||A home prescreening system based on sleep questionnaires and smartwatches with physiological signal measurement for sleep apnea detection
||Department of Electrical Engineering
blood oxygen saturation
deep convolutional and long short-term memory network
本論文旨在利用睡眠問卷系統與智慧錶的生理訊號開發一套針對睡眠呼吸中止症的居家檢測系統。睡眠問卷系統是一套應用程式(APP)，包含臨床使用的五種睡眠相關問卷；而智慧錶則內含光體積變化描記圖法感測器(Photoplethysmography sensor，簡稱PPG感測器)、血氧濃度訊號(SpO2)感測器和生理訊號演算法。本論文所開發的睡眠呼吸中止症之居家檢測系統分為兩種：第一種是單純使用受測者填寫之問卷資料評估其睡眠呼吸中止症的嚴重程度；第二種是結合受測者填寫之問卷資料和智慧錶之生理訊號分析結果，進一步地評估其睡眠呼吸中止症的嚴重程度。在第一種睡眠呼吸中止症居家檢測系統中，總共有來自成大醫院的睡眠中心所收錄1,746位病患的睡眠問卷，而問卷會在病患進行多項生理監測儀(Polysomnography, PSG)檢測前完成，因此受檢後的嚴重程度指標AHI(Apnea-hypopnea index)其嚴重程度將會作為黃金標準，並且進行5等分交叉驗證本論文所提出之方法的有效性。在訓練模型之前，首先將問卷資料中含有遺失值的病患進行移除，隨後透過快速過濾的特徵選取法在睡眠問卷中選出辨識效果較為良好的輸入特徵。辨識器的部分，本論文使用並比較了三種不同的辨識器，分別為支持向量機、梯度提升決策樹、倒傳遞類神經網路，最後將辨識結果轉換成睡眠呼吸中止症之嚴重程度。結果顯示，倒傳遞類神經網路辨識器有最好的結果，其平均準確率、靈敏度與特異度分別為74.4%、77.3%和71.1%。在第二種睡眠呼吸中止症居家檢測系統中，除了利用第一種的睡眠問卷系統外，還會結合智慧錶的生理訊號演算法，智慧錶的生理訊號演算法首先透過訊號濾波將雜訊濾除，接著利用P波偵測抓取光體積變化描記圖(PPG)中的P波，最後訊號在經過補償後，我們將P波和P波之間距離所計算的心跳間距(PP intervals, PPIs)與血氧濃度訊號進行片段的切割，並且將切割的片段作為辨識器的輸入特徵。辨識器的部分，本論文使用並比較了三種不同的辨識器，分別為支持向量機、梯度提升決策樹、深層卷積與長短期記憶網路，透過辨識器的分析，判斷未知的睡眠片段是否發生睡眠呼吸中止事件。本研究共有38位經醫師診斷為睡眠呼吸中止症的病患參與收案，同時蒐集每位受試者在睡眠中心入睡一晚的PSG資料與智慧錶的生理訊號，並以技師在PSG所判讀的睡眠事件作為智慧錶的黃金標準，最後進行5等分交叉驗證本論文所提出之方法的有效性。結果顯示，使用深層卷積與長短期記憶網路辨識器可得到最佳結果，對於睡眠呼吸中止事件的辨識，其平均準確率為81.4%。研究結果驗證了本系統作為居家篩檢睡眠呼吸中止症之可行性，希冀本系統能幫助睡眠呼吸中止症患者使用方便且平價的工具進行居家檢測，並且提供醫生相關數據使得嚴重患者能夠優先治療。
This thesis aims to develop a home prescreening system based on a sleep questionnaire system and physiological signals of smartwatches for sleep apnea detection. The sleep questionnaire system is implemented by an application program (APP) running in portable devices, such as smartphones or pads. The APP contains five sleep-related questionnaires used for clinical evaluation, while the smartwatch contains a photoplethysmography (PPG) sensor, a blood oxygen saturation (SpO2) sensor and physiological signal analysis algorithms. Two types of home prescreening systems for sleep apnea have been developed: the first type is to use only the questionnaires filled out by subjects for the severity of sleep apnea evaluation, and the second type is to combine the questionnaires and the physiological signal analysis of the smartwatch to assess the severity of sleep apnea. In the first type of home prescreening systems, the total of 1,746 patients’ sleep questionnaires was collected from the sleep center of National Cheng Kung University Hospital, and the questionnaires had completed before the patient undertook a full night of polysomnography (PSG) sleep study. The values of AHI provided by the hospital were regarded as the gold standard of our system training. Before system training, we first removed the patients with missing data in the questionnaires, and then selected significant input features by a fast correlation-based filter. Using the selected features, three classifiers including support vector machines (SVM), gradient boosting decision trees (GBDT), and backpropagation neural networks (BPNN) were trained to detect the severity of apnea-hypopnea index (AHI). The results showed that the best classifier was BPNN which reached the average accuracy, sensitivity, and specificity were 74.4%, 77.3%, and 71.1%, respectively, with a 5-fold cross validation. The second type of home prescreening systems combined the sleep questionnaires with the results obtained by the physiological signal analysis algorithm of the smartwatch for sleep apnea detection. The physiological signal analysis algorithm first removed the noise or artifacts by a filter, and then detected the P waves from filtered PPG signals. Then, the algorithm calculated the intervals between two adjacent P waves, denoted as PPIs, and then we segmented PPIs combined with the blood oxygen saturation signals to form the training patterns of classifiers. In this study, three classifiers were implemented with performance comparisons: a SVM, a GBDT and a deep convolutional and long short-term memory network (DeepConvLSTM). The classifiers were trained to classify each segment whether it occurred sleep apnea event. The total of 38 patients was recruited in this study. They were arranged to stay at the sleep center for one night to collect the PSG data and the physiological signals of the smartwatches simultaneously during their sleep. The sleep events analyzed by registered sleep technologists were served the gold standard and a 5-fold cross validation was employed to validate the proposed classifiers. The results showed that the DeepConvLSTM classifier reached the best performance, and the average accuracy for sleep apnea event detection was 81.4%. The results have successfully validated the effectiveness of the proposed system as a home prescreening system for sleep apnea detection. In the future, we hope this system become a convenient and affordable tool that helps sleep apnea patients and provides valuable information to the doctor for arranging the hospitalization of severe patients with higher priority.
第1章 緒論 1
1.1 研究背景與動機 1
1.2 文獻討論 2
1.2.1 睡眠呼吸中止症的定義與判讀標準 3
1.2.2 睡眠呼吸中止症辨識的研究現況 4
1.3 研究目的 6
1.4 論文架構 7
第2章 實驗設置與收案工具 8
2.1 實驗設置 8
2.2 穿戴式裝置硬體架構 10
2.2.1 iNCKU智慧錶 10
2.2.2 指貼式血氧夾 13
2.3 睡眠問卷 14
2.4 系統流程 15
第3章 居家檢測睡眠呼吸中止症演算法 19
3.1 演算法流程 19
3.2 睡眠問卷系統 20
3.2.1 訊號前處理 20
3.2.2 辨識器 23
3.2.3 分類結果轉換成嚴重程度之機制 29
3.3 智慧錶的生理訊號演算法 29
3.3.1 訊號前處理 30
3.3.2 辨識器 37
3.3.3 片段辨識結果轉換成嚴重程度之機制 40
第4章 實驗結果與討論 43
4.1 資料來源與特性 43
4.2 交叉驗證方式與評估指標 45
4.3 睡眠問卷系統之不同辨識器辨識結果比較 46
4.4 智慧錶的生理訊號演算法之不同辨識器辨識結果比較 47
4.5 居家檢測病患偵測睡眠呼吸中止症的結果比較 48
第5章 結論與未來展望 52
5.1 結論 52
5.2 未來展望 53
 L. Almazaydeh, K. Elleithy, and M. Faezipour, "Obstructive sleep apnea detection using SVM-based classification of ECG signal features," in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp. 4938-4941.
 B. Amra, E. Nouranian, M. Golshan, I. Fietze, and T. Penzel, "Validation of the persian version of berlin sleep questionnaire for diagnosing obstructive sleep apnea," International Journal of Preventive Medicine, vol. 4, no. 3, pp. 334-339, 2013.
 S. Babaeizadeh, D. P. White, S. D. Pittman, and S. H. Zhou, "Automatic detection and quantification of sleep apnea using heart rate variability," Journal of Electrocardiology, vol. 43, no. 6, pp. 535-541, 2010.
 M. Bsoul, H. Minn, and L. Tamil, "Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG," IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 3, pp. 416-427, 2011.
 D. J. Buysse, C. F. Reynolds, T. H. Monk, S. R. Berman, and D. J. Kupfer, "The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research," Psychiatry Research, vol. 28, no. 2, pp. 193-213, 1989.
 M. Cheng, W. J. Sori, F. Jiang, A. Khan, and S. Liu, "Recurrent neural network based classification of ecg signal features for obstruction of sleep apnea detection," in 2017 IEEE International Conference on Computational Science and Engineering and IEEE International Conference on Embedded and Ubiquitous Computing, 2017, pp. 199-202.
 F. Chung, H. R. Abdullah, and P. Liao, "STOP-Bang Questionnaire: A Practical Approach to Screen for Obstructive Sleep Apnea," Chest, vol. 149, no. 3, pp. 631-638, 2016.
 L. J. Cronbach, "Coefficient alpha and the internal structure of tests," Psychometrika, vol. 16, no. 3, pp. 297-334, 1951.
 J. A. Fiz et al., "Acoustic analysis of snoring sound in patients with simple snoring and obstructive sleep apnoea," European Respiratory Journal, vol. 9, no. 11, p. 2365, 1996.
 R. E. Gliklich and P. C. Wang, "Validation of the snore outcomes survey for patients with sleep-disordered breathing," Archives of Otolaryngology–Head & Neck Surgery, vol. 128, no. 7, pp. 819-824, 2002.
 C. Guilleminault, R. Winkle, S. Connolly, K. Melvin, and A. Tilkian, "CYCLICAL VARIATION OF THE HEART RATE IN SLEEP APNOEA SYNDROME: Mechanisms, and Usefulness of 24 h Electrocardiography as a Screening Technique," The Lancet, vol. 323, no. 8369, pp. 126-131, 1984.
 R. Hornero, D. Alvarez, D. Abasolo, F. d. Campo, and C. Zamarron, "Utility of Approximate Entropy From Overnight Pulse Oximetry Data in the Diagnosis of the Obstructive Sleep Apnea Syndrome," IEEE Transactions on Biomedical Engineering, vol. 54, no. 1, pp. 107-113, 2007.
 M. Jayawardhana and P. d. Chazal, "Enhanced detection of sleep apnoea using heart-rate, respiration effort and oxygen saturation derived from a photoplethysmography sensor," in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2017, pp. 121-124.
 M. W. Johns, "A new method for measuring daytime sleepiness: the Epworth sleepiness scale," Sleep, vol. 14, no. 6, pp. 540-545, 1991.
 L.-G. Lindberg, H. Ugnell, and P. Å. Öberg, "Monitoring of respiratory and heart rates using a fibre-optic sensor," Medical and Biological Engineering and Computing, vol. 30, no. 5, pp. 533-537, 1992.
 A. E. Mirrakhimov, T. Sooronbaev, and E. M. Mirrakhimov, "Prevalence of obstructive sleep apnea in Asian adults: a systematic review of the literature," BMC Pulmonary Medicine, vol. 13, no. 1, p. 10, 2013.
 G. B. Moody et al., "Clinical validation of the ECG-derived respiration (EDR) technique," Computers in Cardiology, vol. 13, no. 1, pp. 507-510, 1986.
 K. Nakajima, T. Tamura, and H. Miike, "Monitoring of heart and respiratory rates by photoplethysmography using a digital filtering technique," Medical Engineering & Physics, vol. 18, no. 5, pp. 365-372, 1996.
 F. Ordóñez and D. Roggen, "Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition," Sensors, vol. 16, no. 1, p. 115, 2016.
 T. Penzel, J. W. Kantelhardt, L. Grote, J.-H. Peter, and A. Bunde, "Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea," IEEE Transactions on Biomedical Engineering, vol. 50, no. 10, pp. 1143-1151, 2003.
 L. Pigou, A. Van Den Oord, S. Dieleman, M. Van Herreweghe, and J. Dambre, "Beyond temporal pooling: Recurrence and temporal convolutions for gesture recognition in video," International Journal of Computer Vision, vol. 126, no. 2-4, pp. 430-439, 2018.
 L. D. Rosenthal and D. C. Dolan, "The Epworth Sleepiness Scale in the Identification of Obstructive Sleep Apnea," The Journal of Nervous and Mental Disease, vol. 196, no. 5, pp. 429-431, 2008.
 F. Senny, J. Destine, and R. Poirrier, "Midsagittal Jaw Movement Analysis for the Scoring of Sleep Apneas and Hypopneas," IEEE Transactions on Biomedical Engineering, vol. 55, no. 1, pp. 87-95, 2008.
 L. M. Sepulveda-Cano, E. Gil, P. Laguna, and G. Castellanos-Dominguez, "Selection of Nonstationary Dynamic Features for Obstructive Sleep Apnoea Detection in Children," EURASIP Journal on Advances in Signal Processing, vol. 2011, no. 1, p. 538314, 2011.
 A. S. M. Shamsuzzaman, B. J. Gersh, and V. K. Somers, "Obstructive Sleep ApneaImplications for Cardiac and Vascular Disease," JAMA, vol. 290, no. 14, pp. 1906-1914, 2003.
 C. Song, K. Liu, X. Zhang, L. Chen, and X. Xian, "An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model From ECG Signals," IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1532-1542, 2016.
 M. K. Uçar, M. R. Bozkurt, C. Bilgin, and K. Polat, "Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques," Neural Computing and Applications, vol. 28, no. 10, pp. 2931-2945, 2017.
 M. S. Urschitz, J. Wolff, V. von Einem, P. M. Urschitz-Duprat, M. Schlaud, and C. F. Poets, "Reference Values for Nocturnal Home Pulse Oximetry During Sleep in Primary School Children," Chest, vol. 123, no. 1, pp. 96-101, 2003.
 B. Xie and H. Minn, "Real-time sleep apnea detection by classifier combination," IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 3, pp. 469-477, 2012.
 L. Yu and H. Liu, "Feature selection for high-dimensional data: A fast correlation-based filter solution," in Proceedings of the 20th International Conference on Machine Learning, 2003, pp. 856-863.
 王拔群，李學禹，莊銘隆，黃玉書，陳彥宏，邱國樑，陳濘宏，"中文版 Epworth 嗜睡量表之信效度研究，"台灣精神醫學，第17卷，第1期，14-22頁，2003年。
 台灣睡眠障礙協會。(2019)。 [Online]. Available: http://www.sleep.org.tw/ugC_News_Detail.asp?hidID=25&hidPage1=1.
 台灣睡眠醫學學會。(2019)。 [Online]. Available: http://www.tssm.org.tw/certi.php?key=certi04.
 美國國家公路交通安全管理局。(2017)。 [Online]. Available: https://www.nhtsa.gov/risky-driving/drowsy-driving.