysmography, | en |
| dc.relation.page | 51 | |
| dc.identifier.doi | 10.6342/NTU202102871 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-09-14 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| 顯示於系所單位： | 醫學工程學研究所 | |
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