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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 林啟萬(Chii-Wann Lin) | |
| dc.contributor.author | Dan-Jing Chang | en |
| dc.contributor.author | 張丹菁 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:51:52Z | - |
| dc.date.available | 2022-03-06 | |
| dc.date.copyright | 2020-03-06 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2020-02-26 | |
| dc.identifier.citation | [1] W. H. Organization, World Health Statistics 2015. World Health Organization, 2015.
[2] T. G. Weiser et al., “An estimation of the global volume of surgery: a modelling strategy based on available data,” The Lancet, vol. 372, no. 9633, pp. 139–144, Jul. 2008. [3] E. S. Ford, D. M. Mannino, A. G. Wheaton, W. H. Giles, L. Presley-Cantrell, and J. B. Croft, “Trends in the Prevalence of Obstructive and Restrictive Lung Function Among Adults in the United States: Findings From the National Health and Nutrition Examination Surveys From 1988-1994 to 2007-2010,” Chest, vol. 143, no. 5, pp. 1395–1406, May 2013. [4] C. H. Martinez et al., “Undiagnosed Obstructive Lung Disease in the United States. Associated Factors and Long-term Mortality,” Ann. Am. Thorac. Soc., vol. 12, no. 12, pp. 1788–1795, Nov. 2015. [5] F. Frutos-Vivar et al., “Risk Factors for Extubation Failure in Patients Following a Successful Spontaneous Breathing Trial,” Chest, vol. 130, no. 6, pp. 1664–1671, Dec. 2006. [6] R. X. A. Pramono, S. Bowyer, and E. Rodriguez-Villegas, “Automatic adventitious respiratory sound analysis: A systematic review,” PLOS ONE, vol. 12, no. 5, p. e0177926, May 2017. [7] M. E. Wechsler, “Managing Asthma in Primary Care: Putting New Guideline Recommendations Into Context,” Mayo Clin. Proc., vol. 84, no. 8, pp. 707–717, Aug. 2009. [8] H. Kiyokawa, M. Greenberg, K. Shirota, and H. Pasterkamp, “Auditory detection of simulated crackles in breath sounds,” Chest, vol. 119, no. 6, pp. 1886–1892, Jun. 2001. [9] S. Swarup and A. N. Makaryus, “Digital stethoscope: technology update,” Med. Devices Auckl. NZ, vol. 11, pp. 29–36, Jan. 2018. [10] M. Korppi and E. Lauhkonen, “Auscultation of respiratory sounds: how to practise, how to teach?,” Acta Paediatr., vol. 107, no. 7, pp. 1120–1121, Jul. 2018. [11] L. Bickley and P. G. Szilagyi, Bates’ Guide to Physical Examination and History-Taking. Lippincott Williams & Wilkins, 2012. [12] A. Bohadana, G. Izbicki, and S. S. Kraman, “Fundamentals of Lung Auscultation,” N. Engl. J. Med., vol. 370, no. 8, pp. 744–751, Feb. 2014. [13] I. Tomos, A. Karakatsani, E. D. Manali, and S. A. Papiris, “Celebrating Two Centuries since the Invention of the Stethoscope. René Théophile Hyacinthe Laënnec (1781–1826),” Ann. Am. Thorac. Soc., vol. 13, no. 10, pp. 1667–1670, Jul. 2016. [14] R. Lethbridge and M. L. Everard, “The Stethoscope: Historical Considerations,” in Breath Sounds: From Basic Science to Clinical Practice, K. N. Priftis, L. J. Hadjileontiadis, and M. L. Everard, Eds. Cham: Springer International Publishing, 2018, pp. 15–31. [15] D. Littmann, “An Approach to the Ideal Stethoscope,” JAMA, vol. 178, no. 5, p. 504, Nov. 1961. [16] R. Palaniappan, K. Sundaraj, and N. U. Ahamed, “Machine learning in lung sound analysis: A systematic review,” Biocybern. Biomed. Eng., vol. 33, no. 3, pp. 129–135, Jan. 2013. [17] H. Pasterkamp, S. S. Kraman, and G. R. Wodicka, “Respiratory Sounds,” Am. J. Respir. Crit. Care Med., vol. 156, no. 3, pp. 974–987, Sep. 1997. [18] “3M Littmann Range.” [Online]. Available: https://www.littmann.com/3M/en_US/littmann-stethoscopes/. [Accessed: 23-Jun-2019]. [19] M. P. Hlastala and A. J. Berger, Physiology of Respiration. Oxford University Press, USA, 2001. [20] P. Su and H. Chang, “Ssuchiang Tungchi: Wuhsiao Te Huhsi [Dead Space Ventilation: Ineffective Breathing!],” Neiko Hsuehchih, vol. 26, no. 2, pp. 69–76, 2015. [21] L. Vannuccini et al., “Capturing and preprocessing of respiratory sounds,” Eur Respir Rev, vol. 10, pp. 616–620, Jan. 2000. [22] Y. Wu, P. Wen, Y. Huang, Y. Huang, and C. Liu, “Huhsi Hsitung Te Shenti Chiencha [Physical Examination of the Respiratory System],” Chiating Yihsueh Yu Chitseng Yiliao, vol. 30, no. 3, pp. 67–83, Mar. 2015. [23] American Thoracic Society and others., “Updated nomenclature for membership reaction.,” American Thoracic Society and others., 3, 1977. [24] Y. Nagasaka, “Lung Sounds in Bronchial Asthma,” Allergol. Int., vol. 61, no. 3, pp. 353–363, Jan. 2012. [25] N. Meslier, G. Charbonneau, and J. L. Racineux, “Wheezes,” Eur. Respir. J., vol. 8, no. 11, pp. 1942–1948, Nov. 1995. [26] R. P. Baughman and R. G. Loudon, “Stridor: Differentiation from Asthma or Upper Airway Noise,” Am. Rev. Respir. Dis., vol. 139, no. 6, pp. 1407–1409, Jun. 1989. [27] M. Munakata et al., “Spectral and waveform characteristics of fine and coarse crackles.,” Thorax, vol. 46, no. 9, pp. 651–657, Sep. 1991. [28] M. Sarkar, I. Madabhavi, N. Niranjan, and M. Dogra, “Auscultation of the respiratory system,” Ann. Thorac. Med., vol. 10, no. 3, pp. 158–168, 2015. [29] S. Müller, A. Member, and P. Massarani, Transfer-Function Measurement with Sweeps DIRECTOR’S CUT INCLUDING PREVIOUSLY UNRELEASED MATERIAL. 2001. [30] N. M. Papadakis and G. E. Stavroulakis, “Low Cost Omnidirectional Sound Source Utilizing a Common Directional Loudspeaker for Impulse Response Measurements,” Appl. Sci., vol. 8, no. 9, p. 1703, Sep. 2018. [31] P. Lall, A. Abrol, and D. Locker, “Effects of Sustained Exposure to Temperature and Humidity on the Reliability and Performance of MEMS Microphone,” presented at the ASME 2017 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems collocated with the ASME 2017 Conference on Information Storage and Processing Systems, 2017, p. V001T01A022-V001T01A022. [32] L. Reboursiére and O. Lädeoja, “Augmented window: structure-borne sound drivers for sound-emitting solid objects and surfaces,” vol. 4, no. 4, p. 3, 2011. [33] E. Winer, The Audio Expert : Everything You Need to Know About Audio. Routledge, 2017. [34] Texas Instruments, “Audio Characterization Primer,” Texas Instruments, 2014. [35] P. Wang, “Kuoli Pingtung Kochi Tahsueh Chuangyi Chiaohsueh Chiaotsai Shuweihua - Shihyen Motai Fenhsi [National Pingtung University of Science and Technology Creative Teaching Materials’ Digitalization - Experimental Modal Analysis].” [Online]. Available: http://cec.npust.edu.tw/e-learning/craft/. [Accessed: 30-Jun-2019]. [36] R. K. Lindsay, B. G. Buchanan, E. A. Feigenbaum, and J. Lederberg, “DENDRAL: A case study of the first expert system for scientific hypothesis formation,” Artif. Intell., vol. 61, no. 2, pp. 209–261, Jun. 1993. [37] B. Buchanan and E. Shortliffe, Rule-based Expert System – The MYCIN Experiments of the Stanford Heuristic Programming Project. 1984. [38] R. A. Miller, “Medical diagnostic decision support systems--past, present, and future: a threaded bibliography and brief commentary,” J. Am. Med. Inform. Assoc. JAMIA, vol. 1, no. 1, pp. 8–27, Feb. 1994. [39] P. McCorduck, Machines who think: a personal inquiry into the history and prospects of artificial intelligence, 25th anniversary update. Natick, Mass: A.K. Peters, 2004. [40] O. Y. Al-Jarrah, P. D. Yoo, S. Muhaidat, G. K. Karagiannidis, and K. Taha, “Efficient Machine Learning for Big Data: A Review,” Big Data Res., vol. 2, no. 3, pp. 87–93, Sep. 2015. [41] M. Bahoura and C. Pelletier, “New parameters for respiratory sound classification,” in CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436), 2003, vol. 3, pp. 1457–1460 vol.3. [42] S. Chakroborty, A. Roy, and G. Saha, “Improved Closed Set Text-Independent Speaker Identification by Combining MFCC with Evidence from Flipped Filter Banks,” vol. 2, no. 11, p. 8, 2008. [43] P. Mayorga, C. Druzgalski, R. L. Morelos, O. H. González, and J. Vidales, “Acoustics based assessment of respiratory diseases using GMM classification,” in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010, pp. 6312–6316. [44] C. Yu, T. Tsai, S. Huang, and C. Lin, “Soft Stethoscope for Detecting Asthma Wheeze in Young Children,” Sensors, vol. 13, no. 6, pp. 7399–7413, Jun. 2013. [45] A. L. Beam and I. S. Kohane, “Big Data and Machine Learning in Health Care,” JAMA, vol. 319, no. 13, p. 1317, Apr. 2018. [46] V. Sessions, “THE EFFECTS OF DATA QUALITY ON MACHINE LEARNING ALGORITHMS,” p. 14, 2006. [47] W. Durfee and P. Iaizzo, Medical Device Innovation Handbook. Lulu.com, 2016. [48] S. C. Kurachek et al., “Extubation failure in pediatric intensive care: A multiple-center study of risk factors and outcomes,” Crit. Care Med., vol. 31, pp. 2657–64, Nov. 2003. [49] M. Y. Rady and T. Ryan, “Perioperative predictors of extubation failure and the effect on clinical outcome after cardiac surgery,” Crit. Care Med., vol. 27, no. 2, pp. 340–347, Feb. 1999. [50] STMicroelectronics, “MP23AB01DH Datasheet,” STMicroelectronics, DocID030017 Rev 2, Aug. 2017. [51] TAICEDN, “TAICEDN Shuhou Fuliao (Miehchun) Chanpin Tsaiho [Surgical Patch (Sterilized) Production Colored-Box].” TAICEDN. [52] Delta Electronics Group, “Medical AC-DC Adapter Datasheet,” Rev 07, May 2017. [53] 3M Littmann, “Electronic Stethoscope Model 3200 User Manual,” 3M Littmann, User Manual, 2009. [54] IMEDIPLUS INC., “Electronic Stethoscope DS101 User Manual,” IMEDIPLUS INC., User Manual, Aug. 2017. [55] V. N. Oliynik, “Determining the amplitude-frequency response for electronic stethoscope 3M Littmann 3200,” Acoust Bul, vol. 16, no. 3, pp. 46–57, 2013. [56] STMicroelectronics, “MEMS audio sensor omnidirectional digital microphone,” STMicroelectronics. [57] H. Chuang, “Chuchia Shengli Hsunhao Liangtse Shepei Kantsu Chishu Chienchieh [Introduction to Home Physiology Signal Measurement Equipment Sensing Technology],” 12:01:45 UTC. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64516 | - |
| dc.description.abstract | 呼吸道疾病是全球第三大死因,且在經歷重大手術或是插管呼吸器治療時,肺部及心因性併發症是醫療成本與死亡率上升的兩個重要原因,但要正確評估處置肺部疾病卻是長年以來在取決於人力成本與病況惡化間的難解議題。本研究以此為動機,結合臨床經驗與意見,研發一種達成密集持續監測,又可符合臨床人員工作流程的可連續性監測紀錄肺音聽診系統。除將研究成品與類似競品以聲學實驗方式實施頻響測試以驗證收音表現外,並根據手術中及加護病房之臨床聽診操作,設計良好的臨床肺音紀錄方法,可收集更適於機器學習或大數據分析之臨床肺音數據,有助於提升醫療照護品質。 | zh_TW |
| dc.description.abstract | Respiratory diseases are the third leading cause of death in the world. In major surgeries and endotracheal intubation ventilation therapies, pulmonary and cardiogenic complications are two primary causes of rising medical costs and mortality increasing, but the proper evaluation and treatment of lung diseases has been a long-term debated topic, and also a difficult problem between the cost of labor and the progression of disease. As a motivation to solve the problem, this study combined clinical experience and opinions to develop a lung sound auscultation system capable of continuous monitoring and recording, which achieves intensive continuous monitoring and meets the clinical staff's workflow at the same time. This research put the finished system and similar competing products in the acoustic test to verify its audio performance, and designed a proper clinical lung sound recording method from the clinical auscultation operation in operation rooms and intensive care units. The data recorded by this system proved to be more suitable for either machine learning or big data analysis in clinical lung sound data, and the result can be expected to help improve the quality of medical care. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:51:52Z (GMT). No. of bitstreams: 1 ntu-108-R06548067-1.pdf: 5091159 bytes, checksum: ec372cff1a62327e4b7bf60baca1e5a0 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | CONTENTS
口試委員會審定書 # 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES ix Chapter 1 緒論 1 1.1 研究動機 1 1.2 研究目的 2 1.3 研究之重要性 2 1.4 論文架構 3 Chapter 2 文獻探討 4 2.1 肺部聽診簡介 4 2.1.1 肺部聽診的目的 4 2.1.2 肺部聽診的工具 6 2.2 肺音簡介 9 2.2.1 呼吸的解剖生理機制 9 2.2.2 肺音的分類與特性 11 2.3 頻響測試 16 2.3.1 Room EQ Wizard (REW) 16 2.3.2 指數正弦掃頻(Logarithmic Sine Sweep) 16 2.4 人工智慧於生理訊號醫療之應用 17 2.4.1 演進 17 2.4.2 困境與展望 19 Chapter 3 研究原理與方法 20 3.1 連續肺音聽診系統設計 20 3.1.1 臨床需求 20 3.1.2 機構設計 23 3.2 聲學實驗 31 3.2.1 競品比較 32 3.2.2 實驗架構與流程 33 3.3 臨床操作流程設計 35 3.3.1 臨床聽診與連續生理訊號收集 36 3.3.2 臨床操作流程設計 37 3.4 肺音數據驗證 41 3.4.1 標準肺音收集 41 3.4.2 臨床數據收集 41 Chapter 4 實驗結果與討論 44 4.1 聲學實驗結果 44 4.2 肺音數據驗證結果 46 Chapter 5 結論與未來展望 54 5.1 結論 54 5.2 未來展望 55 REFERENCE 56 | |
| dc.language.iso | zh-TW | |
| dc.subject | 呼吸音 | zh_TW |
| dc.subject | 連續聽診 | zh_TW |
| dc.subject | 電子聽診器 | zh_TW |
| dc.subject | 頻響測試 | zh_TW |
| dc.subject | 肺音分析 | zh_TW |
| dc.subject | lung sound analysis | en |
| dc.subject | breathing sound | en |
| dc.subject | continuous auscultation | en |
| dc.subject | electronic stethoscope | en |
| dc.subject | frequency response test | en |
| dc.title | 連續肺音聽診系統開發 | zh_TW |
| dc.title | Development of Continuous Lung Sound Auscultation System | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃念祖(Nien-Tsu Huang),施博仁(Po-Jen Shih) | |
| dc.subject.keyword | 呼吸音,連續聽診,電子聽診器,頻響測試,肺音分析, | zh_TW |
| dc.subject.keyword | breathing sound,continuous auscultation,electronic stethoscope,frequency response test,lung sound analysis, | en |
| dc.relation.page | 61 | |
| dc.identifier.doi | 10.6342/NTU202000616 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-02-27 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| Appears in Collections: | 醫學工程學研究所 | |
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| File | Size | Format | |
|---|---|---|---|
| ntu-108-1.pdf Restricted Access | 4.97 MB | Adobe PDF |
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