Littérature scientifique sur le sujet « Respiratory signal processing »
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Articles de revues sur le sujet "Respiratory signal processing"
Song, Ning, Lian Ying Ji et Yong Peng Xu. « Denoising of the Respiratory Signal of Electrical Bio-Impedance ». Advanced Materials Research 718-720 (juillet 2013) : 1024–28. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1024.
Texte intégralLorino, H., C. Mariette, M. Karouia et A. M. Lorino. « Influence of signal processing on estimation of respiratory impedance ». Journal of Applied Physiology 74, no 1 (1 janvier 1993) : 215–23. http://dx.doi.org/10.1152/jappl.1993.74.1.215.
Texte intégralQi, Qingjie, Youxin Zhao, Liang Zhang, Zhen Yang, Lifeng Sun et Xinlei Jia. « Research on Ultra-Wideband Radar Echo Signal Processing Method Based on P-Order Extraction and VMD ». Sensors 22, no 18 (6 septembre 2022) : 6726. http://dx.doi.org/10.3390/s22186726.
Texte intégralKemper, Guillermo, Angel Oshita, Ricardo Parra et Carlos Herrera. « An algorithm for obtaining the frequency and the times of respiratory phases from nasal and oral acoustic signals ». International Journal of Electrical and Computer Engineering (IJECE) 13, no 1 (1 février 2023) : 358. http://dx.doi.org/10.11591/ijece.v13i1.pp358-373.
Texte intégralSchulz, André, Thomas M. Schilling, Claus Vögele, Mauro F. Larra et Hartmut Schächinger. « Respiratory modulation of startle eye blink : a new approach to assess afferent signals from the respiratory system ». Philosophical Transactions of the Royal Society B : Biological Sciences 371, no 1708 (19 novembre 2016) : 20160019. http://dx.doi.org/10.1098/rstb.2016.0019.
Texte intégralZhao, Huayu. « Design and Application of Human Movement Respiratory and ECG Signal Acquisition System ». Journal of Medical Imaging and Health Informatics 10, no 4 (1 avril 2020) : 890–97. http://dx.doi.org/10.1166/jmihi.2020.2950.
Texte intégralDE SILVA, CLARENCE W., SHAN XIAO, MAOQING LI et CHERYL N. DE SILVA. « SENSORY SIGNAL PROCESSING ISSUES IN A TELEMEDICINE SYSTEM ». International Journal of Information Acquisition 09, no 02 (juin 2013) : 1350013. http://dx.doi.org/10.1142/s0219878913500137.
Texte intégralDe Meersman, R. E., A. S. Zion, S. Teitelbaum, J. P. Weir, J. Lieberman et J. Downey. « Deriving respiration from pulse wave : a new signal-processing technique ». American Journal of Physiology-Heart and Circulatory Physiology 270, no 5 (1 mai 1996) : H1672—H1675. http://dx.doi.org/10.1152/ajpheart.1996.270.5.h1672.
Texte intégralMoreno, Silvia, Andres Quintero-Parra, Carlos Ochoa-Pertuz, Reynaldo Villarreal et Isaac Kuzmar. « A Signal Processing Method for Respiratory Rate Estimation through Photoplethysmography ». International Journal of Signal Processing, Image Processing and Pattern Recognition 11, no 2 (30 avril 2018) : 1–10. http://dx.doi.org/10.14257/ijsip.2018.11.2.01.
Texte intégralMotamedi-Fakhr, Shayan, Mohamed Moshrefi-Torbati, Martyn Hill, David Simpson, Romola S. Bucks, Annette Carroll et Catherine M. Hill. « Respiratory cycle related EEG changes : Modified respiratory cycle segmentation ». Biomedical Signal Processing and Control 8, no 6 (novembre 2013) : 838–44. http://dx.doi.org/10.1016/j.bspc.2013.08.001.
Texte intégralThèses sur le sujet "Respiratory signal processing"
Cherif, Safa. « Effective signal processing methods for robust respiratory rate estimation from photoplethysmography signal ». Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2018. http://www.theses.fr/2018IMTA0094/document.
Texte intégralOne promising area of research in clinical routine involves using photoplethysmography (PPG) for monitoring respiratory activities. PPG is an optical signal acquired from oximeters, whose principal use consists in measuring oxygen saturation. Despite its simplicity of use, the deployment of this technique is still limited because of the signal sensitivity to distortions and the non-reproducibility between subjects, but also for the same subject, due to age and health conditions. The main aim of this work is to develop robust and universal methods for estimating accurate respiratory rate regardless of the intra- and inter-individual variability that affects PPG features. For this purpose, firstly, an adaptive artefact detection method based on template matching and decision by Random Distortion Testing is introduced for detecting PPG pulses with artefacts. Secondly, an analysis of several spectral methods for Respiratory Rate (RR) estimation on two different databases, with different age ranges and different respiratory modes, is proposed. Thirdly, a Spectral Respiratory Quality Index (SRQI) is attributed to respiratory rate estimates, in order that the clinician may select only RR values with a large confidence scale. Promising results are found for two different databases
Antonsson, Per, et Jesper Johansson. « Measuring Respiratory Frequency Using Optronics and Computer Vision ». Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176354.
Texte intégralMotamedi, Fakhr Shayan. « Application of signal processing to respiratory cycle related EEG change (RCREC) in children ». Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/363767/.
Texte intégralRaoof, Kosai. « Traitement du signal électromyographique des muscles respiratoires et estimation des paramètres en temps réel ». Grenoble 1, 1993. http://www.theses.fr/1993GRE10013.
Texte intégralAjčević, Miloš. « Personalized setup of high frequency percussive ventilator by estimation of respiratory system viscoelastic parameters ». Doctoral thesis, Università degli studi di Trieste, 2015. http://hdl.handle.net/10077/10976.
Texte intégralHigh Frequency Percussive Ventilation (HFPV) is a non-conventional ventilatory modality which has proven highly effective in patients with severe gas exchange impairment. However, at the present time, HFPV ventilator provides only airway pressure measurement. The airway pressure measurements and gas exchange analysis are currently the only parameters that guide the physician during the HFPV ventilator setup and treatment monitoring. The evaluation of respiratory system resistance and compliance parameters in patients undergoing mechanical ventilation is used for lung dysfunctions detection, ventilation setup and treatment effect evaluation. Furthermore, the pressure measured by ventilator represents the sum of the endotracheal tube pressure drop and the tracheal pressure. From the clinical point of view, it is very important to take into account the real amount of pressure dissipated by endotracheal tube to avoid lung injury. HFPV is pressure controlled logic ventilation, thus hypoventilation and hyperventilation cases are possible because of tidal volume variations in function of pulmonary and endotracheal tube impedance. This thesis offers a new approach for HFPV ventilator setup in accordance with protective ventilatory strategy and optimization of alveolar recruitment using estimation of the respiratory mechanics parameters and endotracheal pressure drop. Respiratory system resistance and compliance parameters were estimated, firstly in vitro and successively in patients undergoing HFPV, applying least squares regression on Dorkin high frequency model starting from measured respiratory signals. The Blasius model was identified as the most adequate to estimate pressure drop across the endotracheal tube during HFPV. Beside measurement device was developed in order to measure respiratory parameters in patients undergoing HFPV. The possibility to tailor HFPV ventilator setup, using respiratory signals measurement and estimation of respiratory system resistance, compliance and endotracheal tube pressure drop, provided by this thesis, opens a new prospective to this particular ventilatory strategy, improving its beneficial effects and minimizing ventilator-induced lung damage.
XXVII Ciclo
1981
Park, Seonyeong. « Respiratory Prediction and Image Quality Improvement of 4D Cone Beam CT and MRI for Lung Tumor Treatments ». VCU Scholars Compass, 2017. http://scholarscompass.vcu.edu/etd/5046.
Texte intégralHult, Peter. « Bioacoustic principles used in monitoring and diagnostic applications / ». Linköping : Univ, 2002. http://www.bibl.liu.se/liupubl/disp/disp2002/tek778s.pdf.
Texte intégralLucangelo, Umberto. « Titration of High Frequency Percussive Ventilation by means of real-time monitoring of the viscoelastic respiratory system properties and endotracheal tubes pressure drop ». Doctoral thesis, Università degli studi di Trieste, 2014. http://hdl.handle.net/10077/9992.
Texte intégralThe use of High Frequency Percussive Ventilation (HFPV) is still debated although this type of non-conventional ventilation has proven effective and safe in patients with acute respiratory failure. In the clinical practice, HFPV is not an intuitive ventilatory modality and the absence of real-time delivered volume monitoring produces disaffection among the physicians. Avoiding the "volutrauma" is the cornerstone of the "protective ventilation strategy", which assumes a constant monitoring of inspiratory volume delivered to the patient. Currently the system capable of delivering HFPV is the VDR-4® (Volumetric Diffusive Respirator), which provides only analog airway pressure waveform and digital output of peak and the mean airway pressure. The latter is involved in the determination of oxygenation and hemodynamics, irrespective of the mode of ventilation. At the present time, the mean airway pressure, together with gas exchange analysis, are the only parameters that indirectly guide the physician in assessing the clinical effectiveness of HFPV. Till now, flow, volume and pressure curves generated by HFPV have never been studied in relation to the specific patients respiratory mechanics. The real-time examination of these parameters could allow the physicians to analyze and understand elements of respiratory system mechanics as compliance (Crs), resistance (Rrs), inertance (Irs) and of patient-ventilator interaction. The mechanical effects are complex and result from interactions between ventilator settings and patient’s respiratory system impedance. The aim of this doctoral thesis was to acquire and study volume and respiratory parameters during HFPV in order to explain this complex patients-machine interaction and transfer the results in clinical practice.
XXVI Ciclo
1959
Breuilly, Marine. « Imagerie TEMP 4D du petit animal : estimation du mouvement respiratoire et de la biodistribution de l'iode ». Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00908962.
Texte intégralLi, Yelei. « Heartbeat detection, classification and coupling analysis using Electrocardiography data ». Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1405084050.
Texte intégralLivres sur le sujet "Respiratory signal processing"
Hadjileontiadis, Hadji. Lung Sounds : An Advanced Signal Processing Perspective. Springer International Publishing AG, 2008.
Trouver le texte intégralHadjileontiadis, Hadji. Lung Sounds : An Advanced Signal Processing Perspective. Morgan & Claypool Publishers, 2008.
Trouver le texte intégralHadjileontiadis, Hadji. Lung Sounds : An Advanced Signal Processing Perspective. Morgan & Claypool Publishers, 2009.
Trouver le texte intégralChoi, Haan-Go. Multiresolution segmentation methodology for respiratory electromyographic signals. 1992.
Trouver le texte intégralBoric-Lubecke, Olga, Byung-Kwon Park, Victor M. Lubecke, Amy D. Droitcour et Aditya Singh. Doppler Radar Physiological Sensing. Wiley & Sons, Limited, John, 2016.
Trouver le texte intégralBoric-Lubecke, Olga, Byung-Kwon Park, Victor M. Lubecke, Amy D. Droitcour et Aditya Singh. Doppler Radar Physiological Sensing. Wiley & Sons, Incorporated, John, 2015.
Trouver le texte intégralDoppler Radar Physiological Sensing. John Wiley & Sons, 2013.
Trouver le texte intégralBoric-Lubecke, Olga, Byung-Kwon Park, Victor M. Lubecke, Amy D. Droitcour et Aditya Singh. Doppler Radar Physiological Sensing. Wiley & Sons, Incorporated, John, 2015.
Trouver le texte intégralButkov, Nic. Polysomnography. Sous la direction de Sudhansu Chokroverty, Luigi Ferini-Strambi et Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0007.
Texte intégralTarsia, Paolo. Dyspnoea in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0083.
Texte intégralChapitres de livres sur le sujet "Respiratory signal processing"
Zhang, Yiying, Delong Wang, Baoxian Zhou et Yiyang Liu. « A Method of Respiratory Monitoring Based on Knowledge Graph ». Dans New Approaches for Multidimensional Signal Processing, 263–70. Singapore : Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8558-3_21.
Texte intégralWang, Yan-Di, Chun-Hui Liu, Ren-Yi Jiang, Bor-Shing Lin et Bor-Shyh Lin. « Novel Approach of Respiratory Sound Monitoring Under Motion ». Dans Advances in Intelligent Information Hiding and Multimedia Signal Processing, 167–74. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63856-0_21.
Texte intégralKopaczka, Marcin, Özcan Özkan et Dorit Merhof. « Face Tracking and Respiratory Signal Analysis for the Detection of Sleep Apnea in Thermal Infrared Videos with Head Movement ». Dans New Trends in Image Analysis and Processing – ICIAP 2017, 163–70. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70742-6_15.
Texte intégralEngin, T., E. Ç. Güler, B. Sankur et Y. P. Kahya. « COMPARISON OF AR-BASED CLASSIFIERS FOR RESPIRATORY SOUNDS ». Dans Signal Processing, 1745–48. Elsevier, 1992. http://dx.doi.org/10.1016/b978-0-444-89587-5.50138-9.
Texte intégralJin, Feng, et Farook Sattar. « Enhancement of Recorded Respiratory Sound Using Signal Processing Techniques ». Dans Encyclopedia of Information Communication Technology, 291–300. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-845-1.ch039.
Texte intégralMalarvili, M. B., Teo Aik Howe, Santheraleka Ramanathan, Mushikiwabeza Alexie et Om Prakash Singh. « The human respiratory system and overview of respiratory diseases ». Dans Systems and Signal Processing of Capnography as a Diagnostic Tool for Asthma Assessment, 1–24. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-323-85747-5.00002-4.
Texte intégralKomalla, Ashoka Reddy. « Pulse Oximetry ». Dans Handbook of Research on Information Security in Biomedical Signal Processing, 130–53. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5152-2.ch007.
Texte intégralMaletras, F. X., A. T. Augousti et J. Mason. « Signal Processing Considerations in the use of the Fibre Optic Respiratory Plethysmograph (FORP) for Cardiac Monitoring ». Dans Sensors and their Applications XI, 371–76. CRC Press, 2018. http://dx.doi.org/10.1201/9781351076593-56.
Texte intégralJindal, Sumit Kumar, Sayak Banerjee, Ritayan Patra et Arin Paul. « Applications of Deep Learning in Medical Engineering ». Dans Advances in Computing Communications and Informatics, 68–99. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815040401122030006.
Texte intégralWitschey, Walter RT, et Michael Markl. « Blood flow and phase contrast CMR ». Dans The EACVI Textbook of Cardiovascular Magnetic Resonance, sous la direction de Massimo Lombardi, Sven Plein, Steffen Petersen, Chiara Bucciarelli-Ducci, Emanuela R. Valsangiacomo Buechel, Cristina Basso et Victor Ferrari, 146–63. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198779735.003.0018.
Texte intégralActes de conférences sur le sujet "Respiratory signal processing"
Lee, E. M., N. H. Kim, N. T. Trang, J. H. Hong, E. J. Cha et T. S. Lee. « Respiratory rate detection algorithms by photoplethysmography signal processing ». Dans 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4649362.
Texte intégralTseng, Hsien-Wei, Yang-Han Lee, Yi-Lun Chen et Chih-Hsien Hsia. « Analysis between ECG and respiratory signal ». Dans 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE, 2017. http://dx.doi.org/10.1109/ispacs.2017.8266513.
Texte intégral« Multifractality Analysis of Respiratory Signals ». Dans 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE, 2020. http://dx.doi.org/10.1109/siu49456.2020.9302342.
Texte intégralRady, Radwa Magdy, Ibrahim Mohamed El Akkary, Ahmed Nashaat Haroun, Nader Abd Elmoneum Fasseh et Mohamed Moustafa Azmy. « Respiratory Wheeze Sound Analysis Using Digital Signal Processing Techniques ». Dans 2015 7th International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN). IEEE, 2015. http://dx.doi.org/10.1109/cicsyn.2015.38.
Texte intégralJarchi, Delaram, et Saeid Sanei. « Derivation of Respiratory Effort from Photoplethysmography ». Dans 2019 27th European Signal Processing Conference (EUSIPCO). IEEE, 2019. http://dx.doi.org/10.23919/eusipco.2019.8902606.
Texte intégralLe Cam, S., Ch Collet et F. Salzenstein. « Acoustical respiratory signal analysis and phase detection ». Dans ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518438.
Texte intégralMomot, Michal, Alina Momot et Ewelina Piekar. « Robust estimation of respiratory rate based on linear regression ». Dans 2015 Signal Processing Symposium (SPSympo). IEEE, 2015. http://dx.doi.org/10.1109/sps.2015.7168261.
Texte intégralNallanthighal, Venkata Srikanth, Aki Harma, Helmer Strik et Mathew Magimai Doss. « Phoneme Based Respiratory Analysis of Read Speech ». Dans 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9615986.
Texte intégralAlinovi, Davide, Gianluigi Ferrari, Francesco Pisani et Riccardo Raheli. « Respiratory rate monitoring by maximum likelihood video processing ». Dans 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2016. http://dx.doi.org/10.1109/isspit.2016.7886029.
Texte intégralMa, Ganjun, Biao Xue, Hong Hong, Xiaohua Zhu et Zhiyong Wang. « Unsupervised snore detection from respiratory sound signals ». Dans 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE, 2015. http://dx.doi.org/10.1109/icdsp.2015.7251905.
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