臺灣博碩士論文加值系統

呼吸器是加護病房中輔助病人進行人工呼吸，支持生命很重要的生醫儀器。呼吸器除提供人工呼吸外，會有警報的設計，目的為能及時提醒醫療人員，病人在使用呼吸器時處於異常狀態，其中最常見的氣道高壓警報，代表的是呼吸器通氣時，病人氣道有受阻的問題，但是可能的原因很多，加上每日病人臨床的問題也繁多，需要更有效率地精準地判斷與處置。當呼吸器發出高壓警報時，第一種可能原因為病人咳嗽、痰液增加之動態阻塞。第二種原因是病人的呼吸道產生靜態阻塞，如支氣管收縮所引起；或病人肺功能的改變而引起的高壓警報。而之前發展的呼吸器高壓警報提醒系統，是以呼吸道檢測機制來分辨呼吸器病人之高壓警報時，呼吸道阻塞的分類。為了能再精準地提醒醫護人員，病人是否需要抽痰的警報，因此，我們的研究目的是以量化「呼吸流量」上之震盪狀況來發展呼吸器高壓警報之智慧型輔助系統。本論文將痰液阻塞時「呼吸流量」波形中特有的「鋸齒狀」波形(在此稱為震盪現象)，加以量化。量化的步驟為先將「呼吸流量」中含「鋸齒狀」的部分切出，然後再將其部分利用曲線擬合的方式找出平滑線，最後計算「鋸齒狀」波形與平滑線的差異作為量化值。在實驗結果部分，當病人出現痰液阻塞時「呼吸流量」波形中的震盪現象就會比較明顯，所量化出的震盪值就會較高；反之震盪值就會較小。因此本文藉由這量化值，使得呼吸器的高壓警報系統可以準確地給予醫療人員其病人需要抽痰的警示，以發展出智慧型呼吸器警報系統。

With the modern technology, people can still alive by the assistance of biomedical instrument even when they are seriously injured. Now, many types of ventilators have alarm systems in order to send alert signal to health care personnel when patients are in emergency condition. But the traditional ventilators alarm systems often give false alarm because the criterion was often only relying on one parameter. And health care personnel often can not recognize what the exactly conditions is in the beginning. So they must rely on a wealth of clinical experience to make right judgments after going to patients’ ward. According to that, we design an intelligent alarm system that will help health care personnel work more efficiently. In this study, we design a sputum alarm system to send alert signals when patients’ airway were obstructed by sputum. Until now, health care personnel have to go to patient’s ward to check what patient’s condition is after hearing high pressure alarm. And once they hear something like grunt sounds, they will implement sputum suction process. Thus, we can add that way to our alarm system. The grunt sounds will reflect on flow parameter in the ventilator. If there are some grunt sounds from the patient’s airway, flow wave will reveal some vibrations known as saw tooth pattern. In this study, we quantify the vibrations of the flow wave, and let this value as a criterion that our system can give the exact condition when patient’s was during airway sputum obstruction period. When the value we quantified was high, our system will send airway sputum obstruction alarm. In contrast, when the value was low, our system will send airway static obstruction alarm.

摘要 I
AbstractII
致謝 III
目錄 IV
圖目錄 VI
表目錄 IX
第一章 緒論 1
1-1 研究動機 1
1-2 文獻探討 1
第二章 人體呼吸系統原理 4
2-1 肺部生理機制 4
2-1-1 呼吸系統 4
2-1-2 通氣的概念 5
2-1-3 影響通氣的因素 6
2-2 呼吸治療基本概念 9
2-2-1 呼吸衰竭 9
2-2-2 機械通氣 9
2-2-3 使用呼吸器之併發症 10
2-2-4 呼吸器之脫離 10
第三章 呼吸治療相關之儀器設備 11
3-1 呼吸器 11
3-2 呼吸器之呼吸參數簡介 13
3-3 人工氣道內管 15
第四章 發展呼吸道阻塞檢測機制 17
4-1 設計動機 17
4-2 呼吸器警報系統簡介 17
4-3 第一代智慧型呼吸道阻塞檢測機制 18
4-4 發展第二代智慧型呼吸道阻塞檢測機制 21
第五章 發展智慧型抽痰警報 24
5-1 設計動機 24
5-2 呼吸流量震動現象之量化 25
5-2-1 「呼吸流量」資料的前處理 26
5-2-2 片段分割 28
5-2-3 修正分割錯誤的片段 29
5-2-4 量化「呼吸流量」的震盪現象 32
5-2-4-1步驟一 曲線擬合 33
5-2-4-2步驟二 計算震盪值 34
5-3 初步效果展示 35
5-4 警報設計之程式流程 38
第六章 實驗與討論 41
6-1 實驗環境 41
6-2 實驗步驟介紹 42
6-2-1 合適參數選定與實驗用物 42
6-2-2 實驗結果 49
6-3 智慧型呼吸器之抽痰警報實驗 54
第七章 結論與未來展望 61
參考文獻 62

[1] I. Grossbach, “Troubleshooting ventilator and patient-related problems--Part 2”. NCBI, Crit Care Nurse, Vol 6, No 5, pp. 64-79. 1986.
[2] I. Grossbach, “Troubleshooting ventilator- and patient-related problems/Part 1”. NCBI, Crit Care Nurse, Vol 6, No 4, pp. 58-70. 1986.
[3] I. Grossbach, L. Chlan, and M.F. Tracy, “Overview of mechanical ventilatory support and management of patient- and ventilator-related responses”. AACN Crit Care Nurse, Vol 31, No. 3 pp. 30-44, June 2011.
[4] J. Edworthy, and E. Hellier, “Alarms and human behaviour: implications for medical alarms”. BJA, Vol 97, No 1, pp. 12–17, May 2006.
[5] M. Imhoff, and S. Kuhls, “Alarm algorithms in critical care monitoring”. IARS Anesth Analg, Vol 102, No 5, pp. 1525-37, December 2006.
[6] M. Gorges, B.A. Markewitz, and D.R. Westenskow, “Improving alarm performance in the medical intensive care unit using delays and clinical context”, IARS Anesth Analg, Vol. 108, No. 5, pp 1546 –52, May 2009.
[7] K.R. Myerson, A.H. Ilsley, and W.B. Runciman, “An evaluation of ventilator monitoring alarms”. NCBI, Anaesth Intensive Care, Vol. 14, No. 2, pp. 174-85, 1986
[8] R.K. Visaria, and D.R. Westenskow “Model-based detection of partially obstructed endotracheal tube”. SCCM, Vol. 33, No. 1, 2005.
[9] R.S. Evant, K.V. Johnson, V. B. Flint, T. Kinder, C.R. Lyon, W. Hawley, D.K. Vawdrey, G. Thomsen, “Enhanced Notification of Critical Ventilator Events” . JAMIA, Vol 12, No. 6 pp. 589–595. November 2005.
[10] 中時健康—加護病房病人救星 呼吸器自動化時代來臨。報導日期：2009年7月14日。網址：http://health.chinatimes.com/。
[11] 衛生福利部中央健康保險署—全民健康保險重大傷病證明有效領證統計表。網址 : http://www.nhi.gov.tw/。
[12] 壢新醫院—首創自動化人工呼吸器 增加醫療即時性。報導日期：2012年4月29日。網址：
http://www.landseedhospital.com.tw/lishin/index_citi.php。
[13] J. M. Arnal, M. Wysocki, D. Novotni, D. Demory, R. Lopez, S. Donati, I. Granier, G. Corno, J. D. Gasselin. “Safety and efficacy of a fully closed-loop control ventilation (IntelliVent-ASV®) in sedated ICU patients with acute respiratory failure: a prospective randomized crossover study”.ESICM, Intensive Care Med, Vol 38, pp. 781–787. March 2012.
[14] W. Beachey, Respiratory care anatomy physiology, 2th Edition, Mosby Elsevier.,2007.
[15] T. Kevin and Patton, Anatomy &; Physiology, 3th Edition.1996.
[16] N.B. Lynelle, Management of the Mechanically Ventilated Patient, 2th Edition. Mosby Elsevier, 2006.
[17] A. Giorgio and A. Braschi, Measurements of respiratory mechanics during mechanical ventilation, Hamilton Medical AG, Rhäzüns, Switzerland. 1999.
[18] 吳清平，呼吸作功在呼吸機使用上的角色，中華民國呼吸照護簡訊。1999年4 月。
[19] 美國胸腔學院院士等編著，呼吸加強照護，二版。合記圖書出版社，台北市，民國八十一年。
[20] 吳清平等，機械性呼吸的演進－人工智慧型呼吸器的進展。Twain Crit. Care Med. 2003年。
[21] 吳清平，人工呼吸器的脫離。Taiwan Crit. Care Med. 1999年。
[22] 楊式興等，呼吸器監測與管理通報系統。台灣呼吸治療雜誌，2007年。
[23] J. M. Cairo and Pilbeam. Respiratory Care Equipment, 8th Edition. Mosby Elsevier, 2009.
[24] S. P. Pilbeam, and J.M. Cairo, Mechanical ventilation: physiological and clinical applications, 4th Edition. Mosby Elsevier, 2006.
[25] Shih-Hsing Yang¬ and Kuo-Kai Shyu, “Intelligent alarm system of mechanical ventilation: innovative pressure alarm for immediate clinical management”, IEEE International Conference on Biomedical Engineering and Biotechnology, pp.336-339, 2012.
[26] MT Steele, The occupational risk of motor vehicle collisions for emergency medicine residents . Acad Emerg Med 1999;6(10):1050-1053.
[27] 賴怡樺，施富金，成功脫離呼吸器之影響因素與護理。榮總護理 26卷4期，382-387，2009.12。