||Development of Obstructive Sleep Apnea Event Detection Algorithms Based on Heart Rate Variability and ECG Morphology Features
||Department of Electrical Engineering
heart rate variability
obstructive sleep apnea (OSA)
近年來，睡眠醫學已成為醫學界注目且強調的重點之一，本論文針對睡眠呼吸中止事件偵測發展兩套分析演算法。首先，基於心電圖訊號，提出一個基於心律變異性的阻塞型睡眠呼吸中止症偵測演算法，應用於阻塞型睡眠呼吸中止症的早期偵測及篩檢。此演算法萃取出HRV、EDR與CPC等等ECG參數，並利用AdaBoost Bootstrap k-dimension tree k-nearest neighbor (KDKNN) 演算法做呼吸中止事件偵測。透過此演算法，成功的區分了患者的阻塞型睡眠呼吸中止症事件。此外，本論文亦提出了基於波形特徵及與隨機森林演算法的阻塞型睡眠呼吸中止症辨識演算法。此演算法提出一套ECG波行偵測演算法，可用於偵測ECG中PQRST等波形的位置，並產生相對應的波形特徵參數，整合於以CART演算法為基礎的隨機森林演算法偵測呼吸中止事件。最後本論文成功的量測出此演算法的時間消耗，期待將來可將其應用至居家照護與阻塞型睡眠呼吸中止症的前期篩檢。
Sleep medicine has become a salient issue in health and medical industry in the past decade. This thesis proposes two electroencephalography (ECG) signal analysis algorithms for obstructive sleep apnea (OSA) detection. The first algorithm is an ECG feature-based AdaBoost Bootstrap k-dimension tree k-nearest neighbor algorithm for OSA events recognition. The proposed method processes single-lead ECG recordings to generate heart rate variability, ECG-derived respiratory signals, and cardiopulmonary coupling features for detecting the occurrence of sleep apnea, and then provides a minute-by-minute analysis of disordered breathing. The second algorithm is an ECG waveform detection method to locate the PQRST position of ECG signals. After generating the ECG morphological features from the PQRST position, a Classification and Regression Tree-based Random Forest algorithm was used to detect the OSA events. The effectiveness and time consumption of the algorithms have been successfully validated by experimental results. In the future, we hope these algorithms can be applied to home care and obstructive sleep apnea early screening.
中 文 摘 要 I
List of Tables VIII
List of Figures IX
Chapter 1. Introduction 1
1.1 Introduction and Motivation 1
1.2 Research Goals 2
1.3 Literature Review 4
1.4 Thesis Organization 5
Chapter 2. ECG Indices for OSA Detection and Their Clinical Interpretation 6
2.1. Heart Rate Variability 6
2.2. Relationship between ECG and OSA 7
2.2.1. The Impact of OSA on Heart Rate Variability 7
2.2.2. ECG Morphological Changes Caused by OSA 8
Chapter 3. Proposed HRV Feature-Based OSA Event Detection Algorithm 9
3.1. Algorithm Flow and Architecture 9
3.2. Feature Generation Process 11
3.3. Feature Selection and Transformation 16
3.4. OSA Event Recognition Using BA-KDKNN Classifier 17
Chapter 4. Obstructive Sleep Apnea Event Detection Algorithm using ECG Morphology Features and Random Forest Classifier 20
4.1. Algorithm Flow and Architecture 20
4.2. ECG Waveform Detection Process 21
4.3. Morphology-Feature Extraction Process 30
4.4. Feature Selection Process 32
4.5. Classification Process 33
Chapter 5. Experimental Results 34
5.1. Experimental Context 34
5.2. Results for HRV Feature-Based Obstructive Sleep Apnea Detection Algorithm 36
5.2.1. Parameter Determination for BA-KDKNN 36
5.2.2. Physionet Cross-Validation Results 38
5.2.3. Subject Independent System Result 39
5.3. Results for Obstructive Sleep Apnea Event Detection Algorithm using ECG Morphology Features and Random Forest Classifier 39
5.3.1. Sleep Apnea Detection Algorithm Based on ECG Morphology Features 39
5.3.2. Time Consumption of the Sleep Apnea Detection Mobile Application 42
Chapter 6. Discussion 44
6.1. HRV Feature-Based Obstructive Sleep Apnea Detection Algorithm 44
6.1.1. Performance comparison between BA-KDKNN and traditional KDKNN 44
6.1.2. Performance Comparison with other Classifiers 44
6.2. Obstructive Sleep Apnea Event Detection Algorithm using ECG Morphology Features and Random Forest Classifier 45
6.2.1. Number of CART Classifiers for Random Forests Classification 45
6.2.2. Appropriateness of Feature Selection Approaches 46
6.2.3. Computational Time Considerations 47
Chapter 7. Conclusions and Future Work 49
7.1. Conclusions 49
7.2. Future Work 50
 E. Aserinsky, “The discovery of REM sleep,” Journal of the History of the Neurosciences, vol. 5(3), pp. 213-27, 1996.
 D. B. W. W. Flemons, S. Redline, A. Pack, K. P. Strohl, J. Wheatley, T. Young, N. Douglas, P. Levy, W. McNicholas, J. Fleetham, D. White, W. Schmidt-Nowarra, D. Carley, and J. Romanjuk, “Sleep-related Breathing Disorders in Adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research. The Report of an American Academy of Sleep Medicine Task Force,” Sleep, vol. 22, pp. 667-89, 1999.
 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, pp. 1230-1235, 1993.
 W. W. Flemons, N. J. Douglas, S. T. Kuna, D. O. Rodenstein, and J. Wheatley, “Access to Diagnosis and Treatment of Patients with Suspected Sleep Apnea,” American Journal of Respiratory and Critical Care Medicine, vol. 169, pp. 668-672, 2004.
 T. Young, P. Peppard, M. Palta, K. M. Hla, L. Finn, B. Morgan, and J. Skatrud, “Population-based Study of Sleep-disordered Breathing as a Risk Factor for Hypertension,” Archives of Internal Medicine, vol. 157, pp. 1746-52, 1997.
 M. Bsoul, H. Minn, and L. Tamil, “Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG,” IEEE Trans. on Information Technology in Biomedicine, vol. 15, no. 3, pp. 416-427, 2011.
 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 Trans. on Biomedical Engineering, vol. 50, no. 6, pp. 686-96, 2003.
 K. Dingli, T. Assimakopoulos, P. K. Wraith, I. Fietze, C. Witt, and N. J. Douglas, “Spectral Oscillations of RR Intervals in Sleep Apnoea/Hypopnoea Syndrome Patients,” European Respiratory Journal, vol. 22, no. 6, pp. 943-50, 2003.
 D. Liu, X. Yang, G. Wang, J. Ma, Y. Liu, C.-K. Peng, J. Zhang, and J. Fang, “HHT Based Cardiopulmonary Coupling Analysis for Sleep Apnea Detection,” Sleep Medicine, vol. 13, pp. 503-509, 2012.
 M. O. Mendez, A. M. Bianchi, M. Matteucci, S. Cerutti, and T. Penzel, “Sleep Apnea Screening by Autoregressive Models from a Single ECG Lead,” IEEE Trans. on Biomedical Engineering, vol. 56, no. 12, pp. 2838-2850, 2009.
 T. Penzel, G. Amend, K. Meinzer, J. H. Peter, and P. von Wichert, “MESAM: a Heart Rate and Snoring Recorder for Detection of Obstructive Sleep Apnea,” Sleep, vol. 13, pp. 175-82, 1990.
 R. J. Thomas, J. E. Mietus, C. K. Peng, and A. L. Goldberger, “An Electrocardiogram-based Technique to Assess Cardiopulmonary Coupling during Sleep,” Sleep, vol. 28, pp. 1151-61, 2005.
 B. Xie and M. Hlaing, “Real-Time Sleep Apnea Detection by Classifier Combination,” IEEE Trans. on Information Technology in Biomedicine, vol. 16, pp. 469-477, 2012.
 I. Can, K. Aytemir, A. U. Demir, A. Deniz, O. Ciftci, L. Tokgozoglu, A. Oto, and A. Sahin, “P-wave Duration and Dispersion in Patients with Obstructive Sleep Apnea,” International Journal of Cardiology, vol. 133, pp. e85-e89, 2009.
 M. Jazi, B. Amra, M. Yazdchi, M. Jahangiri, F. Tabesh, and A. Gholamrezaei, “P Wave Duration and Dispersion in Holter Electrocardiography of Patients with Obstructive Sleep Apnea,” Sleep and Breathing, vol. 18, pp. 549-554, 2014.
 K.-I. Maeno, S. Kasagi, A. Ueda, F. Kawana, S. Ishiwata, M. Ohno, T. Yamaguchi, K. Narui, and T. Kasai, “Effects of Obstructive Sleep Apnea and its Treatment on Signal-Averaged P-Wave Duration in Men,” Circulation: Arrhythmia and Electrophysiology, vol. 6, pp. 287-293, 2013.
 A. Alonso-Fernández, F. García-Río, M. A. Racionero, J. M. Pino, F. Ortuño, I. Martínez, and J. Villamor, “Cardiac Rhythm Disturbances and ST-Segment Depression Episodes in Patients with Obstructive Sleep Apnea-Hypopnea Syndrome and Its Mechanisms,” Chest, vol. 127, pp. 15-22, 2005.
 D. Dursunoglu, N. Dursunoğlu, H. Evrengül, S. Özkurt, M. Kılıç, F. Fisekci, Ö. Kuru, and Ö. Delen, “QT Interval Dispersion in Obstructive Sleep Apnoea Syndrome Patients without Hypertension,” European Respiratory Journal, vol. 25, pp. 677-681, 2005.
 S. Boudaoud, H. Rix, O. Meste, C. Heneghan, and C. Brien, “Corrected Integral Shape Averaging Applied to Obstructive Sleep Apnea Detection from the Electrocardiogram,” EURASIP Journal on Advances in Signal Processing, vol. 2007, 2007.
 K. Lweesy, L. Fraiwan, N. Khasawneh, and H. Dickhaus, “New Automated Detection Method of OSA Based on Artificial Neural Networks Using P-Wave Shape and Time Changes,” Journal of Medical Systems, vol. 35, pp. 723-734, 2011.
 M. Malik, A. J. Camm, R. E. Kleiger, A. Malliani, A. J. Moss, and P. J. Schwartz, “Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use,” European Heart Journal, vol. 17, no. 3, pp. 354-381, 1996.
 N. Montano, T. G. Ruscone, A. Porta, F. Lombardi, M. Pagani, and A. Malliani, “Power Spectrum Analysis of Heart Rate Variability to Assess the Changes in Sympathovagal Balance during Graded Orthostatic Tilt,” Circulation, vol. 90, no. 4, pp. 1826-1831, 1994.
 C. Guilleminault, S. Connolly, R. Winkle, 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,” Lancet, vol. 1, pp. 126-31, 1984.
 L. Soojeong, M. Bolic, V. Z. Groza, H. R. Dajani, and S. Rajan, “Confidence Interval Estimation for Oscillometric Blood Pressure Measurements Using Bootstrap Approaches,” IEEE Trans. on Instrumentation and Measurement, vol. 60, pp. 3405-3415, 2011.
 Y. Hamamoto, S. Uchimura, and S. Tomita, “A Bootstrap Technique for Nearest Neighbor Classifier Design,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 19, pp. 73-79, 1997.
 H. Masnadi-Shirazi and N. Vasconcelos, “Cost-Sensitive Boosting,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 33, pp. 294-309, 2011.
 S.-B. Park, Y.-S. Noh, S.-J. Park, and H.-R. Yoon, “An Improved Algorithm for Respiration Signal Extraction from Electrocardiogram Measured by Conductive Textile Electrodes Using Instantaneous Frequency Estimation,” Medical and Biological Engineering and Computing, vol. 46, no. 2, pp. 147-158, 2008.
 J. Pan and W. J. Tompkins, “A Real-Time QRS Detection Algorithm,” IEEE Trans. on Biomedical Engineering, vol. BME-32, no. 3, pp. 230-236, 1985.
 S. J. Redmond and C. Heneghan, “Cardiorespiratory-based Sleep Staging in Subjects with Obstructive Sleep Apnea,” IEEE Trans. on Biomedical Engineering, vol. 53, pp. 485-496, 2006.
 A. Malliani, “The Pattern of Sympathovagal Balance Explored in the Frequency Domain,” News in Physiological Sciences, vol. 14, no. 3, pp. 111-117, 1999.
 A. H. Khandoker, M. Palaniswami, and C. K. Karmakar, “Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome from ECG Recordings,” IEEE Trans. on Information Technology in Biomedicine, vol. 13, no. 1, pp. 37-48, 2009.
 R. Kohavi and G. H. John, “Wrappers for Feature Subset Selection,” Artif. Intell., vol. 97, pp. 273-324, 1997.
 B. Yilmaz, M. H. Asyali, E. Arikan, S. Yetkin, and F. Ozgen, “Sleep Stage and Obstructive Apneaic Epoch Classification Using Single-lead ECG,” BioMedical Engineering Online, vol. 9, 2010.
 J. Zongli and D. Yi, “Improving KNN Based Text Classifications,” in 2nd International Conference on Future Computer and Communication (ICFCC), 2010, pp. V2-317-V2-321.
 S. Ertekin, J. Huang, L. Bottou, and L. Giles, “Learning on the Border: Active Learning in Imbalanced Data Classification,” Proc. ACM Conf. Information and Knowledge Management, pp. 127-136, 2007.
 N. V. Chawla, N. Japkowicz, and A. Kotcz, “Editorial: Special Issue on Learning from Imbalanced Data Sets,” SIGKDD Explor. Newsl., vol. 6, pp. 1-6, 2004.
 T. Onoda, “Overfitting of Boosting and Regularized Boosting Algorithms,” Electronics and Communications in Japan (Part III: Fundamental Electronic Science), vol. 90, no. 9, pp. 776-784, 2007.
 S. Kawaguchi and R. Nishii, “Hyperspectral Image Classification by Bootstrap AdaBoost With Random Decision Stumps,” IEEE Trans. on Geoscience and Remote Sensing, vol. 45, no. 11, pp. 3845-3851, 2007.
 K. Barta, Z. Szabó, C. Kun, C. Munkácsy, O. Bene, M. Tünde Magyar, L. Csiba, and I. Lörincz, “The Effect of Sleep Apnea on QT Interval, QT Dispersion, and Arrhythmias,” Clinical Cardiology, vol. 33, pp. E35-E39, 2010.
 L. Breiman, “Random Forests,” Machine Learning, vol. 45, pp. 5-32, 2001.
 I. K. Daskalov and Christov, II, “Automatic Detection of the Electrocardiogram T-wave End,” Medical & Biological Engineering & Computing, vol. 37, pp. 348-53, May 1999.
 A. J. Moss, W. Zareba, J. Benhorin, E. H. Locati, W. J. Hall, J. L. Robinson, P. J. Schwartz, J. A. Towbin, G. M. Vincent, M. H. Lehmann, M. T. Keating, J. W. MacCluer, and K. W. Timothy, “ECG T-Wave Patterns in Genetically Distinct Forms of the Hereditary Long QT Syndrome,” Circulation, vol. 92, pp. 2929-2934, 1995.
 H. V. Pipberger and H. L. Tanenbaum, “The P Wave, P-R Interval, and Q-T Ratio of the Normal Orthogonal Electrocardiogram,” Circulation, vol. 18, pp. 1175-1180, 1958.
 G. Wei, P. Cosman, C. C. Berry, F. Zhaoyang, and W. R. Schafer, “Automatic Tracking, Feature Extraction and Classification of C. Elegans Phenotypes,” IEEE Trans. on Biomed. Eng., vol. 51, pp. 1811-1820, 2004.
 W. Qingyao, Y. Yunming, L. Yang, and M. K. Ng, "SNP Selection and Classification of Genome-Wide SNP Data Using Stratified Sampling Random Forests," IEEE Trans. on Nanobiosci., vol. 11, pp. 216-227, 2012.
 T. Bylander, “Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates,” Machine Learning, vol. 48, pp. 287-297, 2002.
 L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Belmont, CA: Wadsworth, 1984.
 L. Pecchia, P. Melillo, M. Sansone, and M. Bracale, “Discrimination Power of Short-term Heart Rate Variability Measures for CHF Assessment,” IEEE Trans. on Inf. Technol. Biomed., vol. 15, pp. 40-46, 2011.
 [Online Available] http://www.physionet.org/physiobank/database/apnea-ecg/
 M. N. Benzadón, D. F. Ortega, J. M. Thierer, R. A. S. Torcivia, L. Aldunate, A. E. Alves de Lima, D. Navia, A. Dorsa, A. Rossi, and M. Trivi, “Comparison of the Amplitude of the P-Wave from Intracardiac Electrocardiogram Obtained by Means of a Central Venous Catheter Filled With Saline Solution to That Obtained via Esophageal Electrocardiogram,” The American journal of cardiology, vol. 98, pp. 978-981, 2006.
 E. Reynolds, G. Seda, J. Ware, A. Vinik, M. Risk, and N. Fishback, “Autonomic Function in Sleep Apnea Patients: Increased Heart Rate Variability Except during REM Sleep in Obese Patients,” Sleep and Breathing, vol. 11, pp. 53-60, 2007.