Suggestions for Alternative System Designs for Polysomnography Device

Yıl 2017, Cilt: 5 Sayı: 3, 1 - 12, 03.03.2017

Arş. Gör. Muhammed Kürşad Uçar

Öz

Abstract

Sleep disorders are one of the era's most important diseases. Like any major disease, the quality of the diagnostic process directly affects the treatment process. With theprogress of technology, diagnostic methods are increasing and developing day by day. Sleep staging and respiratory scoring is vital for diagnose obstructive sleep apnea. Therefore, in the study, the sleep staging studies in the literature were examined and presented in tables. After that, there are suggestions for an ideal respiratory scoring. For acomplete diagnosis, practical, portable house hold use suitable systems have been examined. The aim of the study is to examine the portable devices developed for OSA diagnosis and to propose the system design which can be used in practice. The disadvantages of the systems developed for this purpose have been revealed, and then there commendations for the ideal system have been made.

Anahtar Kelimeler

Polysomnography Device, Obstructive Sleep Apnea, Portable System, Sleep Staging, Respiratory Scoring

Polisomnografi Cihazına Alternatif Sistem Tasarımları için Öneriler

Öz

Öz

Uyku bozukluğu çağın en önemli hastalıklarından biridir. Her önemli hastalık gibi teşhis sürecisin kalitesi, tedavi sürecini doğrudan etkilemektedir. Teknolojinin ilerlemesi ile birlikte teşhis yöntemleri gün geçtikçe artmakta ve gelişmektedir. Obstrüktif Uyku Apne teşhisi için uyku evreleme ve solunum skorlama hayati öneme sahiptir. Bu yüzden çalışmada, literatürde yapılan uyku evreleme çalışmaları incelenmiş ve tablolar halinde sunulmuştur. Ardında ideal bir solunum skorlama için önerilerde bulunulmuştur.Tam bir teşhis için pratik, taşınabilir ev kullanıma uygun sistemler incelenmiştir. Çalışmanın amacı, OSA teşhisi için geliştirilen taşınabilir cihazların incelenmesi ve pratikte kulanılabilecek sistem tasarımı için öneriler sunmaktır. Bu amaçla geliştirilen sistemlerin dezavantajları ortaya konmuş, ardından ideal bir sistem için önerilerde bulunulmuştur.

Anahtar Kelimeler

Polisomnografi Cihazı, Obstrüktif Uyku Apne, Taşınabilir sistem, Uyku Evreleme, Solunum Skorlama

Kaynakça

• Kaynakça 1.B. Yilmaz, M. H. Asyali, E. Arikan, S. Yetkin, and F. Ozgen, “Sle-ep stage and obstructive apneaic epoch classification using single-lead ECG.,” Biomed. Eng. Online, vol. 9, no. 1, p. 39, Jan. 2010.[ 2.H. Kaynak, Uyku Uyuyamamak mı Uyanamamak mı? Doğan Kitap,2008. 3.P. Respironics, Practical aspects of sleep diagnostic. PhilipsElectronics N.V, 2009. 4.R. JUNG and W. KUHLO, “NEUROPHYSIOLOGICAL STUDIESOF ABNORMAL NIGHT SLEEP AND THE PICKWICKIANSYNDROME.,” Prog. Brain Res., vol. 18, pp. 140–59, 1965. 5.C. Guilleminault, F. Eldridge, and W. C. Dement, “Insomnia, nar-colepsy, and sleep apneas.,” Bull. Physiopathol. Respir. (Nancy).,vol. 8, no. 5, pp. 1127–38. 6.T. Young, P. E. Peppard, and D. J. Gottlieb, “Epidemiology of obs-tructive sleep apnea: a population health perspective.,” Am. J. Res-pir. Crit. Care Med., vol. 165, no. 9, pp. 1217–39, May 2002. 7.T. I. Morgenthaler, V. Kagramanov, V. Hanak, and P. A. Decker,“Complex sleep apnea syndrome: is it a unique clinical syndrome?,”Sleep, vol. 29, no. 9, pp. 1203–9, Sep. 2006. 8.M. O. Mendez, A. M. Bianchi, M. Matteucci, S. Cerutti, and T. Pen-zel, “Sleep Apnea Screening by Autoregressive Models From a Sin-gle ECG Lead,” IEEE Trans. Biomed. Eng., vol. 56, no. 12, pp.2838–2850, Dec. 2009. 9.G. Angius and L. Raffo, “Cardiovascular disease and sleep apno-ea: A wearable device for PPG acquisition and research aims,” inComputing in Cardiology (CinC), 2012, pp. 513–516. 10.World Health Organization Regional Office for Europe EuropeanCentre for Environment and Health Bonn Office, “WHO technicalmeeting on sleep and health,” Bonn Germany, 2004. 11.R. Wolk, A. S. Gami, A. Garcia-Touchard, and V. K. Somers, “Sle-ep and Cardiovascular Disease,” Curr. Probl. Cardiol., vol. 30, no.12, pp. 625–662, 2005. 12.E. A. Iliescu et al., “Quality of sleep and health-related quality oflife in haemodialysis patients.,” Nephrol. Dial. Transplant, vol. 18,no. 1, pp. 126–32, Jan. 2003. 13.J. M. Rodrigues, M. H. Estevao, J. L. Malaquias, P. Santos, G. Gou-veia, and J. B. Simoes, “SleepAtHome - Portable Home Based Systemfor Pediatric Sleep Apnoea Diagnosis,” in 2007 IEEE Internatio-nal Conference on Portable Information Devices, 2007, pp. 1–4. 14.K. Šušmáková, “Human Sleep and Sleep EEG,” Meas. Sci. Rev.,vol. 4, no. 2, 2004. 15.E. F. Haponik, P. L. Smith, D. A. Meyers, and E. R. Bleecker, “Eva-luation of sleep-disordered breathing. Is polysomnography neces-sary?,” Am. J. Med., vol. 77, no. 4, pp. 671–7, Oct. 1984. 16.M. Drinnan, J. Allen, P. Langley, and A. Murray, “Detection of sle-ep apnoea from frequency analysis of heart rate variability,” in Com-puters in Cardiology 2000. Vol.27 (Cat. 00CH37163), 2000, pp.259–262. 17.J. V. Marcos, R. Hornero, D. Álvarez, F. del Campo, and C. Zamar-rón, “Assessment of four statistical pattern recognition techniquesto assist in obstructive sleep apnoea diagnosis from nocturnal oxi-metry,” Med. Eng. Phys., vol. 31, no. 8, pp. 971–978, Oct. 2009. 18.P. Varady, T. Micsik, S. Benedek, and Z. Benyo, “A novel method forthe detection of apnea and hypopnea events in respiration signals,”IEEE Trans. Biomed. Eng., vol. 49, no. 9, pp. 936–942, Sep. 2002. 19.C. C. R. Sady, U. S. Freitas, A. Portmann, J.-F. Muir, C. Letellier,and L. A. Aguirre, “Automatic sleep staging from ventilator signalsin non-invasive ventilation,” Comput. Biol. Med., vol. 43, no. 7, pp.833–839, Aug. 2013. 20.M. Chan, D. Estève, J.-Y. Fourniols, C. Escriba, and E. Campo,“Smart wearable systems: Current status and future challenges,”Artif. Intell. Med., vol. 56, no. 3, pp. 137–156, Nov. 2012. 21.A. Kales and A. Rechtschaffen, A Manual of Standardized Termi-nology Techniques and Scoring System for Sleep Stages of HumanSubjects. Los Angeles: Bethesda, Md., U. S. National Institute of Neu-rological Diseases and Blindness, Neurological Information Net-work, 1968. 22.W. Dement and N. Kleitman, “Cyclic variations in EEG during sle-ep and their relation to eye movements, body motility, and drea-ming,” Electroencephalogr. Clin. Neurophysiol., vol. 9, no. 4, pp.673–690, 1957. 23.C. Pollak, M. J. Thorpy, J. Yager, and M. J. Thorpy, The encyclo-pedia of sleep and sleep disorders. Facts on File, 2010. 24.C. Iber, S. Ancoli-Israel, A. L. Chesson, and S. F. Quan, The AASMmanual for the scoring of sleep and associated events: rules, ter-minology and technical specifications, 1st ed. We. American Aca-demy of Sleep Medicine, 2007. 25.“10/20 positioning | DIY tDCS.” [Online]. Available:http://www.diytdcs.com/tag/1020-positioning/. [Accessed: 15-Feb-2017]. 26.AASM, “Sleep-related breathing disorders in adults: recommenda-tions for syndrome definition and measurement techniques in cli-nical research. The Report of an American Academy of Sleep Me-dicine Task Force.,” Sleep, vol. 22, no. 5, pp. 667–89, Aug. 1999. 27.S. Khalighi, T. Sousa, G. Pires, and U. Nunes, “Automatic sleep sta-ging: A computer assisted approach for optimal combination of fea-tures and polysomnographic channels,” Expert Syst. Appl., vol. 40,no. 17, pp. 7046–7059, 2013. 28.S. Eyal, Y. Dagan, and A. Baharav, “Sleep in the Cloud: On Howto Use Available Heart Rate Monitors to Track Sleep and Impro-ve Quality of Life,” in Computing in Cardiology (CinC), 2012, pp.329–332. 29.M. Ronzhina, O. Janoušek, J. Kolářová, M. Nováková, P. Honzík,and I. Provazník, “Sleep scoring using artificial neural networks,”Sleep Med. Rev., vol. 16, no. 3, pp. 251–263, Jun. 2012. 30.I. Hermawan, M. S. Alvissalim, M. I. Tawakal, and W. Jatmiko, “Anintegrated sleep stage classification device based on electrocardiog-raph signal,” in Advanced Computer Science and InformationSystems (ICACSIS), 2012 International Conference on, 2012, pp. 37–41. 31.A. Ersöz and S. Özşen, “UYKU EEG SİNYALİNİN YAPAY SİNİRAĞ MODELİ İLE SINIFLANDIRILMASI,” in Elektrik-Elektronikve Bilgisayar Sempozyumu, 2011, pp. 298–301. 32.W. Zhao, J. Yan, B. Hu, H. Ma, and L. Liu, “Advanced measure se-lection in automatic NREM discrimination based on EEG,” in ICP-CA10 - 5th International Conference on Pervasive Computing andApplications, 2010, pp. 26–31. 33.P. de Chazal, E. O’Hare, N. Fox, and C. Heneghan, “Assessmentof sleep/wake patterns using a non-contact biomotion sensor,” in2008 30th Annual International Conference of the IEEE Enginee-ring in Medicine and Biology Society, 2008, vol. 2008, pp. 514–517. 34.S. J. Redmond and C. Heneghan, “Cardiorespiratory-Based SleepStaging in Subjects With Obstructive Sleep Apnea,” IEEE Trans. Bio-med. Eng., vol. 53, no. 3, pp. 485–496, Mar. 2006. 35.N. A. Sazonova, E. E. Sazonov, B. Tan, S. A. C. Schuckers, and CHI-ME study group, “Sleep State Scoring in Infants from Respiratoryand Activity Measurements,” in 2006 International Conference ofthe IEEE Engineering in Medicine and Biology Society, 2006, vol.1, pp. 2462–2465. 36.A. T. Lewicke, E. S. Sazonov, M. J. Corwin, and S. A. C. Schuckers,“Reliable determnination of sleep versus wake from heart rate va-riability using neural networks,” in Proceedings. 2005 IEEE Inter-national Joint Conference on Neural Networks, 2005., vol. 4, pp.2394–2399. 37.K. Watanabe, T. Manabe, and T. Yoshikawa, “Estimation of sleepstages based on heart rate fluctuation and body movement,” in SICE2004 Annual Conference, 2004, pp. 2153–2156. 38.R. B. Berry et al., “Rules for scoring respiratory events in sleep: up-date of the 2007 AASM Manual for the Scoring of Sleep and Asso-ciated Events. Deliberations of the Sleep Apnea Definitions Task For-ce of the American Academy of Sleep Medicine.,” J. Clin. Sleep Med.,vol. 8, no. 5, pp. 597–619, Oct. 2012. 39.R. B. Berry et al., “The AASM Manual for the Scoring of Sleep andAssociated Events: Rules, Terminology and Technical Specificati-ons,” 2016. 40.T. T. Society, “Türk toraks derneği̇obstrükti̇f uyku apne sendromutani ve tedavi̇uzlaşı raporu,” Türk Toraks Derg., vol. 13, no. De-cember, p. 66, 2012. 41.M. K. Uçar, M. R. Bozkurt, C. Bilgin, and K. Polat, “Automatic de-tection of respiratory arrests in OSA patients using PPG and mac-hine learning techniques,” Neural Comput. Appl., pp. 1–15, Oct. 2016. 42.M. K. Uçar, M. R. Bozkurt, C. Bilgin, and K. Polat, “Automatic sle-ep staging in obstructive sleep apnea patients using photoplethys-mography, heart rate variability signal and machine learning tech-niques,” Neural Comput. Appl., pp. 1–16, May 2016. 43.L. G. Doroshenkov, V. A. Konyshev, and S. V Selishchev, “Classi-fication of human sleep stages based on EEG processing using hid-den Markov models,” Med. Tekh., no. 1, pp. 24–8. 44.V. P. Jain, V. D. Mytri, V. V. Shete, and B. K. Shiragapur, “Sleepstages classification using wavelettransform & neural network,” inProceedings - IEEE-EMBS International Conference on Biomedi-cal and Health Informatics: Global Grand Challenge of Health In-formatics, BHI 2012, 2012, pp. 71–74. 45.J. Virkkala, J. Hasan, A. Värri, S.-L. Himanen, and M. Härmä, “Theuse of two-channel electro-oculography in automatic detection ofunintentional sleep onset,” J. Neurosci. Methods, vol. 163, no. 1,pp. 137–144, Jun. 2007. 46.J. Zhang, D. Chen, J. Zhao, M. He, Y. Wang, and Q. Zhang, “RASS:A Portable Real-time Automatic Sleep Scoring System,” in 2012 IEEE33rd Real-Time Systems Symposium, 2012, pp. 105–114. 47.Y. Zhang, C. C. Y. Poon, C. Chan, M. W. W. Tsang, and K. Wu, “AHealth-Shirt using e-Textile Materials for the Continuous and Cuff-less Monitoring of Arterial Blood Pressure,” in 2006 3rd IEEE/EMBSInternational Summer School on Medical Devices and Biosensors,2006, pp. 86–89. 48.N. Butkov, T. L. Lee-Chiong, and American Association of Sleep Tech-nologists., Fundamentals of sleep technology. Wolters Kluwer He-alth/Lippincott Williams & Wilkins, 2007. 49.Y. G. Lim, K. K. Kim, and K. S. Park, “ECG Recording on a BedDuring Sleep Without Direct Skin-Contact,” IEEE Trans. Biomed.Eng., vol. 54, no. 4, pp. 718–725, Apr. 2007. 50.K. Wu and Y. Zhang, “Contactless and continuous monitoring of he-art electric activities through clothes on a sleeping bed,” in 2008International Conference on Technology and Applications in Bio-medicine, 2008, pp. 282–285. 51.W. Verkruysse, L. O. Svaasand, and J. S. Nelson, “Remote plethys-mographic imaging using ambient light.,” Opt. Express, vol. 16, no.26, pp. 21434–45, Dec. 2008. 52.J. R. SHAMBROOM, S. E. FÁBREGAS, and J. JOHNSTONE, “Va-lidation of an automated wireless system to monitor sleep in healthyadults,” J. Sleep Res., vol. 21, no. 2, pp. 221–230, Apr. 2012. 53.R. Spaulding, D. Stevens, and S. E. Velasquez, “Experience with te-lehealth for sleep monitoring and sleep laboratory management,”J. Telemed. Telecare, vol. 17, no. 7, pp. 346–349, Oct. 2011. 54.R. Parikh, M. N. TouVelle, H. Wang, and S. N. Zallek, “Sleep Te-lemedicine: Patient Satisfaction and Treatment Adherence,” Tele-med. e-Health, vol. 17, no. 8, pp. 609–614, Oct. 2011. 55.J. Shin, Y. Chee, and K. W. Park, “Long-term Sleep MonitoringSystem and Long-term Sleep Parameters using Unconstrained Met-hod,” in IntI. Special Topic Con! on Info. Tech., 2006. 56.W. K, T. Tasaki, T. Nemoto, K. Yamakoshi, and W. Chen, “Deve-lopment of biometry system in the sleep by pillow cuff installed onthe occiput,” Japanese Soc. Med. BioI. Eng., vol. 41, no. 1, p. 168,2003. 57.D. C. Mack, D. C. Mack, J. T. Patrie, P. M. Suratt, R. A. Felder, andM. Alwan, “Development and Preliminary Validation of Heart Rateand Breathing Rate Detection Using a Passive, Ballistocardiography-Based Sleep Monitoring System,” IEEE Trans. Inf. Technol. Bio-med., vol. 13, no. 1, pp. 111–120, Jan. 2009. 58.R. J. Thomas, J. E. Mietus, C.-K. Peng, and A. L. Goldberger, “Anelectrocardiogram-based technique to assess cardiopulmonarycoupling during sleep.,” Sleep, vol. 28, no. 9, pp. 1151–61, Sep. 2005. 59.J. Mass, S. Fabregas, R. Kopynec, D. Haswell, R. Fortgang, and J.Shambroom, “Putting sleep to the test: a collegiate sleep study,”Sleep, vol. 33, p. A77, 2010. 60.S. Herscovici, A. Pe’er, S. Papyan, and P. Lavie, “Detecting REMsleep from the finger: an automatic REM sleep algorithm based onperipheral arterial tone (PAT) and actigraphy,” Physiol. Meas., vol.28, no. 2, pp. 129–140, Feb. 2007. 61.S. Güneş, K. Polat, and Ş. Yosunkaya, “Efficient sleep stage recog-nition system based on EEG signal using k-means clustering basedfeature weighting,” Expert Syst. Appl., vol. 37, no. 12, pp.7922–7928, 2010. 62.A. Hossen, B. Al Ghunaimi, and M. Hassan, “The importance of thevery low frequency power of heart rate variability in screening ofpatients with obstructive sleep Apnea,” in 2011 IEEE Symposiumon Industrial Electronics and Applications, 2011, pp. 638–642. 63.J. H. Jae Hyuk Shin, Y. J. Young Joon Chee, D.-U. Do-Un Jeong,and K. S. Kwang Suk Park, “Nonconstrained Sleep MonitoringSystem and Algorithms Using Air-Mattress With Balancing Tube Met-hod,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 1, pp. 147–156,Jan. 2010. 64.A. Yilmaz and T. Dundar, “Home recording for pre-phase sleep ap-nea diagnosis by Holter recorder using MMC memory,” in 2010IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems, 2010, pp. 126–129. 65.A. Burgos, A. Goñi, A. Illarramendi, and J. Bermudez, “Real-TimeDetection of Apneas on a PDA,” IEEE Trans. Inf. Technol. Biomed.,vol. 14, no. 4, pp. 995–1002, Jul. 2010. 66.K. H. Ang, Y. Xu, and A. H. Khandoker, “Simulink-based sleep ap-nea screening model for portable diagnosis,” in 2009 Internatio-nal Conference on Intelligent Sensors, Sensor Networks and Infor-mation Processing (ISSNIP), 2009, pp. 201–206. 67.A. Nobuyuki, N. Yasuhiro, T. Taiki, Y. Miyae, M. Kiyoko, and H. Te-rumasa, “Trial of measurement of sleep apnea syndrome with so-und monitoring and SpO2 at home,” in 2009 11th International Con-ference on e-Health Networking, Applications and Services (Health-com), 2009, pp. 66–69. 68.A. Zaffaroni, P. de Chazal, C. Heneghan, P. Boyle, P. R. Mppm, andW. T. McNicholas, “SleepMinder: An innovative contact-free de-vice for the estimation of the apnoea-hypopnoea index,” in 2009 An-nual International Conference of the IEEE Engineering in Medici-ne and Biology Society, 2009, vol. 2009, pp. 7091–9094. 69.A. Otero, S. F. Dapena, P. Felix, J. Presedo, and M. Tarasco, “Alow cost screening test for Obstructive Sleep Apnea that can be per-formed at the patient’s home,” in 2009 IEEE International Sympo-sium on Intelligent Signal Processing, 2009, pp. 199–204. 70.Yung-Fu Chen, Jen-Ho Chen, Liang-Wen Hung, Yen-Ju Lin, andChih-Jaan Tai, “Diagnosis and prediction of patients with severeobstructive apneas using support vector machine,” in 2008 Inter-national Conference on Machine Learning and Cybernetics, 2008,pp. 3236–3241. 71.W. Xu, X. Liu, and Z. Pan, “Definition of Sleep Apnea Event by OneMinute HRV Spectrum Analysis,” in 2008 2nd International Con-ference on Bioinformatics and Biomedical Engineering, 2008, pp.2292–2294. 72.G. Angius and L. Raffo, “A sleep apnoea keeper in a wearable de-vice for Continuous detection and screening during daily life,” in2008 Computers in Cardiology, 2008, pp. 433–436. 73.K. M. Chang, M. S. Shih, T. H. Lan, and L. W. Hang, “BluetoothBased Wireless Sleep Remote Monitoring System,” in 2007 6th In-ternational Special Topic Conference on Information TechnologyApplications in Biomedicine, 2007, pp. 309–312. 74.K. M. Al-Ashmouny, H. M. Hamed, and A. A. Morsy, “FPGA-ba-sed Sleep Apnea Screening Device for Home Monitoring,” in 2006International Conference of the IEEE Engineering in Medicine andBiology Society, 2006, vol. 1, pp. 5948–5951.7 5.A. S. Wakwella, U. R. Abeyratne, and C. Hukins, “Snore basedsystems for the diagnosis of apnoea: a novel feature and its recei-ver operating characteristics for a full-night clinical database,” inIEEE International Workshop on Biomedical Circuits and Systems,2004., pp. 147–150. 76.F. Barbé, M. A. Quera-Salva, J. de Lattre, P. Gajdos, and A. G. Agus-tí, “Long-term effects of nasal intermittent positive-pressure ventila-tion on pulmonary function and sleep architecture in patients with neu-romuscular diseases.,” Chest, vol. 110, no. 5, pp. 1179–83, Nov. 1996. 77.M. W. Elliott, A. K. Simonds, M. P. Carroll, J. A. Wedzicha, and M.A. Branthwaite, “Domiciliary nocturnal nasal intermittent positi-ve pressure ventilation in hypercapnic respiratory failure due to chro-nic obstructive lung disease: effects on sleep and quality of life.,”Thorax, vol. 47, no. 5, pp. 342–8, May 1992.