近年來，隨著睡眠時間以及睡眠品質逐漸受到重視，睡眠呼吸中止症逐漸成為了重要的議題，其後遺症會對人體造成精神上面的傷害與心血管方面的疾病，傳統診斷睡眠呼吸中止症是借助睡眠多項生理檢查紀錄患者整夜的生理訊號來評估病症的嚴重性，此方法雖然準確但是昂貴且耗時。因此，需要發展一項簡便且省時的技術來降低時間及經濟上的花費。

在此論文中，提出一個以支持向量機為基礎，藉由每分鐘心電圖訊號萃取出的特徵來辨識睡眠呼吸中止症之演算法，並將演算法的過程分成三個部分。首先，利用離散小波轉換來消除基線飄移雜訊及市電雜訊並且抓出心電圖中的峰值R，藉由找出的峰值R萃取出三種心電圖中的呼吸訊號。接著，利用此三種呼吸訊號做不同的時域與頻域分析去抓取126種具有辨識睡眠呼吸中止症能力的特徵，再將每一個不同的特徵逐一做縮放範圍之特徵正規化來降低特徵之間範圍變化的影響。最後，利用一種分類方法叫支持向量機來建立分類器模型，其支持向量機模型的參數則是利用一種交叉驗證方法叫10-fold cross-validation來選擇模型最佳參數。

利用Sleep-ECG資料庫中醫生所提供以分鐘為單位的答案與我們演算法中所產生的答案做效能評估，得到的準確率為88.29%，敏感性為92.9%，特異性為86.4%。除此之外，利用此資料庫中以受試者為單位來做效能評估，所得到的準確率為100%，敏感性為100%，特異性為100%，達到降低時間複雜度與金錢花費的效果。

Nowadays, sleep disorders become an important issue because it can adversely affect neurocognitive, cardiovascular, respiratory diseases, which subsequently induce the behavior
disorder, majority of these cases up to 85% of these cases are obstructive sleep apnea (OSA). Therefore, the study of how to diagnose, detect and treat OSA is becoming a critical
issue from both academical and medical perspective. Polysomnography (PSG) can monitor the OSA using relatively few invasive techniques. However, sleep studies are expensive and time-consuming because they require overnight evaluation at sleep laboratories with dedicated systems and attending personnel. To improve such inconveniences of exam and high cost, it is important to develop a simplified method to diagnose the OSA.

The motivation of this study is to develop an OSA detection algorithm, which uses only electrocardiogram (ECG) signal. The procedures of this algorithm include three parts. At
first, ECG signals are preprocessed by discrete wavelet transform (DWT) method in order to detect the R peck by removing the base line wander and power line noise. Based on the R peak position, the 126 features were generated from the ECG-derived respiration (EDR) signal using time domain and frequency analysis. The range scaling method can be applied afterward to normalize all features in order to minimize the effect of large range variation from the value of each feature. At last, a classification method called support vector machine (SVM) is used to classify if the subject shows OSA symptom or not in each minute. The 10-
fold cross-validation method is applied to select the best SVM parameter for classification. By combining all the classification results, one can determine if the subject is normal or apnea.

The accuracy of 88.29%, the sensitivity of 92.90% and the specificity value of 86.48%can be seen from the Apnea-ECG database using the minute by minute performance based algorithm. Beside, the accuracy of 100%, the sensitivity of 100% and the specificity value of 100% also can be seen from the database using the subject based performance. The goal of this study to reduce both the detection time and cost is accomplished.

Abstract i
1 Introduction 1
1.1 Backgrounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Previous Sleep Detection Method . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.5 Organization of The Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Background and Related works 7
2.1 Sleep Apnea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.1 Polysomnography . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.2 Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Electrocardiography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.1 ECG Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3 Apnea-ECG Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4 Introduction to Wavelet Transform . . . . . . . . . . . . . . . . . . . . . . . 17
2.4.1 Discrete Wavelet Transform . . . . . . . . . . . . . . . . . . . . . . 17
3 Algorithm Architecture and Data Preprocessing 21
3.1 Algorithm Flow Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Feature Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2.1 Noise Removed Based on Wavelet Transform . . . . . . . . . . . . . 23
3.2.2 R Peak Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2.3 EDR Signal Extraction . . . . . . . . . . . . . . . . . . . . . . . . . 28
4 Feature Extraction and Feature Postprocessing 33
4.1 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.1.1 Time Domain HRV Features . . . . . . . . . . . . . . . . . . . . . . 33
4.1.2 Frequency Domain HRV Features . . . . . . . . . . . . . . . . . . . 37
4.1.3 Time Domain EDR Feature . . . . . . . . . . . . . . . . . . . . . . 37
4.1.4 Frequency Domain EDR Feature . . . . . . . . . . . . . . . . . . . . 38
4.2 Feature Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5 Classification and Performance Estimation 41
5.1 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.1.1 Linearly Separable Binary Classification . . . . . . . . . . . . . . . . 41
5.1.2 Linearly Non-Separable Binary Classification . . . . . . . . . . . . . 44
5.2 Non-Linear Support Vector Machines . . . . . . . . . . . . . . . . . . . . . 47
5.3 Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.4 Performance Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.5 Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
6 Future Works and Conclusion 65
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
6.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

[1] L. R. Melinda Smith, M. A. and M. L. Robert Segal. (2013, May.) How much sleep do
you need? [Online]. Available: http://www.helpguide.org/life/sleeping.htm
[2] H. Attack. (2013, May) What is the electrocardiogram ECG? [Online]. Available: http:
//www.attackheart.com/treatment-methods/what-is-the-electrocardiogram-ecg.html
[3] A. Khandoker, M. Palaniswami, and C. Karmakar, “Support vector machines for automated
recognition of obstructive sleep apnea syndrome from ECG recordings,” IEEE
Transactions on Information Technology in Biomedicine (T-ITB), vol. 13, no. 1, pp. 37–
48, Jan. 2009.
[4] P. De Chazal, C. Heneghan, E. Sheridan, R. Reilly, P. Nolan, and M. O’Malley, “Automated
processing of the single-lead electrocardiogram for the detection of obstructive
sleep apnoea,” IEEE Transactions on Biomedical Engineering (T-BME), vol. 50, no. 6,
pp. 686–696, Jun. 2003.
[5] M. Mendez, A. Bianchi, M. Matteucci, S. Cerutti, and T. Penzel, “Sleep apnea screening
by autoregressive models from a single ecg lead,” IEEE Transactions on Biomedical
Engineering (T-BME), vol. 56, no. 12, pp. 2838–2850, 2009.
[6] S. Pittman, N. Tal, G. Pillar, A. Malhotra, M. Hilton, R. Fogel, N. Ayas, and D. White,
“Automated detection of obstructive sleep-disordered breathing events using peripheral
arterial tonometry and oximetry,” in IEEE International Conference on Computers in
Cardiology (CinC), vol. 27, Cambridge, MA, USA, Sep. 2000, pp. 485–488.
[7] D. Alvarez, R. Hornero, J. Marcos, F. del Campo, and M. Lopez, “Spectral analysis
of electroencephalogram and oximetric signals in obstructive sleep apnea diagnosis,” in
IEEE Engineering in Medicine and Biology Society (EMBC), vol. 59, Minneapolis, MN,
USA, Sep. 2009, pp. 400–403.
[8] T. Young, M. Palta, J. Dempsey, J. Skatrud, S. Weber, and S. Badr, “The occurrence
of sleep-disordered breathing among middle-aged adults,” New England Journal of
Medicine, vol. 328, no. 17, pp. 1230–1235, 1993.
[9] O. Marrone, G. Insalaco, M. R. Bonsignore, S. Romano, A. Salvaggio, and G. Bonsignore,
“Sleep structure correlates of continuous positive airway pressure variations
during application of an autotitrating continuous positive airway pressure machine in
patients with obstructive sleep apnea syndrome,” Journal of CHEST, vol. 121, no. 3, pp.
759–767, 2002.
[10] C. Guilleminault, J. Motta, F. Mihm, and K. Melvin, “Obstructive sleep apnea and cardiac
index,” Journal of CHEST, vol. 89, no. 3, pp. 331–334, Mar. 1986.
[11] G. Faiq and E. Colin, “Reversal of central sleep apnea using nasal cpap,” Journal of
CHEST, vol. 90, no. 2, pp. 165–171, Aug. 1986.
[12] A. L. Chesson, R. A. Ferber, J. M. Fry, M. Grigg-Damberger, K. M. Hartse, T. D. Hurwitz,
S. Johnson, G. A. Kader, M. Littner, G. Rosen, R. B. Sangal, W. Schmidt-Nowara,
and A. Sher, “The indications for polysomnography and related procedures,” Journal of
the World Association of Sleep Medicine and International Pediatric Sleep Association,
vol. 20, no. 6, pp. 423–487, May 1997.
[13] A. J. Block, P. G. Boisen, J. W. Wynne, and L. A. Hunt, “Sleep apnea, hypopnea and
oxygen desaturation in normal subjects a strong male predominance,” The new England
Journal of Medicine, vol. 300, no. 10, pp. 513–517, Mar. 1979.
[14] R. J. Thomas, “Effect of added dead space to positive airway pressure for treatment
of complex sleep-disordered breathing,” Journal of the World Association of Sleep Medicine and International Pediatric Sleep Association, vol. 6, no. 2, pp. 177 – 178,
Mar. 2005.
[15] F. Barb, J. Durn-Cantolla, and M. Snchez-de-la Torre, “Effect of continuous positive
airway pressure on the incidence of hypertension and cardiovascular events in nonsleepy
patients with obstructive sleep apnea: A randomized controlled trial,” Journal of JAMA,
vol. 307, no. 20, pp. 2161–2168, May 2012.
[16] J. Hall, Guyton and Hall Textbook of Medical Physiology, 12th ed. Jackson, MS, USA:
Saunders, Jun. 2010.
[17] G. D. Clifford, F. Azuaje, and P. McSharry, Advanced Methods and Tools for ECG Data
Analysis, 1st ed. Norwood, MA, USA: Artech House Inc., Sep. 2006.
[18] J. Loscalzo, Harrison’s Cardiovascular Medicine, 1st ed. New York, NY, USA:
McGraw-Hill Prof Med/Tech, May 2010.
[19] T. Penzel, G. Moody, R. Mark, A. Goldberger, and J. Peter, “The Apnea-ECG database,”
in IEEE International Conference on Computers in Cardiology (CinC), vol. 27, Cambridge,
MA, USA, Sep. 2000, pp. 255–258.
[20] G. Moody, R. Mark, A. Goldberger, and T. Penzel, “Stimulating rapid research advances
via focused competition: the computers in cardiology challenge,” in IEEE International
Conference on Computers in Cardiology (CinC), vol. 27, Cambridge, MA, USA, Sep.
2000, pp. 207–210.
[21] G. Furman, Z. Shinar, A. Baharav, and S. Akselrod, “Electrocardiogram derived respiration
during sleep,” in IEEE International Conference on Computers in Cardiology
(CinC), vol. 32, Lyon, France, 2005, pp. 351–354.
[22] A. Voss, R. Schroeder, S. Truebner, M. Goernig, H. R. Figulla, and A. Schirdewan,
“Comparison of nonlinear methods symbolic dynamics, detrended fluctuation,
and poincar plot analysis in risk stratification in patients with dilated cardiomyopathy,”
Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 17, no. 1, pp. 115–120,
Mar. 2007.
[23] A. Stolcke, S. Kajarekar, and L. Ferrer, “Nonparametric feature normalization for svmbased
speaker verification,” in IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP), Las Vegas, Nevada, USA, Apr. 2008, pp. 1577–1580.
[24] S. Aksoy and R. M. Haralick, “Feature normalization and likelihood-based similarity
measures for image retrieval,” Journal of Pattern Recognition Letters, vol. 22, no. 5, pp.
563–582, Jan. 2001.
[25] G. Rozenberg, T. Bck, and J. Kok, SVM Tutorial Classification, Regression and Ranking,
1st ed. Berlin, Germany: Springer Berlin Heidelberg, Jul. 2012.
[26] J. A. K. Suykens, “Nonlinear modelling and support vector machines,” in IEEE International
Conference on Instrumentation and Measurement Technology Conference
(IMTC), vol. 1, Budapest, Hungary, May 2001, pp. 287–294.
[27] S. Geisser, Predictive Inference, 1st ed. New York, NY, USA: Chapman and Hall, Dec.
1993.
[28] R. R. Picard and R. D. Cook, “Cross-validation of regression models,” Journal of the
American Statistical Association, vol. 79, no. 387, pp. 575–583, Sep. 1984.
[29] R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model
selection,” in Proceedings of the international joint conference on Artificial intelligence
(IJCAI), San Francisco, CA, USA, Dec. 1995, pp. 1137–1143.
[30] T. Hastie, R. Tibshirani, and J. Friedman., The Elements of Statistical Learning : Data
Mining, Inference, and Prediction, 2nd ed. New York, NY, USA: Springer Berlin
Heidelberg, Dec. 2009.
[31] D. Novak, K. Mucha, and T. Al-ani, “Long short-term memory for apnea detection based
on heart rate variability,” in IEEE International Conference on Engineering in Medicine
and Biology Society (EMBC), Vancouver, BC, Canada, Aug. 2008, pp. 5234–5237.
[32] S. Tan-a ram and C. Thanawattano, “Procedure to identify sleep apnea events from statistical
features,” in IEEE International Conference on Biomedical Engineering and Informatics
(BMEI), vol. 3, Yantai, China, Oct. 2010, pp. 996–1001.
[33] C. Avci, I. Delibasoglu, and A. Akbas, “Sleep apnea detection using wavelet analysis
of ECG derived respiratory signal,” in IEEE International Conference on Biomedical
Engineering (iCBEB), vol. 11, Macao, China, May. 2012, pp. 272–275.
[34] M. Mendez, D. Ruini, O. Villantieri, M. Matteucci, T. Penzel, S. Cerutti, and A. Bianchi,
“Detection of sleep apnea from surface ECG based on features extracted by an autoregressive
model,” in IEEE International Conference on Engineering in Medicine and
Biology Society (EMBC), Lyon, France, Aug. 2007, pp. 6105–6108.
[35] C. Varon, D. Testelmans, B. Buyse, J. A. Suykens, and S. Van Huffel, “Sleep apnea
classification using least-squares support vector machines on single lead ECG,” in IEEE
International Conference on Engineering in Medicine and Biology Society (EMBC), Osaka,
Japan, Jul. 2013, pp. 5029–5032.
[36] A. Burgos, A. Goi, A. Illarramendi, and J. Bermudez, “Real-time detection of apneas on
a PDA,” IEEE Transactions on Information Technology in Biomedicine (T-ITB), vol. 14,
no. 4, pp. 995–1002, Jul. 2010.
[37] B. Xie and H. Minn, “Real-time sleep apnea detection by classifier combination,” IEEE
Transactions on Information Technology in Biomedicine (T-ITB), vol. 16, no. 3, pp. 469–
477, May 2012.