Abstract
Obstructive sleep apnea (OSA) is a condition of cyclic, periodic obstruction (stenosis) of the upper respiratory tract. OSA could be associated with serious cardiovascular problems, such as hypertension, arrhythmias, hearth failure or peripheral vascular disease. Understanding the way of connection between OSA and cardiovascular diseases is important to choose proper treatment strategy. In this paper, we present a method for integrated measurements of biosignals for automatic OSA detection. The proposed method was implemented using a portable device with the application of the Support Vector Machine (SVM) classifier. The specific objective of this work is to analyze the minimum set of features for the ECG signal that could produce acceptable classification results. Those features can be further expanded using other biosignals, measured by the portable SleAp device. Additionally, the influence of the body movements and positions on measurement results with SleAp system are presented. The proposed system could help to determine the influence of OSA on the state of the cardiovascular system.
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Almazaydeh, L., Elleithy, K., Faezipour, M.: Obstructive sleep apnea detection using SVM-based classification of ECG signal features. In: Engineering in Medicine and Biology Society, pp. 4938–4941 (2012)
Azhagusundari, B., Thanamani, A.S.: Feature selection based on information gain. International J. of Innovative Technology and Exploring Engineering 18, 2278–3075 (2013)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning, 273–297 (1995)
Drager, L.F., Bortolotto, L.A., Figueiredo, A.C., Silva, B.C., Krieger, E.M., Lorenzi-Filho, G.: Obstructive sleep apnea, hypertension, and their interaction on arterial stiffness and heart remodeling. Chest 227, 1379–1386 (2007)
Gupta, S., Cepeda-Valery, B., Romero-Corral, A., Shamsuzzaman, A., Somers, V.K., Pressman, G.S.: Association between QRS duration and obstructive sleep apnea. J. of Clinical Sleep Medicine, 649–654 (2012)
Jaimchariyatam, N., Dweik, R.A., Kaw, R., Aboussouan, L.S.: Polysomnographic determinants of nocturnal hypercapnia in patients with sleep apnea. Clinical Sleep Medicine 9(3), 209–215 (2013)
Lado, M.J., Vila, X.A., Rodriguez-Linares, L., Mendez, A.J., Olivieri, D.N., Felix, P.: Detecting sleep apnea by heart rate variability anaysis: assesing the validity of databases and algorithms. J. Med. Syst. 35(4), 473–481 (2011)
McNames, J., Fraser, A., Rechtsteiner, A.: Sleep apnea classification based on frequency of heart-rate variability. Computers in Cardiolog, 749–752 (2000)
Otero, A., Vila, X., Palacios, F., Coves, F.J.: Detection of obstructive sleep apnea from the frequency analysis of heart rate variability. In: Proc. 3rd International Conference on Bio-inspired Systems and Signal Processing, Valencia, pp. 359–362 (2010)
Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. on Biomedical Engineering 32(3), 230–236 (1985)
Pitzalis, M.V., Mastropasqua, F., Massari, F., Passantino, A., Colombo, R., Mannarini, A., Forleo, C., Rizzon, P.: Effect of respiratory rate on the relationships between RR interval and systolic blood pressure fluctuations: a frequency-dependent phenomenon. Cardiovascular Research 38(2), 332–339 (1998)
Punjabi, N.M.: The epidemiology of adult obstructive sleep apnea. American Thoracic Society 5(2), 136–143 (2008)
Penzel, T., Moody, G.B., Mark, R.G.: The apnea-ECG database. Computers in Cardiology, 255–258 (2000)
Physionet (2013), http://www.physionet.org (accessed August 15, 2013)
Przystup, P., Bujnowski, A., Ruminski, J., Wtorek, J.: A multisensor detector of a sleep apnea for using at home. IEEE Xplore Digital Library, pp. 513–517 (2013)
Rendon, D.B., Rojas, J.L., Ojeda, F., Crespo, L.F., Morillo, D.S., Fernández, M.A.: Mapping the human body for vibrations using an accelerometer. In: Eng. in Med. and Biol. Society, pp. 1671–1674 (2007)
Shahar, E., Whitney, C.W., Redline, S., Lee, E.T., Newman, A.B., Nieto, F.J., O’Connor, G.T., Boland, L.L., Schwartz, J.E., Samet, J.: Sleep-disordered breathing and cardiovascular disease: cross-sectional results of the sleep heart health study. American J. of Respiratory and Critical Care Medicine 163(1), 19–25 (2011)
Shrader, M., Zywietz, C., von Einem, V., Widiger, B., Joseph, G.: Detection of sleep apnea in single channel ECGs from the PhysioNet data base. Computers in Cardiology, 263–266 (2000)
Siebert, J., Wtorek, J., Rogowski, J.: Stroke volume variability - cardiovascular response to orthostatic maneuver in patients with coronary artery diseases. Annals of the New York Academy of Science 873, 182–190 (1999)
de Silva, S., Abeyratne, U.R., Hukins, C.: Impact of gender on snore-based obstructive sleep apnea screening. Physiol. Meas. 33(4), 587–601 (2012)
Song, M.K., Ha, J.H., Ryu, S.H., Yu, J., Park, D.H.: The effect of aging and severity of sleep apnea on heart rate variability indices in obstructive sleep apnea syndrome. Psychiatry Investigation, 65–72 (2012)
Vanschoenwinkel, B., Manderick, B.: Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data. In: Winkler, J.R., Niranjan, M., Lawrence, N.D. (eds.) Machine Learning Workshop. LNCS (LNAI), vol. 3635, pp. 256–280. Springer, Heidelberg (2005)
Varon, C.: Sleep apnea classification using least-squares support vector machines on single lead ECG. In: Engineering in Medicine and Biology Society, pp. 5029–5032 (2013)
Widjaja, D., Taelman, J., Vandeput, S., Braeken, M.A.K.A., Otte, R.A., Van den Bergh, B.R.H., Van Huffel, S.: ECG-derived respiration: comparison and new measures for respiratory. Computing in Cardiology 37, 149–152 (2010)
Wtorek, J.: Electrical impedance technique in medicine. Series of Momographs-43. Publishing Office of Gdansk University of Technology (2003) (in Polish)
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Przystup, P., Bujnowski, A., Poliński, A., Rumiński, J., Wtorek, J. (2014). Sleep Apnea Detection by Means of Analyzing Electrocardiographic Signal. In: Hippe, Z., Kulikowski, J., Mroczek, T., Wtorek, J. (eds) Human-Computer Systems Interaction: Backgrounds and Applications 3. Advances in Intelligent Systems and Computing, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-319-08491-6_15
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DOI: https://doi.org/10.1007/978-3-319-08491-6_15
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