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Journal articles on the topic 'Apnea detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

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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Ramachandran, Anita, and Anupama Karuppiah. "A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems." Healthcare 9, no. 7 (2021): 914. http://dx.doi.org/10.3390/healthcare9070914.

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Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier

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Oppersma, Eline, Wolfgang Ganglberger, Haoqi Sun, Robert Thomas, and Michael Westover. "475 Automatic detection of self-similarity and prediction of CPAP failure." Sleep 44, Supplement_2 (2021): A187. http://dx.doi.org/10.1093/sleep/zsab072.474.

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Abstract Introduction Sleep disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. Tolerance and efficacy of continuous positive airway pressure (CPAP), the primary form of therapy for sleep apnea, is often poor. High loop gain (HLG) is a driving mechanism of central sleep apnea or periodic breathing. The current study aimed to develop a computational approach to detect HLG based on self-similarity in respiratory oscillations during sleep solely using breathing patterns, measured via Respiratory Inductance Plethysmography (RIP). To quantify the po

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ElMoaqet, Hisham, Jungyoon Kim, Dawn Tilbury, Satya Krishna Ramachandran, Mutaz Ryalat, and Chao-Hsien Chu. "Gaussian Mixture Models for Detecting Sleep Apnea Events Using Single Oronasal Airflow Record." Applied Sciences 10, no. 21 (2020): 7889. http://dx.doi.org/10.3390/app10217889.

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Sleep apnea is a common sleep-related disorder that significantly affects the population. It is characterized by repeated breathing interruption during sleep. Such events can induce hypoxia, which is a risk factor for multiple cardiovascular and cerebrovascular diseases. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score sleep-related events. To address these limitations, many previous studies have proposed and implemented automatic scoring processes based on fewer sensors and machine learning classification algorithms. How

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von Einem, V., B. Widiger, G. Joseph, and C. W. Zywietz. "ECG Analysis for Sleep Apnea Detection." Methods of Information in Medicine 43, no. 01 (2004): 56–59. http://dx.doi.org/10.1055/s-0038-1633835.

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Summary Objectives: The objective of our study was to find out whether obstructive sleep apnea (OSA) may be detected on ECGs recorded during sleep. Methods: We have analyzed 70 eight-hour single-channel ECG recordings taken at polysomnographia. The 70 data sets were annotated for definition of regular sleep and phases with sleep apnea. From the 70 data sets, 35 have been used as a learning set. Our analysis is based on spectral components of heart rate variability. Frequency analysis was performed using Fourier and wavelet transformation with appropriate application of the Hilbert transform. C

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PEARSON, MICHAEL, and OLIVER FAUST. "HEART-RATE BASED SLEEP APNEA DETECTION USING ARDUINO." Journal of Mechanics in Medicine and Biology 19, no. 01 (2019): 1940006. http://dx.doi.org/10.1142/s0219519419400062.

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The purpose of this study was to investigate the use of a cost-effective heart rate monitor sensor and Arduino Uno configuration to accurately detect simulated sleep apnea, through the use of the inter-beat interval (R-R interval). Three separate 30[Formula: see text]min heart rate recordings were taken, each with six simulated sleep apnea events ranging from 20 to 40[Formula: see text]s. The results were gathered and processed to identify the simulated sleep apnea events. In each of the recordings, the simulated sleep apnea events were visible and the key characteristics, surrounding the even

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Schätz, Martin, Aleš Procházka, Jiří Kuchyňka, and Oldřich Vyšata. "Sleep Apnea Detection with Polysomnography and Depth Sensors." Sensors 20, no. 5 (2020): 1360. http://dx.doi.org/10.3390/s20051360.

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This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep apnea events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are sm

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Chaw, Hnin Thiri, Sinchai Kamolphiwong, and Krongthong Wongsritrang. "Sleep apnea detection using deep learning." Tehnički glasnik 13, no. 4 (2019): 261–66. http://dx.doi.org/10.31803/tg-20191104191722.

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Sleep apnea is the cessation of airflow at least 10 seconds and it is the type of breathing disorder in which breathing stops at the time of sleeping. The proposed model uses type 4 sleep study which focuses more on portability and the reduction of the signals. The main limitations of type 1 full night polysomnography are time consuming and it requires much space for sleep recording such as sleep lab comparing to type 4 sleep studies. The detection of sleep apnea using deep convolutional neural network model based on SPO2 sensor is the valid alternative for efficient polysomnography and it is

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Ademola Bello, Saheed, and Umar Alqasemi. "Computer Aided Detection of Obstructive Sleep Apnea from EEG Signals." Signal & Image Processing : An International Journal 12, no. 3 (2021): 17–24. http://dx.doi.org/10.5121/sipij.2021.12302.

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Sleep Apnea is an anomaly in sleeping characterized by short pause in breathing. Failure to treat sleep apnea leads to fatal complications in both psychological and physiological being of human. Electroencephalogram (EEG) performs an important task in probing for sleep apnea through identifying and recording the brain’s activities while sleeping. In this study, computer aided detection of sleep apnea from EEG signals is developed to optimize and increase the prompt recognition and diagnosis of sleep apnea in patients. The time domain, wavelets, and frequency domain of the EEG signals were comp

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Korotun, M., L. Weizman, A. Drori, et al. "0584 Detecting Sleep Disordered Breathing Using Sub-Terahertz Radio-Frequency Micro-Radar." Sleep 43, Supplement_1 (2020): A224. http://dx.doi.org/10.1093/sleep/zsaa056.581.

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Abstract Introduction New sensor technologies are entering sleep testing at a rapid pace; Neteera™ developed a novel sensor and algorithm for sleep apnea detection utilizing a contact-free, radar-based sensor system. The system utilizes a high-frequency, low-power, directional micro-radar which operates at ~120GHz and a sampling rate of 2500Hz as well as algorithms which are able to detect both pulse and respiratory activity of subjects during sleep. Methods Adult subjects undergoing diagnostic PSG for clinical purposes were simultaneously assessed with the novel micro-radar system with sensor

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Wu, Yanan, Jing Liu, Baolin He, Xiaotong Zhang, and Lu Yu. "Adaptive Filtering Improved Apnea Detection Performance Using Tracheal Sounds in Noisy Environment: A Simulation Study." BioMed Research International 2020 (May 27, 2020): 1–8. http://dx.doi.org/10.1155/2020/7429345.

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Objective. Tracheal sounds were used to detect apnea on various occasions. However, ambient noises can contaminate tracheal sounds which result in poor performance of apnea detection. The objective of this paper was to apply the adaptive filtering (AF) algorithm to improve the quality of tracheal sounds and examine the accuracy of the apnea detection algorithm using tracheal sounds after AF. Method. Tracheal sounds were acquired using a primary microphone encased in a plastic bell, and the ambient noises were collected using a reference microphone resting outside the plastic bell in quiet and

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Tkachenko, Dmytro, Ihor Krush, Vitalii Mykhalko, and Anatolii Petrenko. "Machine learning for diagnosis and monitoring of sleep apnea." System research and information technologies, no. 4 (December 29, 2020): 43–58. http://dx.doi.org/10.20535/srit.2308-8893.2020.4.04.

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This paper contains a review and analysis of applications of modern ma-chine learning approaches to solve sleep apnea severity level detection by localization of apnea episodes and prediction of the subsequent apnea episodes. We demonstrate that signals provided by cheap wearable devices can be used to solve typical tasks of sleep apnea detection. We review major publicly available datasets that can be used for training respective deep learning models, and we analyze the usage options of these datasets. In particular, we prove that deep learning could improve the accuracy of sleep apnea classi

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Baffet, G., C. Montaron, J. Boissinot, C. Freycenon, and J. Pinguet. "Sensor for apnea classification and detection." Sleep Medicine 16 (December 2015): S229. http://dx.doi.org/10.1016/j.sleep.2015.02.1488.

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Nandakumar, Rajalakshmi, Shyamnath Gollakota, and Nathaniel Watson. "Contactless Sleep Apnea Detection on Smartphones." GetMobile: Mobile Computing and Communications 19, no. 3 (2015): 22–24. http://dx.doi.org/10.1145/2867070.2867078.

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24

East, Katherine A., and Thomas D. East. "Computerized acoustic detection of obstructive apnea." Computer Methods and Programs in Biomedicine 21, no. 3 (1985): 213–20. http://dx.doi.org/10.1016/0169-2607(85)90006-9.

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25

Dickhaus, H., and C. Maier. "Central Sleep Apnea Detection from ECG-derived Respiratory Signals." Methods of Information in Medicine 49, no. 05 (2010): 462–66. http://dx.doi.org/10.3414/me09-02-0047.

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Summary Objectives: This study examines the suitability of recurrence plot analysis for the problem of central sleep apnea (CSA) detection and delineation from ECG-derived respiratory (EDR) signals. Methods: A parameter describing the average length of vertical line structures in recurrence plots is calculated at a time resolution of 1 s as ‘instantaneous trapping time’. Threshold comparison of this parameter is used to detect ongoing CSA. In data from 26 patients (duration 208 h) we assessed sensitivity for detection of CSA and mixed apnea (MSA) events by comparing the results obtained from 8

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Chang, Hung-Yu, Cheng-Yu Yeh, Chung-Te Lee, and Chun-Cheng Lin. "A Sleep Apnea Detection System Based on a One-Dimensional Deep Convolution Neural Network Model Using Single-Lead Electrocardiogram." Sensors 20, no. 15 (2020): 4157. http://dx.doi.org/10.3390/s20154157.

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Many works in recent years have been focused on developing a portable and less expensive system for diagnosing patients with obstructive sleep apnea (OSA), instead of using the inconvenient and expensive polysomnography (PSG). This study proposes a sleep apnea detection system based on a one-dimensional (1D) deep convolutional neural network (CNN) model using the single-lead 1D electrocardiogram (ECG) signals. The proposed CNN model consists of 10 identical CNN-based feature extraction layers, a flattened layer, 4 identical classification layers mainly composed of fully connected networks, and

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Magalang, Ulysses, Brendan Keenan, Bethany Staley, et al. "398 Agreement and reliability of a new respiratory event and arousal detection algorithm against multiple human scorers." Sleep 44, Supplement_2 (2021): A158. http://dx.doi.org/10.1093/sleep/zsab072.397.

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Abstract Introduction Scoring algorithms have the potential to increase polysomnography (PSG) scoring efficiency while also ensuring consistency and reproducibility. We sought to validate an updated event detection algorithm (Somnolyzer; Philips, Monroeville PA USA) against manual scoring, by analyzing a dataset we have previously used to report scoring variability across nine center-members of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Methods Fifteen PSGs collected at a single sleep clinic were scored independently by technologists at nine SAGIC centers located in six count

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Traxler, S., H. Pfützner, E. Kaniusas, and K. Futschik. "Magneto-Elastic Bilayers for Sleep Apnea Monitoring." Materials Science Forum 670 (December 2010): 355–59. http://dx.doi.org/10.4028/www.scientific.net/msf.670.355.

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Magneto-elastic bilayers (BLs), consisting of a magnetostrictive layer and a non-magnetic counter layer, show highest sensitivity with respect to bending. This paper describes a biomedical application in the field of sleep apnea screening. A multi-parametric detector fixed at the thorax contains two BLs. One BL yields a skin curvature sensor adjusting itself to curvature variations given by physiological activities. The second BL exhibits a free end thus working as a motion sensor. The two signals are fed into artificial neural networks for the detection of events like normal respiration and a

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Divakaran, Sindu, T. Sudhakar, R. Sindhiya, Rimisha Gupta, and J. Premkumar. "An intelligent detection and therapeutic device to support sleep apnea in infants." ITM Web of Conferences 37 (2021): 01006. http://dx.doi.org/10.1051/itmconf/20213701006.

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Among the numerous sleep-disorders breathing patterns encountered by babies, such as intermittent respiration, premature apnea, obstructive sleep apnea wa sconsidered a major cause of concern. Upper airway structure, pulmonary system mechanics, etc. are only a few reasons why the babies are vulnerable to obstructive sleep disorder. An imbalance in the viscoelastic properties of the pharynx, dilators and pressure can lead to airway collapse. Low level of oxygen in blood or hypoxemia is considered a characteristic in infants with severe Obstructive Sleep Apnea (OSA). Invasive treatments like nas

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Hayano, Junichiro, Hiroaki Yamamoto, Izumi Nonaka, et al. "Quantitative detection of sleep apnea with wearable watch device." PLOS ONE 15, no. 11 (2020): e0237279. http://dx.doi.org/10.1371/journal.pone.0237279.

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The spread of wearable watch devices with photoplethysmography (PPG) sensors has made it possible to use continuous pulse wave data during daily life. We examined if PPG pulse wave data can be used to detect sleep apnea, a common but underdiagnosed health problem associated with impaired quality of life and increased cardiovascular risk. In 41 patients undergoing diagnostic polysomnography (PSG) for sleep apnea, PPG was recorded simultaneously with a wearable watch device. The pulse interval data were analyzed by an automated algorithm called auto-correlated wave detection with adaptive thresh

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Almazaydeh, Laiali, Khaled Elleithy, Miad Faezipour, and Ahmad Abushakra. "Apnea Detection based on Respiratory Signal Classification." Procedia Computer Science 21 (2013): 310–16. http://dx.doi.org/10.1016/j.procs.2013.09.041.

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Kristiansen, Stein, Mari Sonsteby Hugaas, Vera Goebel, Thomas Plagemann, Konstantinos Nikolaidis, and Knut Liestol. "Data Mining for Patient Friendly Apnea Detection." IEEE Access 6 (2018): 74598–615. http://dx.doi.org/10.1109/access.2018.2882270.

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Azam, Siti Nurfarah Ain Mohd, Khairul Azami Sidek, and Nur Izzati Zainal. "Sleep Apnea Detection using Cardioid Based Graph." International Journal of Bio-Science and Bio-Technology 8, no. 5 (2016): 13–22. http://dx.doi.org/10.14257/ijbsbt.2016.8.5.02.

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Lee, Junghun, Jeon Lee, Hyo-Ki Lee, and Kyoung-Joung Lee. "Sleep Apnea Detection using Estimated Stroke Volume." Journal of Biomedical Engineering Research 34, no. 2 (2013): 97–103. http://dx.doi.org/10.9718/jber.2013.34.2.97.

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Koskinen, Anni, Adel Bachour, Jenni Vaarno, et al. "A detection dog for obstructive sleep apnea." Sleep and Breathing 23, no. 1 (2018): 281–85. http://dx.doi.org/10.1007/s11325-018-1659-x.

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Nandakumar, Rajalakshmi, Shyamnath Gollakota, and Jacob E. Sunshine. "Opioid overdose detection using smartphones." Science Translational Medicine 11, no. 474 (2019): eaau8914. http://dx.doi.org/10.1126/scitranslmed.aau8914.

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Early detection and rapid intervention can prevent death from opioid overdose. At high doses, opioids (particularly fentanyl) can cause rapid cessation of breathing (apnea), hypoxemic/hypercarbic respiratory failure, and death, the physiologic sequence by which people commonly succumb from unintentional opioid overdose. We present algorithms that run on smartphones and unobtrusively detect opioid overdose events and their precursors. Our proof-of- concept contactless system converts the phone into a short-range active sonar using frequency shifts to identify respiratory depression, apnea, and

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Chen, Xianda, Yifei Xiao, Yeming Tang, Julio Fernandez-Mendoza, and Guohong Cao. "ApneaDetector." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 2 (2021): 1–22. http://dx.doi.org/10.1145/3463514.

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Sleep apnea is a sleep disorder in which breathing is briefly and repeatedly interrupted. Polysomnography (PSG) is the standard clinical test for diagnosing sleep apnea. However, it is expensive and time-consuming which requires hospital visits, specialized wearable sensors, professional installations, and long waiting lists. To address this problem, we design a smartwatch-based system called ApneaDetector, which exploits the built-in sensors in smartwatches to detect sleep apnea. Through a clinical study, we identify features of sleep apnea captured by smartwatch, which can be leveraged by ma

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Mukherjee, Debadyuti, Koustav Dhar, Friedhelm Schwenker, and Ram Sarkar. "Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study." Sensors 21, no. 16 (2021): 5425. http://dx.doi.org/10.3390/s21165425.

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Sleep Apnea is a breathing disorder occurring during sleep. Older people suffer most from this disease. In-time diagnosis of apnea is needed which can be observed by the application of a proper health monitoring system. In this work, we focus on Obstructive Sleep Apnea (OSA) detection from the Electrocardiogram (ECG) signals obtained through the body sensors. Our work mainly consists of an experimental study of different ensemble techniques applied on three deep learning models—two Convolutional Neural Network (CNN) based models, and a combination of CNN and Long Short-Term Memory (LSTM) model

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Schlag, Christoph, Alexandra Wörner, Stefan Wagenpfeil, Eberhard F. Kochs, Roland M. Schmid, and Stefan von Delius. "Capnography Improves Detection of Apnea During Procedural Sedation for Percutaneous Transhepatic Cholangiodrainage." Canadian Journal of Gastroenterology 27, no. 10 (2013): 582–86. http://dx.doi.org/10.1155/2013/852454.

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BACKGROUND: Capnography provides noninvasive monitoring of ventilation and can enable early recognition of altered respiration patterns and apnea.OBJECTIVE: To compare the detection of apnea and the prediction of oxygen desaturation and hypoxemia using capnography versus clinical surveillance during procedural sedation for percutaneous transhepatic cholangiodrainage (PTCD).METHODS: Twenty consecutive patients scheduled for PTCD were included in the study. All patients were sedated during the procedure using midazolam and propofol. Aside from standard monitoring, additional capnographic monitor

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Sadek, Ibrahim, Terry Tan Soon Heng, Edwin Seet, and Bessam Abdulrazak. "A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study." Journal of Medical Internet Research 22, no. 9 (2020): e18297. http://dx.doi.org/10.2196/18297.

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Background At present, there is an increased demand for accurate and personalized patient monitoring because of the various challenges facing health care systems. For instance, rising costs and lack of physicians are two serious problems affecting the patient’s care. Nonintrusive monitoring of vital signs is a potential solution to close current gaps in patient monitoring. As an example, bed-embedded ballistocardiogram (BCG) sensors can help physicians identify cardiac arrhythmia and obstructive sleep apnea (OSA) nonintrusively without interfering with the patient’s everyday activities. Detect

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ElMoaqet, Hisham, Mohammad Eid, Martin Glos, Mutaz Ryalat, and Thomas Penzel. "Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals." Sensors 20, no. 18 (2020): 5037. http://dx.doi.org/10.3390/s20185037.

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Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly dependent on the experts’ experience and their prior knowledge about different physiological signals and conditions of the subjects. To overcome these problems, a novel deep recurrent neural network (RNN) framework is developed for automated feature extraction and detection of apnea events from single r

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Drzazga, Jakub, and Bogusław Cyganek. "An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals." Sensors 21, no. 17 (2021): 5858. http://dx.doi.org/10.3390/s21175858.

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One of the most common sleep disorders is sleep apnea. It manifests itself by episodes of shallow breathing or pauses in breathing during the night. Diagnosis of this disease involves polysomnography examination, which is expensive. Alternatively, diagnostic doctors can be supported with recordings from the in-home polygraphy sensors. Furthermore, numerous attempts for providing an automated apnea episodes annotation algorithm have been made. Most of them, however, do not distinguish between apnea and hypopnea episodes. In this work, a novel solution for epoch-based annotation problem is prese

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Bird, Jordan, Jason Chan, Alexander Rimke, et al. "060 Challenging the current assessment criteria for scoring central sleep apnea at altitude." Sleep 44, Supplement_2 (2021): A25. http://dx.doi.org/10.1093/sleep/zsab072.059.

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Abstract Introduction Sleep disordered breathing comes in two forms: obstructive and central sleep apnea (SA). Obstructive sleep apnea (OSA) is caused by upper airway collapse during sleep, and is associated with increases in morbidity and mortality. Conversely, central sleep apnea (CSA) results from increases in respiratory chemosensitivity to blood gas challenges in the context of high-altitude ascent. CSA increases in severity and apneas shorten in duration with higher ascent and/or time spent at altitude. Although both types of SA are characterized by intermittent periods of apnea and hype

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Wu, Ming-Feng, Wei-Chang Huang, Kai-Ming Chang, et al. "Detection Performance Regarding Sleep Apnea-Hypopnea Episodes with Fuzzy Logic Fusion on Single-Channel Airflow Indexes." Applied Sciences 10, no. 5 (2020): 1868. http://dx.doi.org/10.3390/app10051868.

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Obstructive sleep apnea-hypopnea syndrome (OSAHS) affects more than 936 million people worldwide and is the most common sleep-related breathing disorder; almost 80% of potential patients remain undiagnosed. To treat moderate to severe OSAHS as early as possible, the use of fewer sensing channels is recommended to screen for OSAHS and shorten waiting lists for the gold standard polysomnography (PSG). Hence, an effective out-of-clinic detection method may provide a solution to hospital overburden and associated health care costs. Applying single-channel signals to simultaneously detect apnea and

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LEE, REN-GUEY, I.-CHI CHOU, CHIEN-CHIH LAI, MING-HSIU LIU, and MING-JANG CHIU. "A NOVEL QRS DETECTION ALGORITHM APPLIED TO THE ANALYSIS FOR HEART RATE VARIABILITY OF PATIENTS WITH SLEEP APNEA." Biomedical Engineering: Applications, Basis and Communications 17, no. 05 (2005): 258–62. http://dx.doi.org/10.4015/s101623720500038x.

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Sleep-related breathing disorders can cause heart rate changes known as cyclical variation. The heart rate variation of patients with obstructive sleep apnea syndrome (OSAS) is more prominent in sleep. For this reason, to analyze heart rate variability (HRV) of patients with sleep apnea is a very important issue that can assist physicians to diagnose and give suitable treatment for patients. In this paper, a novel QRS detection algorithm is developed and applied to the analysis for HRV of patients with sleep apnea. The advantageous of the proposed algorithm is the combination of digital filter

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Ponganis, Paul J., Ulrike Kreutzer, Napapon Sailasuta, Torre Knower, Ralph Hurd, and Thomas Jue. "Detection of myoglobin desaturation in Mirounga angustirostris during apnea." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 282, no. 1 (2002): R267—R272. http://dx.doi.org/10.1152/ajpregu.00240.2001.

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1H NMR solution-state study of elephant seal ( Mirounga angustirostris) myoglobin (Mb) and hemoglobin (Hb) establishes the temperature-dependent chemical shifts of the proximal histidyl NδH signal, which reflects the respective intracellular and vascular Po 2 in vivo. Both proteins exist predominantly in one major isoform and do not exhibit any conformational heterogeneity. The Mb and Hb signals are detectable in M. angustirostris tissue in vivo. During eupnea M. angustirostris muscle maintains a well-saturated MbO2. However, during apnea, the deoxymyoglobin proximal histidyl NδH signal become

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Wenz, H., H. Dickhaus, and C. Maier. "Robust Detection of Sleep Apnea from Holter ECGs." Methods of Information in Medicine 53, no. 04 (2014): 303–7. http://dx.doi.org/10.3414/me13-02-0043.

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SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems”.Objectives: Detect presence of sleep-related breathing disorders (SRBD) in epochs of 1 min by signal analysis of Holter ECG recordings.Methods: In 121 patients, 140 synchronized polysomnograms (PSGs) and 8-channel Holter ECGs were recorded. The only excluded condition was persistent arrhythmias. Respiratory events as scored from the PSGs were mapped to a 1 min grid and served as reference for ECG-b

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Geil, E. S., A. R. Ramos, A. R. Abreu, et al. "0589 Arrhythmia Detection in Obstructive Sleep Apnea (ADIOS)." Sleep 43, Supplement_1 (2020): A225—A226. http://dx.doi.org/10.1093/sleep/zsaa056.586.

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Abstract Introduction Obstructive sleep apnea (OSA) is a recognized risk factor for ischemic stroke; however, there is a paucity of studies devoted to modifying stroke risk factors in patients with OSA. We aimed to evaluate the prevalence and treatment of stroke risk factors in newly diagnosed OSA patients. Methods We evaluated consecutive patients with an OSA diagnosis made within 12 months and CHADS2 score of &gt;2, consistent with high risk for atrial fibrillation. The patients completed polysomnography, sleep questionnaires, and systematic assessments for demographic variables, vascula

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Mendonca, Fabio, Sheikh Shanawaz Mostafa, Antonio G. Ravelo-Garcia, Fernando Morgado-Dias, and Thomas Penzel. "A Review of Obstructive Sleep Apnea Detection Approaches." IEEE Journal of Biomedical and Health Informatics 23, no. 2 (2019): 825–37. http://dx.doi.org/10.1109/jbhi.2018.2823265.

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Xie, B., and Hlaing Minn. "Real-Time Sleep Apnea Detection by Classifier Combination." IEEE Transactions on Information Technology in Biomedicine 16, no. 3 (2012): 469–77. http://dx.doi.org/10.1109/titb.2012.2188299.

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