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題名 睡眠呼吸中止症(OSA)病患中具低覺醒閾值與高覺醒閾值病人的配適模型，與其對應的重要變數 作者 王揆庭
Wang, Kuei-Ting貢獻者 楊素芬<br>蕭又新
Yang, Su-Fen<br>Shiau, Yuo-Hsien
王揆庭
Wang, Kuei-Ting關鍵詞 睡眠呼吸中止症
低覺醒閾值
身體組成分析
預測模型日期 2021 上傳時間 4-Aug-2021 14:42:10 (UTC+8) 摘要 阻塞型睡眠呼吸中止症(OSA)的部分病患中，因為患有低覺醒閾值，容易在睡眠之中醒過來，對於OSA現行治療方式，Edward等人(2014)指出，配戴持續性正壓呼吸器對於低覺醒患者的依從性低，需另尋其他治療方式。Edward等人(2014)發展一套對於高和低覺醒閾值的病患分類的評分法，其所需的三項評分指標需經多項睡眠生理檢查取得之，但成本高。本研究建立一套預測模型以區分高和低覺醒閾值病患，並找出對分類貢獻度高的重要變數。數據來自雙和醫院睡眠中心的PSG資料庫，包含2270位呼吸中止症病患接受多項睡眠生理檢查與睡眠前、後的身體組成變數資料。採用 Edward等人(2014)的評分法將所有病患分類後，得每一位病患是否為低覺醒閾值的標籤做為應變數，並對病患的體型特徵分組後對其在睡眠前、後檢測的身體組成變數結合打鼾指標和平均心率等作為自變數並執行主成分分析。接著，選用 Logistic regression、Neural network、Random forest與Support vector machine等方法以訓練集配適模型，並比較不同模型的訓練集和測試集的準確率。結果顯示，男性樣本根據Logistic regression或是Neural network模型在每一組建立的不同模型中表現的預測準確率與交叉驗證後的平均預測準確率較佳，女性樣本則是SVM模型預測比現較佳。接著透過模型內找出重要自變數，並觀察在高和低覺醒閾值之下的差異與表現。 參考文獻 周坤達、吳紹豪、蕭光明 (2012)。簡介阻塞型睡眠呼吸中止症。台北市醫師公會會刊，第56卷，第10期。法務部 (2019)。人體研究法，全國法規資料庫陳承昌(2006)。支持向量機及Plausible Neural Network於水稻田辨識之研究。國立交通大學土木工程研究所論文陳時仲(2015)。隨機森林模型效力評估。國立交通大學統計研究所論文衛生福利部國民健康署 (2020)。成人健康體位標準，載於： https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=542&pid=9737鐘威昇、侯承伯 (2013)。阻塞性呼吸中止症候群。家庭醫學與基層醫療，第28卷，第6期。英文部份Ben-Hur, A., Horn, D., Siegelmann, H.T., & Vapnik, V. (2001) Support vector clustering. Journal of Machine Learning Research, 125–137.Bradley, T. D., & Floras, J. S. (2009). Obstructive sleep apnea and its cardiovascular consequences. Lancet, 373(9657), 82–93.Breiman, L. (2001) Random Forests. Machine Learning 45, 5–32.Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (1984). Classification And Regression Trees (1st ed.). Routledge.Cortes, C., Vapnik, V. (1995). Support-vector networks., Mach Learn 20, 273–297.Dinges, D. F., Weaver, T. E. (2003). Effects of modafinil on sustained attention performance and quality of life in OSA patients with residual sleepiness while being treated with nCPAP. Sleep Medicine, Volume 4, Issue 5, 1389-9457.Eckert, D. J., Owens, R. L., Kehlmann, G. B., Wellman, A., Rahangdale, S., Yim-Yeh, S., White, D. P., & Malhotra, A. (2011). Eszopiclone increases the respiratory arousal threshold and lowers the apnea/hypopnea index in obstructive sleep apnea patients with a low arousal threshold. Clinical science, 120(12), 505–514.Eckert, D. J., Owens, R. L., Kehlmann, G. B., Wellman, A., Rahangdale, S., Yim-Yeh, S., White, D. P., & Malhotra, A. (2011). Eszopiclone increases the respiratory arousal threshold and lowers the apnoea/hypopnoea index in obstructive sleep apnoea patients with a low arousal threshold. Clinical science, 120(12), 505–514.Eckert, D. J., White, D. P., Jordan, A. S., Malhotra, A., & Wellman, A. (2013). Defining phenotypic causes of obstructive sleep apnea. Identification of novel therapeutic targets. American journal of respiratory and critical care medicine, 188(8), 996–1004.Edwards, B. A., Eckert, D. J., McSharry, D. G., Sands, S. A., Desai, A., Kehlmann, G., Bakker, J. P., Genta, P. R., Owens, R. L., White, D. P., Wellman, A., & Malhotra, A. (2014). Clinical predictors of the respiratory arousal threshold in patients with obstructive sleep apnea. American journal of respiratory and critical care medicine, 190(11), 1293–1300.Franklin, K. A., & Lindberg, E. (2015). Obstructive sleep apnea is a common disorder in the population-a review on the epidemiology of sleep apnea. Journal of thoracic disease, 7(8), 1311–1322.Geraldine M. N., Liam S. D., Walter T. N. (2007) Auto-Adjusting Versus Fixed Positive Pressure Therapy in Mild to Moderate Obstructive Sleep Apnoea, Sleep, Volume 30, Issue 2, 189-194Glass, G. V. (1965). A ranking variable analogue of biserial correlation: Implications for short-cut item analysis. Journal of Educational Measurement, 2(1), 91–95.Gray, E. L., McKenzie, D. K., & Eckert, D. J. (2017). Obstructive Sleep Apnea without Obesity Is Common and Difficult to Treat: Evidence for a Distinct Pathophysiological Phenotype. Journal of clinical sleep medicine, 13(1), 81–88.Hang, LW., Huang, CS. & Cheng, WJ. (2020). Clinical characteristics of Asian patients with sleep apnea with low arousal threshold and sleep structure change with continuous positive airway pressure. Sleep Breath.Haykin, S., & Network, N. (2004). A comprehensive foundation. Neural networks, 2, 41.Hosmer, D. W., Lemeshow, S. (2013) Applied Logistic Regression Second Edition. New York : John Wiley & Sons, Inc.Hung YC. (2020). Support vector classification. National Chengchi University Department of StatisticsKingma, D and Ba, J . (2015) Adam: A method for Stochastic Optimization.Lee, R., Sutherland, K., Sands, S. A., Edwards, B. A., Chan, T. O., Susana, S. S. NG., Hui, D. S., & Cistulli, P. A. (2017). Differences in respiratory arousal threshold in Caucasian and Chinese patients with obstructive sleep apnoea. Respirology, 22(5), 1015–1021.Li, K. K., Kushida, C., Powell, N. B., Riley, R. W., & Guilleminault, C. (2000). Obstructive sleep apnea syndrome: a comparison between Far-East Asian and white men. The Laryngoscope, 110.Liu, D., Myles, H., Foley, D. L., Watts, G. F., Morgan, V. A., Castle, D., Waterreus, A., Mackinnon, A., & Galletly, C. A. (2016). Risk Factors for Obstructive Sleep Apnea Are Prevalent in People with Psychosis and Correlate with Impaired Social Functioning and Poor Physical Health. Frontiers in psychiatry, 7, 139.Mansukhani, M.P., Kolla, B P., Olson, E. J., Ramar K., & Morgenthaler, T. I. (2014) Bilevel positive airway pressure for obstructive sleep apnea, Expert Review of Medical Devices, 283-294Moghalu, O., Whitesell, P., & Kwagyan, J. (2020) Low Respiratory Arousal Threshold (LRAT) in African Americans with Obstructive Sleep Apnea (OSA). (2740) Neurology, 94.Pavwoski, P., Shelgikar, A. V. (2017). Treatment options for obstructive sleep apnea, Neurol Clinical Practice, 7(1) 77-85.Pratt J.W., Gibbons J.D. (1981) Kolmogorov-Smirnov Two-Sample Tests. In: Concepts of Nonparametric Theory. Springer Series in Statistics. Springer, New YorkZinchuk, A., Edwards, B. A., Jeon, S., Koo, B. B., Concato, J., Sands, S., Wellman, A., & Yaggi, H. K. (2018). Prevalence, Associated Clinical Features, and Impact on Continuous Positive Airway Pressure Use of a Low Respiratory Arousal Threshold Among Male United States Veterans With Obstructive Sleep Apnea. Journal of clinical sleep medicine, 14(5), 809–817. 描述 碩士
國立政治大學
統計學系
108354014資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108354014 資料類型 thesis dc.contributor.advisor 楊素芬<br>蕭又新 zh_TW dc.contributor.advisor Yang, Su-Fen<br>Shiau, Yuo-Hsien en_US dc.contributor.author (Authors) 王揆庭 zh_TW dc.contributor.author (Authors) Wang, Kuei-Ting en_US dc.creator (作者) 王揆庭 zh_TW dc.creator (作者) Wang, Kuei-Ting en_US dc.date (日期) 2021 en_US dc.date.accessioned 4-Aug-2021 14:42:10 (UTC+8) - dc.date.available 4-Aug-2021 14:42:10 (UTC+8) - dc.date.issued (上傳時間) 4-Aug-2021 14:42:10 (UTC+8) - dc.identifier (Other Identifiers) G0108354014 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136319 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 統計學系 zh_TW dc.description (描述) 108354014 zh_TW dc.description.abstract (摘要) 阻塞型睡眠呼吸中止症(OSA)的部分病患中，因為患有低覺醒閾值，容易在睡眠之中醒過來，對於OSA現行治療方式，Edward等人(2014)指出，配戴持續性正壓呼吸器對於低覺醒患者的依從性低，需另尋其他治療方式。Edward等人(2014)發展一套對於高和低覺醒閾值的病患分類的評分法，其所需的三項評分指標需經多項睡眠生理檢查取得之，但成本高。本研究建立一套預測模型以區分高和低覺醒閾值病患，並找出對分類貢獻度高的重要變數。數據來自雙和醫院睡眠中心的PSG資料庫，包含2270位呼吸中止症病患接受多項睡眠生理檢查與睡眠前、後的身體組成變數資料。採用 Edward等人(2014)的評分法將所有病患分類後，得每一位病患是否為低覺醒閾值的標籤做為應變數，並對病患的體型特徵分組後對其在睡眠前、後檢測的身體組成變數結合打鼾指標和平均心率等作為自變數並執行主成分分析。接著，選用 Logistic regression、Neural network、Random forest與Support vector machine等方法以訓練集配適模型，並比較不同模型的訓練集和測試集的準確率。結果顯示，男性樣本根據Logistic regression或是Neural network模型在每一組建立的不同模型中表現的預測準確率與交叉驗證後的平均預測準確率較佳，女性樣本則是SVM模型預測比現較佳。接著透過模型內找出重要自變數，並觀察在高和低覺醒閾值之下的差異與表現。 zh_TW dc.description.tableofcontents 摘要 1圖目錄 4表目錄 5第一章、緒論 8第一節 研究背景 8第二節 研究動機 9第二章 文獻回顧 11第一節 OSA的盛行率與臨床症狀 11第二節 低覺醒閾值類型占比與體型特徵 12第三節 現有區分高和低覺醒閾值方式 13第四節 OSA的治療方針 13第三章、低覺醒閾值的影響變數(以雙和醫院睡眠中心病患分析) 15第一節 資料來源 15第二節 病患資料蒐集與影響變數定義 15第三節 病患體型特徵之分組 25第四節 病患各組下的分組與主成分分析 42第四章 低覺醒閾值與影響變數的模型配適 52第一節 與低覺醒閾值有關的影響變數選取 52第二節 低覺醒閾值與影響變數的模型配適方法 54第三節 各組配適模型準確率與預測準確率比較 64第四節 依配適模型決定影響低覺醒閾值之重要變數 68第五節 低覺醒閾值病患體型特徵探討 83第五章 結論與建議 86附錄 Logistic regression模型建立過程(所有OSA病患以性別、年齡、BMI分組) 88參考文獻 101 zh_TW dc.format.extent 4404822 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108354014 en_US dc.subject (關鍵詞) 睡眠呼吸中止症 zh_TW dc.subject (關鍵詞) 低覺醒閾值 zh_TW dc.subject (關鍵詞) 身體組成分析 zh_TW dc.subject (關鍵詞) 預測模型 zh_TW dc.title (題名) 睡眠呼吸中止症(OSA)病患中具低覺醒閾值與高覺醒閾值病人的配適模型，與其對應的重要變數 zh_TW dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 周坤達、吳紹豪、蕭光明 (2012)。簡介阻塞型睡眠呼吸中止症。台北市醫師公會會刊，第56卷，第10期。法務部 (2019)。人體研究法，全國法規資料庫陳承昌(2006)。支持向量機及Plausible Neural Network於水稻田辨識之研究。國立交通大學土木工程研究所論文陳時仲(2015)。隨機森林模型效力評估。國立交通大學統計研究所論文衛生福利部國民健康署 (2020)。成人健康體位標準，載於： https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=542&pid=9737鐘威昇、侯承伯 (2013)。阻塞性呼吸中止症候群。家庭醫學與基層醫療，第28卷，第6期。英文部份Ben-Hur, A., Horn, D., Siegelmann, H.T., & Vapnik, V. (2001) Support vector clustering. Journal of Machine Learning Research, 125–137.Bradley, T. D., & Floras, J. S. (2009). Obstructive sleep apnea and its cardiovascular consequences. Lancet, 373(9657), 82–93.Breiman, L. (2001) Random Forests. Machine Learning 45, 5–32.Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (1984). Classification And Regression Trees (1st ed.). Routledge.Cortes, C., Vapnik, V. (1995). Support-vector networks., Mach Learn 20, 273–297.Dinges, D. F., Weaver, T. E. (2003). Effects of modafinil on sustained attention performance and quality of life in OSA patients with residual sleepiness while being treated with nCPAP. Sleep Medicine, Volume 4, Issue 5, 1389-9457.Eckert, D. J., Owens, R. L., Kehlmann, G. B., Wellman, A., Rahangdale, S., Yim-Yeh, S., White, D. P., & Malhotra, A. (2011). Eszopiclone increases the respiratory arousal threshold and lowers the apnea/hypopnea index in obstructive sleep apnea patients with a low arousal threshold. Clinical science, 120(12), 505–514.Eckert, D. J., Owens, R. L., Kehlmann, G. B., Wellman, A., Rahangdale, S., Yim-Yeh, S., White, D. P., & Malhotra, A. (2011). Eszopiclone increases the respiratory arousal threshold and lowers the apnoea/hypopnoea index in obstructive sleep apnoea patients with a low arousal threshold. Clinical science, 120(12), 505–514.Eckert, D. J., White, D. P., Jordan, A. S., Malhotra, A., & Wellman, A. (2013). Defining phenotypic causes of obstructive sleep apnea. Identification of novel therapeutic targets. American journal of respiratory and critical care medicine, 188(8), 996–1004.Edwards, B. A., Eckert, D. J., McSharry, D. G., Sands, S. A., Desai, A., Kehlmann, G., Bakker, J. P., Genta, P. R., Owens, R. L., White, D. P., Wellman, A., & Malhotra, A. (2014). Clinical predictors of the respiratory arousal threshold in patients with obstructive sleep apnea. American journal of respiratory and critical care medicine, 190(11), 1293–1300.Franklin, K. A., & Lindberg, E. (2015). Obstructive sleep apnea is a common disorder in the population-a review on the epidemiology of sleep apnea. Journal of thoracic disease, 7(8), 1311–1322.Geraldine M. N., Liam S. D., Walter T. N. (2007) Auto-Adjusting Versus Fixed Positive Pressure Therapy in Mild to Moderate Obstructive Sleep Apnoea, Sleep, Volume 30, Issue 2, 189-194Glass, G. V. (1965). A ranking variable analogue of biserial correlation: Implications for short-cut item analysis. Journal of Educational Measurement, 2(1), 91–95.Gray, E. L., McKenzie, D. K., & Eckert, D. J. (2017). Obstructive Sleep Apnea without Obesity Is Common and Difficult to Treat: Evidence for a Distinct Pathophysiological Phenotype. Journal of clinical sleep medicine, 13(1), 81–88.Hang, LW., Huang, CS. & Cheng, WJ. (2020). Clinical characteristics of Asian patients with sleep apnea with low arousal threshold and sleep structure change with continuous positive airway pressure. Sleep Breath.Haykin, S., & Network, N. (2004). A comprehensive foundation. Neural networks, 2, 41.Hosmer, D. W., Lemeshow, S. (2013) Applied Logistic Regression Second Edition. New York : John Wiley & Sons, Inc.Hung YC. (2020). Support vector classification. National Chengchi University Department of StatisticsKingma, D and Ba, J . (2015) Adam: A method for Stochastic Optimization.Lee, R., Sutherland, K., Sands, S. A., Edwards, B. A., Chan, T. O., Susana, S. S. NG., Hui, D. S., & Cistulli, P. A. (2017). Differences in respiratory arousal threshold in Caucasian and Chinese patients with obstructive sleep apnoea. Respirology, 22(5), 1015–1021.Li, K. K., Kushida, C., Powell, N. B., Riley, R. W., & Guilleminault, C. (2000). Obstructive sleep apnea syndrome: a comparison between Far-East Asian and white men. The Laryngoscope, 110.Liu, D., Myles, H., Foley, D. L., Watts, G. F., Morgan, V. A., Castle, D., Waterreus, A., Mackinnon, A., & Galletly, C. A. (2016). Risk Factors for Obstructive Sleep Apnea Are Prevalent in People with Psychosis and Correlate with Impaired Social Functioning and Poor Physical Health. Frontiers in psychiatry, 7, 139.Mansukhani, M.P., Kolla, B P., Olson, E. J., Ramar K., & Morgenthaler, T. I. (2014) Bilevel positive airway pressure for obstructive sleep apnea, Expert Review of Medical Devices, 283-294Moghalu, O., Whitesell, P., & Kwagyan, J. (2020) Low Respiratory Arousal Threshold (LRAT) in African Americans with Obstructive Sleep Apnea (OSA). (2740) Neurology, 94.Pavwoski, P., Shelgikar, A. V. (2017). Treatment options for obstructive sleep apnea, Neurol Clinical Practice, 7(1) 77-85.Pratt J.W., Gibbons J.D. (1981) Kolmogorov-Smirnov Two-Sample Tests. In: Concepts of Nonparametric Theory. Springer Series in Statistics. Springer, New YorkZinchuk, A., Edwards, B. A., Jeon, S., Koo, B. B., Concato, J., Sands, S., Wellman, A., & Yaggi, H. K. (2018). Prevalence, Associated Clinical Features, and Impact on Continuous Positive Airway Pressure Use of a Low Respiratory Arousal Threshold Among Male United States Veterans With Obstructive Sleep Apnea. Journal of clinical sleep medicine, 14(5), 809–817. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202101064 en_US
