Диссертация (Разработка технологии и программно-аппаратного комплекса для исследования структуры сна человека), страница 17
Описание файла
Файл "Диссертация" внутри архива находится в папке "Разработка технологии и программно-аппаратного комплекса для исследования структуры сна человека". PDF-файл из архива "Разработка технологии и программно-аппаратного комплекса для исследования структуры сна человека", который расположен в категории "". Всё это находится в предмете "технические науки" из Аспирантура и докторантура, которые можно найти в файловом архиве МГТУ им. Н.Э.Баумана. Не смотря на прямую связь этого архива с МГТУ им. Н.Э.Баумана, его также можно найти и в других разделах. Архив можно найти в разделе "остальное", в предмете "диссертации и авторефераты" в общих файлах, а ещё этот архив представляет собой кандидатскую диссертацию, поэтому ещё представлен в разделе всех диссертаций на соискание учёной степени кандидата технических наук.
Просмотр PDF-файла онлайн
Текст 17 страницы из PDF
2015. Vol. 10,N. 2. P. 1–22.100. Remote monitoring of breathing dynamics using infrared thermography /C. B. Pereira [et al.] // Biomedical Optics Express. 2015. Vol. 6, N. 11.4378 p.101. Wu T., Blazek V., Schmitt H.J. Photoplethysmography imaging: a newnoninvasive and noncontact method for mapping of the dermal perfusionchanges // Society of Photo-Optical Instrumentation Engineers (SPIE)Conference Series. 2000. Vol. 4163. P.
62–70.133102. Automatic sleep staging based on ballistocardiographic signals recordedthrough bed sensors / M. Migliorini [et al.] // 2010 Annual InternationalConference of the IEEE Engineering in Medicine and Biology Society,EMBC’10. Vol. 2010. IEEE, 2010. P. 3273–3276.103. Unobtrusive online monitoring of sleep at home / J. Paalasmaa [et al.] //Proceedings of the Annual International Conference of the IEEE Engineeringin Medicine and Biology Society, EMBS.
2012. Vol. 2012. P. 3784–3788.104. Estimation Using Unobtrusively Measured Ballistocardiogram / D.W. Jung[et al.]. 2014. Vol. 61, N. 1. P. 131–138.105. Обнаружение и дистанционная диагностика людей за препятствиями спомощью РЛС / А.С. Бугаев [и др.] // Радиотехника. 2003. Т.
7. С. 42–47.106. Immoreev I., Ivashov S. Remote monitoring of human cardio-respiratorysystem parameters by radar and its applications // 2008 4th InternationalConference on Ultrawideband and Ultrashot Impulse Signals, UWBUSIS2008. 2008. P. 34–38.107. Анищенко Л.Н. Разработка технологии и программно-аппаратногокомплексабиорадиолокационногомониторингадвигательнойактивности, дыхания и пульса: дис. ... канд. техн. наук.Москва.2009. 183 c.108. Дистанционный контроль параметров кардиореспираторной системычеловека с помощью радиолокационных средств / А.С. Бугаев [и др.] //Биомедицинские технологии и радиоэлектроника. 2004.
Т. 10. С. 24–31.109. Биорадиолокация / Под ред. А.С. Бугаев, С.И. Ивашов, И.Я. Имореев.Москва. МГТУ им. Н.Э. Баумана, 2010. 396 c.110. Chest wall motion during tidal breathing. / A. De Groote [et al.] // Journal ofapplied physiology (Bethesda, Md. : 1985).
1997. Vol. 83, N. 5. P. 1531–1537.111. Laser monitoring of chest wall displacement / T. Kondo [et al.] // EuropeanRespiratory Journal. 1997. Vol. 10, N. 8. P. 1865–1869.134112. Kaneko H. Estimating breathing movements of the chest and abdominal wallusing a simple, newly developed breathing movement-measuring device //Respiratory care. 2014. Vol. 59, N. 7. P. 1133–9.113. Kaneko H., Horie J. Breathing Movements of the Chest and Abdominal Wallin Healthy Subjects // Respiratory Care.
2012. P. 1442–1451.114. Миняева А.В. Реакции торокального и абдоминального компонентовдыхания человека на прогрессирующую гиперкапнию и мышечнуюработуиихособености,обусловленныеположениемтелавпространстве: дис. ... канд. биол. наук. Тверь. 1996. 120 c.115. Ramachandran G., Singh M.
Three-dimensional reconstruction of cardiacdisplacement patterns on the chest wall during the P, QRS and T-segments ofthe ECG by laser speckle inteferometry // Medical & Biological Engineering& Computing. 1989. Vol. 27, N. 5. P. 525–530.116. Sleep versus wake classification from heart rate variability usingcomputational intelligence: consideration of rejection in classificationmodels.
/ A. Lewicke [et al.] // IEEE transactions on bio-medical engineering.2008. Vol. 55, N. 1. P. 108–118.117. Sleep stage and obstructive apneaic epoch classification using single-leadECG. / B. Yilmaz [et al.] // Biomedical engineering online. 2010. Vol. 9.P. 39.118. Towards automated sleep state estimation using a holter-oximeter / C.P. Chua[et al.] // Annual International Conference of the IEEE Engineering inMedicine and Biology - Proceedings. 2007. Vol. 2007. P.
3998–4001.119. Adnane M., Jiang Z. Automatic sleep-wake stages classifier based on ECG //Iccas-Sice. Vol. 2. 2009. P. 493–498.120. Heart Rate Spectrum Analysis for the automated Classification of SleepStages / S. Canisius [et al.] // World Congress on Medical Physics andBiomedical Engineering. 2009. P. 782–785.135121. Mendez M., Matteucci M. Sleep staging from Heart Rate Variability:time-varying spectral features and Hidden Markov Models // InternationalJournal of Biomedical Engineering and Technology.
2010. Vol. 3. P. 246–263.122. Adnane M., Jiang Z., Yan Z. Sleep-wake stages classification and sleepefficiency estimation using single-lead electrocardiogram // Expert Systemswith Applications. 2012. Vol. 39, N. 1. P. 1401–1413.123. Karlen W. Adaptive Wake and Sleep Detection for Wearable Systems. Ph.D.thesis / Ecole Polytechnique. 2009.
165 p.124. Automatic sleep staging from ventilator signals in non-invasive ventilation /C. Sady [et al.] // Computers in Biology and Medicine. 2013. Vol. 43, N. 7.P. 833–839.125. Automatic sleep staging using empirical mode decomposition, discretewavelet transform, time-domain, and nonlinear dynamics features of heart ratevariability signals / F. Ebrahimi [et al.] // Computer Methods and Programsin Biomedicine. 2013. Vol.
112, N. 1. P. 47–57.126. Signal processing and feature extraction for sleep evaluation in wearabledevices / A.M. Bianchi [et al.] // Annual International Conference of theIEEE Engineering in Medicine and Biology - Proceedings. 2006. Vol. 1.P. 3517–3520.127. Bianchi A.M., Mendez M.O. Methods for heart rate variability analysisduring sleep // 2013 35th Annual International Conference of the IEEEEngineering in Medicine and Biology Society (EMBC). Vol. 2013. IEEE,2013. P.
6579–6582.128. Sleep stages classification based on heart rate variability and random forest /M. Xiao [et al.] // Biomedical Signal Processing and Control. 2013. Vol. 8,N. 6. P. 624–633.129. An ECG-based algorithm for the automatic identification of autonomicactivations associated with cortical arousal. / M. Basner [et al.] // Sleep. 2007.Vol. 30, N. 10. P. 1349–61.136130.
Assessing sleep architecture and continuity measures through the analysis ofheart rate and wrist movements recordings in healthy subjects: comparisonwith results based on polysomnography / A. Muzet [et al.] // Sleep Medicine.2016.131. Sleep Staging Based on Autonomic Signals : A Multi-Center ValidationStudy / J. Hedner [et al.] // Journal of clinical sleep medicine. 2011. Vol. 7,N. 3. P. 301–306.132. Isa S.
M., Wasito I., Arymurthy A. M. Kernel Dimensionality Reductionon Sleep Stage Classification using ECG Signal // International Journal ofComputer Science Issues. 2011. Vol. 8, N. 4. P. 115–123.133. Devot S., Dratwa R., Naujokat E. Sleep/wake detection based oncardiorespiratory signals and actigraphy // 2010 Annual InternationalConference of the IEEE Engineering in Medicine and Biology Society,EMBC’10.
2010. Vol. 2010. P. 5089–5092.134. Domingues A., Paiva T., Sanches J. M. Hypnogram and sleep parametercomputation from activity and cardiovascular data // IEEE Transactions onBiomedical Engineering. 2014. Vol. 61, N. 6. P. 1711–1719.135. Evolutionary Selection of Features for Neural Sleep/Wake Discrimination /P. Dürr [et al.] // Journal of Artificial Evolution and Applications. 2009. Vol.2009. P. 1–10.136. Karlen W., Mattiussi C., Floreano D.
Improving actigraph sleep/wakeclassification with cardio-respiratory signals // 2008 30th Annual InternationalConference of the IEEE Engineering in Medicine and Biology Society.Vol. 2008. IEEE, 2008. P. 5262–5265.137. Karlen W., Mattiussi C., Floreano D. Adaptive sleep/wake classification basedon cardiorespiratory signals for wearable devices // Conference Proceedings- IEEE Biomedical Circuits and Systems Conference Healthcare Technology,BiOCAS2007. 2007. P. 203–206.137138.
Sleep and Wake Classification With ECG and Respiratory Effort Signals /W. Karlen [et al.]. 2009. Vol. 3, N. 2. P. 71–78.139. An evaluation of cardiorespiratory and movement features with respect tosleep-stage classification / T. Willemen [et al.] // IEEE Journal of Biomedicaland Health Informatics. 2014. Vol. 18, N. 2. P.
661–669.140. Probabilistic cardiac and respiratory based classification of sleep and apneicevents in subjects with sleep apnea / T. Willemen [et al.] // PhysiologicalMeasurement. 2015. Vol. 36, N. 10. P. 2103–2118.141. Respiration during sleep in normal man. / N. J. Douglas [et al.] // Thorax.1982. Vol. 37, N. 11.
P. 840–844.142. Cardiovascular and respiratory dynamics during normal and pathologicalsleep / T. Penzel [et al.] // Chaos. 2007. Vol. 17, N. 1.143. Breathing during REM and non-REM sleep: Correlated versus uncorrelatedbehaviour / J.W. Kantelhardt [et al.] // Physica A: Statistical Mechanics andits Applications. 2003. Vol. 319. P. 447–457.144. Redline S. Sleep Heart Health Study. URL: http://sleepdata.org/datasets/shhs.145. Methods for obtaining and analyzing unattended polysomnography data for amulticenter study. Sleep Heart Health Research Group. / S.