Relevant bibliographies by topics / Apnea detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
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Academic literature on the topic 'Apnea detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Apnea detection"

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Dickhaus, Hartmut, and Christoph Maier. "Confounding Factors in ECG-based Detection of Sleep-disordered Breathing." Methods of Information in Medicine 57, no. 03 (2018): 146–51. http://dx.doi.org/10.3414/me17-02-0005.

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Summary Objectives: To assess the relevance of various potential confounding factors (comorbidities, obesity, body position, ECG lead, respiratory event type and sleep stage) on the detectability of sleep-related breathing disorders from the ECG. Methods: A set of 140 simultaneous recordings of polysomnograms and 8-channel Holter ECGs taken from 121 patients with suspected sleep related breathing disorders is stratified with respect to the named factors. Minute-by-minute apnea detection performance is assessed using separate receiver operating characteristics curves for each of the subgroups.
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Korompili, Georgia, Lampros Kokkalas, Stelios A. Mitilineos, Nicolas-Alexander Tatlas, and Stelios M. Potirakis. "Detecting Apnea/Hypopnea Events Time Location from Sound Recordings for Patients with Severe or Moderate Sleep Apnea Syndrome." Applied Sciences 11, no. 15 (2021): 6888. http://dx.doi.org/10.3390/app11156888.

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The most common index for diagnosing Sleep Apnea Syndrome (SAS) is the Apnea-Hypopnea Index (AHI), defined as the average count of apnea/hypopnea events per sleeping hour. Despite its broad use in automated systems for SAS severity estimation, researchers now focus on individual event time detection rather than the insufficient classification of the patient in SAS severity groups. Towards this direction, in this work, we aim at the detection of the exact time location of apnea/hypopnea events. We particularly examine the hypothesis of employing a standard Voice Activity Detection (VAD) algorit
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Thybo, J., A. N. Olesen, M. Olsen, et al. "0451 Fully Automatic Detection of Sleep Disordered Breathing Events." Sleep 43, Supplement_1 (2020): A172—A173. http://dx.doi.org/10.1093/sleep/zsaa056.448.

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Abstract Introduction Evaluation of sleep apnea involves manual annotation of Polysomnography (PSG) file, a time-consuming process subject to interscorer variations. The DOSED algorithm has been shown to be helpful in detecting Central Sleep Apnea (CSA), Obstructive Sleep Apnea (OSA), and Hypopnea when merged into a single event type. This work uses a modified version of DOSED capable of detecting each event type separately. Methods The network consists of 3 blocks of 1D convolutional layers followed by 6 blocks of 2D convolutional layers. The network has 2 classification layers, one determine
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McClure, Kristin, Brett Erdreich, Jason H. T. Bates, Ryan S. McGinnis, Axel Masquelin, and Safwan Wshah. "Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning." Sensors 20, no. 22 (2020): 6481. http://dx.doi.org/10.3390/s20226481.

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Rapid assessment of breathing patterns is important for several emergency medical situations. In this research, we developed a non-invasive breathing analysis system that automatically detects different types of breathing patterns of clinical significance. Accelerometer and gyroscopic data were collected from light-weight wireless sensors placed on the chest and abdomen of 100 normal volunteers who simulated various breathing events (central sleep apnea, coughing, obstructive sleep apnea, sighing, and yawning). We then constructed synthetic datasets by injecting annotated examples of the vario
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Patil, Dipti, V. M. Wadhai, Snehal Gujar, Karishma Surana, Prajakta Devkate, and Shruti Waghmare. "APNEA Detection on Smart Phone." International Journal of Computer Applications 59, no. 7 (2012): 15–19. http://dx.doi.org/10.5120/9559-4022.

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Bell, Charlotte, Rick Dubose, John Seashore, et al. "Infant apnea detection after herniorrhaphy." Journal of Clinical Anesthesia 7, no. 3 (1995): 219–23. http://dx.doi.org/10.1016/0952-8180(95)00001-x.

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Bell, Charlotte, Rick Dubose, John Seashore, et al. "Infant apnea detection after herniorrhaphy." Journal of Clinical Anesthesia 7, no. 8 (1995): 715. http://dx.doi.org/10.1016/0952-8180(95)90056-x.

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Josten, Klaus U., and Johanniter-Krankenhau S. Bonn. "Impedance Pneumography for Apnea Detection." Critical Care Medicine 15, no. 10 (1987): 990. http://dx.doi.org/10.1097/00003246-198710000-00025.

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Bell, C., R. Duboee, J. Seashore, R. Touloukian, T. Oh, and C. Hughes. "INFANT APNEA DETECTION AFTER HERNIORRHAPHY." Anesthesiology 75, no. 3 (1991): A1047. http://dx.doi.org/10.1097/00000542-199109001-01046.

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BELL, CHARLOTTE, RICK DUBOSE, JOHN SEASHORE, et al. "Infant Apnea Detection After Herniorrhaphy." Survey of Anesthesiology 40, no. 4 (1996): 222. http://dx.doi.org/10.1097/00132586-199608000-00026.

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Dissertations / Theses on the topic "Apnea detection"

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Karci, Ersin. "Detection Of Post Apnea Sounds And Apnea Periods From Sleep Sounds." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612964/index.pdf.

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Obstructive Sleep Apnea Syndrome (OSAS) is defined as a sleep related breathing disorder that causes the body to stop breathing for about 10 seconds and mostly ends with a loud sound due to the opening of the airway. OSAS is traditionally diagnosed using polysomnography, which requires a whole night stay at the sleep laboratory of a hospital, with multiple electrodes attached to the patient&#039<br>s body. Snoring is a symptom which may indicate presence of OSAS<br>thus investigation of snoring sounds, which can be recorded in the patient&#039<br>s own sleeping environment, has become popular
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Shewinvanakitkul, Prapan. "Automated Detection and Prediction of Sleep Apnea Events." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1486490112558014.

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Tian, Tian. "An Ultra-Wide Band Radar Based Noncontact Device for Real-time Apnea Detection." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/1092.

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"This thesis presents a real-time noncontact system that can monitor an infant's respiration and detect apnea when it occurs. For infants, bedside monitoring of respiratory signals using non-contact sensors is desirable at the hospital and for in-home care. Traditional approach employs acoustic sensors which can hardly detect infant breathing due to low SNR. In this thesis, a novel method is introduced by using a ultra-wideband (UWB) radar that obtains breathing signal from an infant's weak chest vibration. Furthermore, advanced signal processing techniques are proposed to monitor the breathin
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White, Daniel T. "Design of a Non-Contact Home Monitoring System for Audio Detection of Infant Apnea." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1463.

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Infant apnea is a widespread condition in which infants fail to effectively breathe, and can lead to death. Clinical solutions exist for continuous monitoring of respirations in a hospital setting and requiring constant skin contact. This thesis investigates the construction of a proof of concept device that performs in-home monitoring without skin contact and with commonly available off-the-shelf components. The device constructed used a directional microphone to detect breathing sounds, an omnidirectional microphone to detect ambient noise as a baseline to help isolate the breathing sounds,
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Simons, Lara Andrea da Silva. "Automatic sleep apnea detection and sleep classification using the ECG and the SpO2 signals." Master's thesis, FCT - UNL, 2009. http://hdl.handle.net/10362/2649.

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Dissertation for a Masters Degree in Computer and Electronic Engineering<br>The present work describes the aspects to implement a system that can be used as a swift and accessible screening tool in patients whose complaints are compatible with OSAS (Obstructive Sleep Apnea Syndrome). This system only uses two signals, electrocardiogram (ECG) and the saturation of oxygen in arterial blood flow (SPO2). This system would be applied for the ambulatory automatic screening of OSAS, which currently are done in a Hospital environment, with a substantial waiting list. The system also would overcome
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Azarbarzin, Ali. "Snoring sounds analysis: automatic detection, higher order statistics, and its application for sleep apnea diagnosis." IEEE, 2011. http://hdl.handle.net/1993/9593.

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Snoring is a highly prevalent disorder affecting 20-40% of adult population. Snoring is also a major indicative of obstructive sleep apnea (OSA). Despite the magnitude of effort, the acoustical properties of snoring in relation to physiological states are not yet known. This thesis explores statistical properties of snoring sounds and their association with OSA. First, an unsupervised technique was developed to automatically extract the snoring sound segments from the lengthy recordings of respiratory sounds. This technique was tested over 5665 snoring sound segments of 30 participants and
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Cavusoglu, Mustafa. "An Efficient And Fast Method Of Snore Detection For Sleep Disorder Investigation." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608236/index.pdf.

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Snores are breath sounds that most people produce during sleep and they are reported to be a risk factor for various sleep disorders, such as obstructive sleep apnea syndrome (OSAS). Diagnosis of sleep disorders relies on the expertise of the clinician that inspects whole night polysomnography recordings. This inspection is time consuming and uncomfortable for the patient. There are surgical and therapeutic treatments. However, evaluation of the success of these methods also relies on subjective criteria and the expertise of the clinician. Thus, there is a strong need for a tool to analyze the
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Hastík, Matěj. "Detekce spánkové apnoe." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221327.

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This master‘s thesis deals with a detailed description of sleep apnea and methods of detection of sleep apnea. The first part of the work is focused on the physiology of sleep, sleep apnea itself, its distribution, symptoms, risk factors and treatment. The next part of the work deals with polysomnographic examination and methods for analysis of polysomnographic data. The last part is devoted to the procedure design for detecting sleep apnea by using only one kind of signal and by using more kinds of signals, implementation of these proposals, their testing on real data, evaluating the detectio
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Baldini, Laura. "Analisi delle funzionalità respiratorie." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5001/.

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Montazeri, Ghahjaverestan Nasim. "Early detection of cardiac arrhythmia based on Bayesian methods from ECG data." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1S061/document.

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L'apnée est une complication fréquente chez les nouveaux-nés prématurés. L'un des problèmes les plus fréquents est l'épisode d'apnée bradycardie dont la répétition influence de manière négative le développement de l'enfant. C'est pourquoi les enfants prématurés sont surveillés en continu par un système de monitoring. Depuis la mise en place de ce système, l'espérance de vie et le pronostic de vie des prématurés ont été considérablement améliorés et ainsi la mortalité réduite. En effet, les avancées technologiques en électronique, informatique et télécommunications ont conduit à l'élaboration d
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Book chapters on the topic "Apnea detection"

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Kumar, K. Nitin Sai, K. V. Trivikrama Ramarao, A. Sivasangari, R. M. Gomathi, E. Brumancia, and K. Indira. "Sleep Apnea Detection." In Advances in Power Systems and Energy Management. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7504-4_61.

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Liu, Li, Tingfeng Ye, Xuemei Guo, Ruixun Kong, Lei Bo, and Guoli Wang. "Apnea Detection with Microbend Fiber-Optic Sensor." In Lecture Notes in Electrical Engineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6499-9_21.

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Travieso, Carlos M., Jesús B. Alonso, Jaime R. Ticay-Rivas, and Marcos del Pozo-Baños. "Apnea Detection Based on Hidden Markov Model Kernel." In Advances in Nonlinear Speech Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25020-0_10.

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Dmitry, Devjatykh, Gerget Olga, and Olga G. Berestneva. "Sleep Apnea Detection Based on Dynamic Neural Networks." In Communications in Computer and Information Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11854-3_48.

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Zhang, Haojing, Weidong Gao, and Peizhi Liu. "Detection of Sleep Apnea Based on Cardiopulmonary Coupling." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9409-6_104.

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Przystup, P., A. Bujnowski, A. Poliński, J. Rumiński, and J. Wtorek. "Sleep Apnea Detection by Means of Analyzing Electrocardiographic Signal." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08491-6_15.

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Rolon, R. E., L. E. Di Persia, H. L. Rufiner, and R. D. Spies. "Most Discriminative Atom Selection for Apnea-hypopnea Events Detection." In VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13117-7_146.

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Alsalamah, Mashail, Saad Amin, and Vasile Palade. "Detection of Obstructive Sleep Apnea Using Deep Neural Network." In Applications of Big Data Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76472-6_5.

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Rostek, Kornel. "Detection of Apnea–Hypopnea Events Using Actigraphy and Sleep Sounds." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64474-5_27.

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Bali, Jyoti, Anilkumar Nandi, and P. S. Hiremath. "Efficient ANN Algorithms for Sleep Apnea Detection Using Transform Methods." In Algorithms for Intelligent Systems. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1100-4_5.

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Conference papers on the topic "Apnea detection"

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Yadollahi, A., and Z. Moussavi. "Acoustic obstructive sleep apnea detection." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5332870.

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Ali, Syeda Qurat-ul-Ain, and Varun Jeoti. "DWPT based sleep apnea detection." In 2011 National Postgraduate Conference (NPC). IEEE, 2011. http://dx.doi.org/10.1109/natpc.2011.6136432.

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Yadollahi, Azadeh, and Zahra Moussavi. "Apnea Detection by Acoustical Means." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.260391.

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Yadollahi, Azadeh, and Zahra Moussavi. "Apnea Detection by Acoustical Means." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.4398482.

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Hamnvik, Sondre, Pierre Bernabé, and Sagar Sen. "Yolo4Apnea: Real-time Detection of Obstructive Sleep Apnea." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/754.

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Obstructive sleep apnea is a serious sleep disorder that affects an estimated one billion adults worldwide. It causes breathing to repeatedly stop and start during sleep which over years increases the risk of hypertension, heart disease, stroke, Alzheimer's, and cancer. In this demo, we present Yolo4Apnea a deep learning system extending You Only Look Once (Yolo) system to detect sleep apnea events from abdominal breathing patterns in real-time enabling immediate awareness and action. Abdominal breathing is measured using a respiratory inductance plethysmography sensor worn around the stomach.
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Karci, E., Y. S. Dogrusoz, and T. Ciloglu. "Detection of post apnea sounds and apnea periods from sleep sounds." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091501.

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Jezzini, Ali, Mohammad Ayache, Lina Elkhansa, and Zein al abidin Ibrahim. "ECG classification for sleep apnea detection." In 2015 International Conference on Advances in Biomedical Engineering (ICABME). IEEE, 2015. http://dx.doi.org/10.1109/icabme.2015.7323312.

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Zhivolupova, Yuliya. "Sleep Apnea and Hypopnea Detection Algorithm." In 2019 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). IEEE, 2019. http://dx.doi.org/10.1109/usbereit.2019.8736554.

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Hsu, Chien-Chang, and Ping-Ta Shih. "An intelligent sleep apnea detection system." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580688.

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Deviaene, Margot, Jesus Lazaro, Dorien Huysmans, et al. "Sleep Apnea Detection Using Pulse Photoplethysmography." In 2018 Computing in Cardiology Conference. Computing in Cardiology, 2018. http://dx.doi.org/10.22489/cinc.2018.134.

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Reports on the topic "Apnea detection"

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Treadwell, Jonathan R., James T. Reston, Benjamin Rouse, Joann Fontanarosa, Neha Patel, and Nikhil K. Mull. Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. Agency for Healthcare Research and Quality (AHRQ), 2021. http://dx.doi.org/10.23970/ahrqepctb38.

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Background. Automated-entry consumer devices that collect and transmit patient-generated health data (PGHD) are being evaluated as potential tools to aid in the management of chronic diseases. The need exists to evaluate the evidence regarding consumer PGHD technologies, particularly for devices that have not gone through Food and Drug Administration evaluation. Purpose. To summarize the research related to automated-entry consumer health technologies that provide PGHD for the prevention or management of 11 chronic diseases. Methods. The project scope was determined through discussions with Ke
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