Relevant bibliographies by topics / Respiratory signal processing

Academic literature on the topic 'Respiratory signal processing'

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Published: 11 March 2023

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Journal articles on the topic "Respiratory signal processing"

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Song, Ning, Lian Ying Ji, and Yong Peng Xu. "Denoising of the Respiratory Signal of Electrical Bio-Impedance." Advanced Materials Research 718-720 (July 2013): 1024–28. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1024.

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Human respiratory signal provides important information in modern medical care. In daily life, respiratory signal is usually captured under different motion states with the help of Electrical impedance pneumography (EIP). Consequently, the captured signal is easily corrupted by electronic/electromagnetic noise, internal mechanical vibration of the lung and motion artifacts. Because respiratory signal and interferences co-exist in an overlapping spectra manner, classical filtering method cannot work here. In this paper, we present a new signal processing method for eliminating the noise and interferences included in EIP signal, by separating the correlated motion artifacts from the raw EIP and 3-axis Acceleration (ACC) signals, restoring the rough respiration signal from the mixed signal, and further processing using wavelet analysis approach. Results are compared to traditional denosing algorithms by wiener filter, which indicates that the new signal processing method we presented is suitable for EIP signals under the motion states.
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Lorino, H., C. Mariette, M. Karouia, and A. M. Lorino. "Influence of signal processing on estimation of respiratory impedance." Journal of Applied Physiology 74, no. 1 (January 1, 1993): 215–23. http://dx.doi.org/10.1152/jappl.1993.74.1.215.

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Respiratory impedance was estimated between 4 and 30 Hz by spectral analysis of the mouth flow and pressure signals measured in spontaneously breathing subjects when applying a pseudorandom pressure excitation at the mouth. The signals were submitted to antialiasing low-pass filtering followed by digital preprocessing before the calculation of spectra by a fast Fourier transform algorithm. The effectiveness of signal preprocessing in eliminating the leakage error due to breathing noise was illustrated in both a mechanical analogue and a patient. Five preprocessing techniques that combined high-pass filtering and windowing were then compared in 32 randomly selected patients by examining the influence of these techniques on 1) the values of impedance at 5, 10, and 20 Hz, and 2) the parameters of linear models fitting the real (Zr) and imaginary (Zi) parts of impedance for coherence values higher than a preset threshold. The impedance values and derived parameters were either the mean of the estimates separately obtained in the three data recordings (PA) or the single estimate obtained from average spectra (SP). Small but significant differences between filtering and windowing, as well as between SP and PA, were evidenced for the Zr, whereas Zi was only slightly sensitive to the type of averaging technique. We conclude that the signal preprocessing and data averaging techniques selected in this study have similar effects on spectral estimation of respiratory impedance.
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Qi, Qingjie, Youxin Zhao, Liang Zhang, Zhen Yang, Lifeng Sun, and Xinlei Jia. "Research on Ultra-Wideband Radar Echo Signal Processing Method Based on P-Order Extraction and VMD." Sensors 22, no. 18 (September 6, 2022): 6726. http://dx.doi.org/10.3390/s22186726.

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As a new method to detect vital signs, Ultra-wideband (UWB) radar could continuously monitor human respiratory signs without contact. Aimed at addressing the problem of large interference and weak acquisition signal in radar echo signals from complex scenes, this paper adopts a UWB radar echo signal processing method that combines strong physical sign information extraction at P time and Variational Mode Decomposition (VMD) to carry out theoretical derivation. Using this novel processing scheme, respiration and heartbeat signals can be quickly reconstructed according to the selection of the appropriate intrinsic mode functions (IMFs), and the real-time detection accuracy of human respiratory signs is greatly improved. Based on an experimental platform, the data collected by the UWB radar module were first verified against the measured values obtained at the actual scene. The results of a validation test proved that our UWB radar echo signal processing method effectively eliminated the respiratory clutter signal and realized the accurate measurement of respiratory and heartbeat signals, which would prove the existence of life and further improve the quality of respiration and heartbeat signal and the robustness of detection.
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Kemper, Guillermo, Angel Oshita, Ricardo Parra, and 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 (February 1, 2023): 358. http://dx.doi.org/10.11591/ijece.v13i1.pp358-373.

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<span lang="EN-US">This work proposes a computational algorithm which extracts the frequency, timings and signal segments corresponding to respiratory phases, through buccal and nasal acoustic signal processing. The proposal offers a computational solution for medical applications which require on-site or remote patient monitoring and evaluation of pulmonary pathologies, such as coronavirus disease 19 (COVID-19). The state of the art presents a few respiratory evaluation proposals through buccal and nasal acoustic signals. Most proposals focus on respiratory signals acquired by a medical professional, using stethoscopes and electrodes located on the thorax. In this case the signal acquisition process is carried out through the use of a low cost and easy to use mask, which is equipped with strategically positioned and connected electret microphones, to maximize the proposed algorithm’s performance. The algorithm employs signal processing techniques such as signal envelope detection, decimation, fast Fourier transform (FFT) and detection of peaks and time intervals via estimation of local maxima and minima in a signal’s envelope. For the validation process a database of 32 signals of different respiratory modes and frequencies was used. Results show a maximum average error of 2.23% for breathing rate, 2.81% for expiration time and 3.47% for inspiration time.</span>
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Schulz, André, Thomas M. Schilling, Claus Vögele, Mauro F. Larra, and 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 (November 19, 2016): 20160019. http://dx.doi.org/10.1098/rstb.2016.0019.

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Current approaches to assess interoception of respiratory functions cannot differentiate between the physiological basis of interoception, i.e. visceral-afferent signal processing, and the psychological process of attention focusing. Furthermore, they typically involve invasive procedures, e.g. induction of respiratory occlusions or the inhalation of CO 2 -enriched air. The aim of this study was to test the capacity of startle methodology to reflect respiratory-related afferent signal processing, independent of invasive procedures. Forty-two healthy participants were tested in a spontaneous breathing and in a 0.25 Hz paced breathing condition. Acoustic startle noises of 105 dB(A) intensity (50 ms white noise) were presented with identical trial frequency at peak and on-going inspiration and expiration, based on a new pattern detection method, involving the online processing of the respiratory belt signal. The results show the highest startle magnitudes during on-going expiration compared with any other measurement points during the respiratory cycle, independent of whether breathing was spontaneous or paced. Afferent signals from slow adapting phasic pulmonary stretch receptors may be responsible for this effect. This study is the first to demonstrate startle modulation by respiration. These results offer the potential to apply startle methodology in the non-invasive testing of interoception-related aspects in respiratory psychophysiology. This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’.
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Zhao, Huayu. "Design and Application of Human Movement Respiratory and ECG Signal Acquisition System." Journal of Medical Imaging and Health Informatics 10, no. 4 (April 1, 2020): 890–97. http://dx.doi.org/10.1166/jmihi.2020.2950.

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To realize the design of mobile phone human movement breathing and electrocardiograph (ECG) signal acquisition system based on Bluetooth transmission, the principle of the generation and detection of ECG and respiratory signal and the guide system of signal acquisition are analyzed. Additionally, the hardware of the system is designed, including the hardware of the signal acquisition system, the design of ADS1292R ECG and respiratory signal acquisition module, the design of the main control chip and the design of the Bluetooth module. Then, the digital filtering processing of the ECG and respiratory signals is completed, including the baseline drift filtering and the suppression of the power frequency interference. The results show that the monitoring system runs well and it can effectively collect ECG and respiratory signals, calculate heart rate and respiratory frequency in real time, and display ECG waveform in real time. To sum up, the monitoring system is of great significance for real-time monitoring of the patient's condition.
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DE SILVA, CLARENCE W., SHAN XIAO, MAOQING LI, and CHERYL N. DE SILVA. "SENSORY SIGNAL PROCESSING ISSUES IN A TELEMEDICINE SYSTEM." International Journal of Information Acquisition 09, no. 02 (June 2013): 1350013. http://dx.doi.org/10.1142/s0219878913500137.

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A telemedicine system will provide sustainable, comprehensive, low-cost, fast, private, and convenient access to medical consultation and diagnosis for patients from remote locations. The telemedicine system addressed in this paper consists of a sensor jacket, which is worn by the patient for medical monitoring. The signals sensed through the jacket are processed and transmitted through a public telecommunication link, to a medical professional in a hospital at distance. The medical professional interacts with the patient through audio and video links, and simultaneously examines the data transmitted by the monitoring system. Medical assessment, diagnosis, and prescription are carried out on this basis. Sensing and signal processing are paramount to providing the patient data to the medical professional in an accurate and effective manner. This paper presents some relevant issues and techniques. Specific examples of electrocardiograms and respiratory signals are provided to illustrate the applicable signal conditioning approaches. Results are presented to demonstrate the feasibility and the effectiveness of these methods.
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De Meersman, R. E., A. S. Zion, S. Teitelbaum, J. P. Weir, J. Lieberman, and J. Downey. "Deriving respiration from pulse wave: a new signal-processing technique." American Journal of Physiology-Heart and Circulatory Physiology 270, no. 5 (May 1, 1996): H1672—H1675. http://dx.doi.org/10.1152/ajpheart.1996.270.5.h1672.

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Investigations of autonomic nervous system activity using spectral analysis of heart rate (HR) and blood pressure (BP) variability is very popular in many scientific disciplines, and yet only half of all studies involving spectral analysis control for respiration. Because respiration modulates HR and BP variability, knowledge of the respiratory rate is necessary for the proper interpretation of HR and BP power spectra. We devised and validated a new signal-processing technique to derive respiration from the blood pressure wave. This technique is based on the relationship between oscillations in the area under the dicrotic notch of the pulse wave and respiration. The results of our view signal-processing technique yielded significant correlations between protocols of the actual number of respiratory cycles and our blood pressure-derived respiratory cycles and their respective spectra for a number of standard autonomic tests (P < 0.05). Our method will allow retrospective extraction of the respiratory wave and as such afford a more precise interpretation of HR and BP spectra.
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Moreno, Silvia, Andres Quintero-Parra, Carlos Ochoa-Pertuz, Reynaldo Villarreal, and 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 (April 30, 2018): 1–10. http://dx.doi.org/10.14257/ijsip.2018.11.2.01.

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Motamedi-Fakhr, Shayan, Mohamed Moshrefi-Torbati, Martyn Hill, David Simpson, Romola S. Bucks, Annette Carroll, and Catherine M. Hill. "Respiratory cycle related EEG changes: Modified respiratory cycle segmentation." Biomedical Signal Processing and Control 8, no. 6 (November 2013): 838–44. http://dx.doi.org/10.1016/j.bspc.2013.08.001.

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Dissertations / Theses on the topic "Respiratory signal processing"

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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.

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Le photopléthysmogramme (PPG) est un signal optique acquis à partir de l’oxymètre de pouls, dont l’usage principal consiste à mesurer la saturation en oxygène. Avec le développement des technologies portables, il est devenu la technique de base pour la surveillance de l’activité cardio-respiratoire des patients et la détection des anomalies. En dépit de sa simplicité d'utilisation, le déploiement de cette technique reste encore limité pour deux principales raisons : 1. L’extrême sensibilité du signal aux distorsions. 2. La non-reproductibilité entre les sujets et pour les mêmes sujets, en raison de l'âge et des conditions de santé. L’objectif de cette thèse est le développement de méthodes robustes et universelles afin d’avoir une estimation précise de la fréquence respiratoire (FR) indépendamment de la variabilité intra et interindividuelle du PPG. Plusieurs contributions originales en traitement statistiques du signal PPG sont proposées. En premier lieu, une méthode adaptative de détection des artefacts basée sur la comparaison de modèle a été développée. Des tests par la technique Random Distortion Testing sont introduits pour détecter les pulses de PPG avec des artefacts. En deuxième lieu, une analyse de plusieurs méthodes spectrales d’estimation de la FR est proposée. Afin de mettre en évidence la robustesse des méthodes proposées face à la variabilité du signal, plusieurs tests ont été effectués sur deux bases de données avec de différentes tranches d'âge et des différents modes respiratoires. En troisième lieu, un indice de qualité respiratoire spectral (SRQI) est conçu dans le but de communiquer au clinicien que les valeurs fiables de la FR avec un certain degré de confiance
One 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
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Antonsson, Per, and 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.

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This thesis investigates the development and use of software to measure respiratory frequency on cows using optronics and computer vision. It examines mainly two different strategies of image and signal processing and their performances for different input qualities. The effect of heat stress on dairy cows and the high transmission risk of pneumonia for calves make the investigation done during this thesis highly relevant since they both have the same symptom; increased respiratory frequency. The data set used in this thesis was of recorded dairy cows in different environments and from varying angles. Recordings, where the authors could determine a true breathing frequency by monitoring body movements, were accepted to the data set and used to test and develop the algorithms. One method developed in this thesis estimated the breathing rate in the frequency domain by Fast Fourier Transform and was named "N-point Fast Fourier Transform." The other method was called "Breathing Movement Zero-Crossing Counting." It estimated a signal in the time domain, whose fundamental frequency was determined by a zero-crossing algorithm as the breathing frequency. The result showed that both the developed algorithm successfully estimated a breathing frequency with a reasonable error margin for most of the data set. The zero-crossing algorithm showed the most consistent result with an error margin lower than 0.92 breaths per minute (BPM) for twelve of thirteen recordings. However, it is limited to recordings where the camera is placed above the cow. The N-point FFT algorithm estimated the breathing frequency with error margins between 0.44 and 5.20 BPM for the same recordings as the zero-crossing algorithm. This method is not limited to a specific camera angle but requires the cow to be relatively stationary to get accurate results. Therefore, it could be evaluated with the remaining three recordings of the data set. The error margins for these recordings were measured between 1.92 and 10.88 BPM. Both methods had execution time acceptable for implementation in real-time. It was, however, too incomplete a data set to determine any performance with recordings from different optronic devices.
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Motamedi, 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/.

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Sleep is an important part of everyday life. It directly affects daytime cognition and general performance. In children, sleep is a crucial requirement for growth and learning and lack of sleep may manifest itself as a long lasting developmental deficit. Sleep disorders which disrupt the normal continuity of sleep therefore benefit from early identification and treatment. A common cause of sleep disruption is sleep disordered breathing which can be associated with frequent arousals from sleep. Many relevant areas of sleep research continue to generate new and interesting findings utilising biosignals such as EEGs. Respiratory cycle related EEG change (RCREC) is a good example of this. The method for quantification of RCREC relies on the appropriate application of signal processing and the signals involved in the procedure are polysomnographic. Furthermore, RCREC is thought to reflect morbid micro-arousals in sleep and is hence also of clinical importance. Given that the field of RCREC research is a recently established one, there is much room for constructive investigation. The current state of RCREC research is therefore expanded in this thesis. The method for calculation of respiratory cycle related EEG change (RCREC) is replicated and expanded in this project. Shortcomings of the method have been identified and accounted for where appropriate. In particular, the sensitivity of RCREC to airflow signal segmentation is addressed and alternative segmentation approaches are suggested. The general influence of airflow segmentation on RCREC is investigated and a mathematical explanation for RCREC sensitivity is given. Additionally, the ability of RCREC related parameters to predict daytime cognitive functions is assessed. Results suggest that RCREC parameters are capable of predicting quality of episodic memory, power (speed) of attention and internal processing speed.
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Raoof, Kosai. "Traitement du signal électromyographique des muscles respiratoires et estimation des paramètres en temps réel." Grenoble 1, 1993. http://www.theses.fr/1993GRE10013.

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La mesure par voie de surface du signal electromyographique (emg) des muscles respiratoires est perturbee par l'artefact cardiaque, le bruit de l'electronique de conditionnement et le bruit des electrodes de mesure. Nous proposons dans le cas de l'artefact cardiaque deux methodes de filtrage; l'une de ces deux methodes est basee sur un filtrage adaptatif applicable en temps reel. Pour le bruit de l'electronique et des electrodes, nous avons mis au point une solution utilisant un reseau d'electrodes permettant de rejecter ce bruit a l'aide d'un traitement multidimensionnel. Une carte-dsp de traitement et d'acquisition multivoies permettant l'implementation temps reel des procedures de traitement et de calcul des parametres physiologiques a ete developpee. Le calcul de ces parametres est synchronise sur le signal de debit ventilatoire ce qui nous permet de calculer les parametres inspiratoires et expiratoires separement
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Ajčević, 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.

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2013/2014
High 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
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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.

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Identification of accurate tumor location and shape is highly important in lung cancer radiotherapy, to improve the treatment quality by reducing dose delivery errors. Because a lung tumor moves with the patient's respiration, breathing motion should be correctly analyzed and predicted during the treatment for prevention of tumor miss or undesirable treatment toxicity. Besides, in Image-Guided Radiation Therapy (IGRT), the tumor motion causes difficulties not only in delivering accurate dose, but also in assuring superior quality of imaging techniques such as four-dimensional (4D) Cone Beam Computed Tomography (CBCT) and 4D Magnetic Resonance Imaging (MRI). Specifically, 4D CBCT used in CBCT IGRT requires precise respiratory signal extraction to avoid burry edges, inaccurate tumor shape, and motion-induced artifacts on the reconstructed CBCT image. 4D MRIs used in MRI-guided radiation therapy typically have low resolution as a tradeoff with field of view, image acquisition time, and image quality. To predict the tumor motion and guarantee the superior quality of the imaging techniques, the dissertation is divided into three parts. The first part describes a new prediction method for respiration-related tumor movements, called Intra- and Inter-fractional variation prediction using Fuzzy Deep Learning (IIFDL). IIFDL clusters the respiratory movements based on breathing similarities, and estimates patients' breathing motion using the proposed predictor, called fuzzy deep learning. The second part of the dissertation includes a novel marker-less binning method for 4D CBCT projections, called Image Registration-based Projection Binning (IRPB), which combines intensity-based feature point detection and trajectory tracking using random sample consensus. IRPB extracts breathing motion and phases by analyzing periodicity of tissue feature point trajectories. The third part the dissertation explains a novel Super-Resolution (SR) method for 4D MRI, called Recurrent Deep Learning-based SR (RDLS), comprised of feature extraction, recurrent nonlinear mapping, and reconstruction. RDLS estimates high-resolution MRIs from low-resolution MRIs according to a specified magnification power.
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Hult, Peter. "Bioacoustic principles used in monitoring and diagnostic applications /." Linköping : Univ, 2002. http://www.bibl.liu.se/liupubl/disp/disp2002/tek778s.pdf.

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Lucangelo, 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.

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2012/2013
The 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
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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.

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L'objectif de cette thèse est d'étudier temporellement des phénomènes évolutifs à l'aide de la tomographie d'émission monophotonique (TEMP). La première partie de cette thèse traite le problème du mouvement respiratoire dans les images TEMP de souris. Nous présentons ici une méthode permettant de détecter ce mouvement respiratoire dans les images TEMP 4D, d'extraire un signal respiratoire intrinsèque, et de déterminer la phase du cycle respiratoire sans mouvement la plus large possible. Les données enregistrées durant ces phases sans mouvement sont alors utilisées pour reconstruire une seule image TEMP 3D sans artefact de mouvement par acquisition. Les images ainsi reconstruites présentent un bon compromis en terme de statistiques et de précision des mesures par rapport aux images TEMP 3D de base et TEMP 4D. Dans la deuxième partie, nous étudions la cinétique d'incorporation de l'iode dans l'estomac de souris à partir d'images TEMP 4D. Afin de comprendre le rôle biologique de cette accumulation dans l'estomac, nous avons modélisé le phénomène par une approche d'analyse compartimentale avec un modèle simplifiée à deux compartiments (paroi et cavité stomacale) et une entrée (sang). Les courbes temps-activité (TAC) de chaque compartiment sont déduites des observations et une première estimation des paramètres a été obtenue.
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Li, 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.

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Books on the topic "Respiratory signal processing"

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Hadjileontiadis, Hadji. Lung Sounds: An Advanced Signal Processing Perspective. Springer International Publishing AG, 2008.

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Hadjileontiadis, Hadji. Lung Sounds: An Advanced Signal Processing Perspective. Morgan & Claypool Publishers, 2008.

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Hadjileontiadis, Hadji. Lung Sounds: An Advanced Signal Processing Perspective. Morgan & Claypool Publishers, 2009.

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Choi, Haan-Go. Multiresolution segmentation methodology for respiratory electromyographic signals. 1992.

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Boric-Lubecke, Olga, Byung-Kwon Park, Victor M. Lubecke, Amy D. Droitcour, and Aditya Singh. Doppler Radar Physiological Sensing. Wiley & Sons, Limited, John, 2016.

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Boric-Lubecke, Olga, Byung-Kwon Park, Victor M. Lubecke, Amy D. Droitcour, and Aditya Singh. Doppler Radar Physiological Sensing. Wiley & Sons, Incorporated, John, 2015.

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Doppler Radar Physiological Sensing. John Wiley & Sons, 2013.

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Boric-Lubecke, Olga, Byung-Kwon Park, Victor M. Lubecke, Amy D. Droitcour, and Aditya Singh. Doppler Radar Physiological Sensing. Wiley & Sons, Incorporated, John, 2015.

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Butkov, Nic. Polysomnography. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0007.

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This chapter provides an overview of the sleep recording process, including the application of electrodes and sensors to the patient, instrumentation, signal processing, digital polysomnography (PSG), and artifact recognition. Topics discussed include indications for PSG, standard recording parameters, patient preparation, electrode placement for recording the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG), the use of respiratory transducers, oximetry, signal processing, filters, digital data display, electrical safety, and patient monitoring. This chapter also includes record samples of the various types of recording artifacts commonly found in sleep studies, with a detailed description of their causes, preventative measures, and recommended corrective actions.
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Tarsia, Paolo. Dyspnoea in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0083.

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Dyspnoea may be defined as a subjective experience of discomfort associated with breathing. Breathing discomfort arises as a result of complex interactions between signals relayed from the upper airways, the chest wall, the lungs, and the central nervous system. Integration of this information with higher brain centres provides further processing. The final aspects of the sensation of dyspnoea are influenced by contextual, environmental, behavioural, and cognitive factors. At least three qualitatively distinct sensations have been employed to describe discomfort in breathing—air hunger, increased effort of breathing, and chest tightness. Air hunger has been shown to be associated with stimulation of chemoreceptors. Increased effort of breathing may arise in clinical conditions that impair respiratory muscle performance through abnormal mechanical loads or when respiratory muscles are weakened (neuromuscular diseases). Chest tightness is often experienced by asthmatic patients during episodes of acute bronchoconstriction. Measurement of dyspnoea is essential in order to assess it adequately and monitor response to treatment. Dyspnoea assessment may be carried out thorough a number of different scales, questionnaires, or exercise tests. Strategies in controlling dyspnoea should not focus uniquely on decreasing dyspnoea intensity. Patients may profit from interventions that decrease the unpleasantness associated with breathlessness without necessarily affecting the intensity component of the symptom.
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Book chapters on the topic "Respiratory signal processing"

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Zhang, Yiying, Delong Wang, Baoxian Zhou, and Yiyang Liu. "A Method of Respiratory Monitoring Based on Knowledge Graph." In New Approaches for Multidimensional Signal Processing, 263–70. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8558-3_21.

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Wang, Yan-Di, Chun-Hui Liu, Ren-Yi Jiang, Bor-Shing Lin, and Bor-Shyh Lin. "Novel Approach of Respiratory Sound Monitoring Under Motion." In 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.

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Kopaczka, Marcin, Özcan Özkan, and Dorit Merhof. "Face Tracking and Respiratory Signal Analysis for the Detection of Sleep Apnea in Thermal Infrared Videos with Head Movement." In 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.

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Engin, T., E. Ç. Güler, B. Sankur, and Y. P. Kahya. "COMPARISON OF AR-BASED CLASSIFIERS FOR RESPIRATORY SOUNDS." In Signal Processing, 1745–48. Elsevier, 1992. http://dx.doi.org/10.1016/b978-0-444-89587-5.50138-9.

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Jin, Feng, and Farook Sattar. "Enhancement of Recorded Respiratory Sound Using Signal Processing Techniques." In Encyclopedia of Information Communication Technology, 291–300. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-845-1.ch039.

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Pulmonary auscultation has been the key method to detect and evaluate respiratory dysfunctions for many years. However, auscultation with a stethoscope is a subjective process that depends on the individual’s own hearing, experience, and ability to differentiate between different sounds (Sovijarvi et al, 2000). Therefore, the computerized method for recording and analysis of pulmonary auscultative signals, being an objective way, are recently playing a more and more important role in the evaluation of patients with pulmonary diseases. Noise interference is one of the most influential factors when dealing with respiratory sound recordings. By definition of (Rossi et al, 2000), any sound not directly induced by breathing is regarded as background noise (BN). BN is divided into two types: environmental noise, which consists of continuous noise and transient noise, and nonrespiratory sounds and body sounds (muscle contraction sounds, skin friction, and heart sounds). The adaptive filtering is usually used to reduce the background noise. However, the problem of existing proposed filtering methods are either not able to minimize the interference or provides distortion which is especially undesirable for biomedical signals (Donoho, 1992).
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Malarvili, M. B., Teo Aik Howe, Santheraleka Ramanathan, Mushikiwabeza Alexie, and Om Prakash Singh. "The human respiratory system and overview of respiratory diseases." In 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.

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Komalla, Ashoka Reddy. "Pulse Oximetry." In 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.

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Pulse rate, body temperature, blood pressure, and respiratory rate are four vital signs indicating health status of a patient. Oxygen saturation of arterial blood (SaO2) is regarded as fifth vital sign of health status. Pulse oximeters are used in post-operative intensive care units for monitoring pulse rate and SaO2. They make non-invasive simultaneous estimation of pulse rate and SaO2 using photoplethysmogram (PPG) signals captured at red and IR wavelengths. This chapter describes the concept of oximetry, importance of non-invasive medical measurements, principle of pulse oximetry, and the block diagram approach for the design of pulse oximeters. It also presents an exhaustive review on various methods in-vogue for SaO2 estimation, identifies the problems associated with pulse oximeters. The critical limitation is that commercial pulse oximeters are as accurate as their calibration curves. Finally, it presents state-of-the-art research aimed at performance enhancement of pulse oximeters and directions for future work.
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Maletras, F. X., A. T. Augousti, and J. Mason. "Signal Processing Considerations in the use of the Fibre Optic Respiratory Plethysmograph (FORP) for Cardiac Monitoring." In Sensors and their Applications XI, 371–76. CRC Press, 2018. http://dx.doi.org/10.1201/9781351076593-56.

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Jindal, Sumit Kumar, Sayak Banerjee, Ritayan Patra, and Arin Paul. "Applications of Deep Learning in Medical Engineering." In Advances in Computing Communications and Informatics, 68–99. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815040401122030006.

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As a result of considerable breakthroughs in the field of artificial intelligence, deep learning has achieved exceptional success in resolving issues.This work brings forth a historical overview of deep learning and neural networks and further discusses its applications in the domain of medical engineerings - such as detection of brain tumours, sleep apnea, arrhythmia detection, etc. One of the most important and mysterious organs of our body is the brain. Like any other organ, our brain may suffer from various life-threatening diseases like brain tumours which can be malignant or benign. Analysis of the brain MRI images by applying convolution neural networks or artificial neural networks can automate this process by classifying these images into various types of tumours. A faster and more effective method can be provided by this method for detecting the disease at a key stage from where recovery is possible. Sleep apnea is a sleeping disorder involving irregular breathing. The brain detects a sudden decrease in the level of oxygen and sends a signal to wake the person up while he is sleeping. Cardiac arrhythmia refers to a group of conditions that causes the heart to beat irregularly, too slowly, or too quickly, e.g., atrial fibrillation. Deep learning along with bio-medical signal and audio processing techniques on respiratory sound datasets and ECG datasets have huge potential in the detection of these diseases. Deep learning outperforms the existing detection algorithms and a good amount of effort on feature engineering, augmentation techniques, and building effective filters can get a high accuracy result.
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Witschey, Walter RT, and Michael Markl. "Blood flow and phase contrast CMR." In The EACVI Textbook of Cardiovascular Magnetic Resonance, edited by Massimo Lombardi, Sven Plein, Steffen Petersen, Chiara Bucciarelli-Ducci, Emanuela R. Valsangiacomo Buechel, Cristina Basso, and Victor Ferrari, 146–63. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198779735.003.0018.

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Flow-sensitive cardiovascular magnetic resonance (CMR) is a widespread non-invasive imaging method for the clinical evaluation of blood flow in cardiovascular disease. The basic principle of phase contrast magnetic resonance imaging (MRI) is the use of bipolar gradients to encode blood velocity in the magnetic resonance (MR) signal phase. The most common type of flow-encoded scan two-dimensional (2D) cine phase contrast CMR with single-direction velocity encoding is clinically used to quantify cardiovascular flow and velocities. Trade-offs between resolution (temporal and spatial) and acquisition time are illustrated in the context of patient examination, considering high-velocity jet flow, patient breath-hold duration, respiratory motion artefacts, and patient comfort. In addition, the chapter describes how the velocity-to-noise ratio and aliasing behaviour of flow measurements are affected by the velocity-encoding sensitivity (VENC). An advantage of phase contrast MR is that flow encoding may be performed in all three spatial dimensions, improving peak velocity measurement accuracy. Several clinical applications (aortic stenosis, coarctation, and ventricular shunting) and best practices are explained in detail with illustrations. Analysis and post-processing of phase contrast data are summarized. The progressive development of advanced phase contrast techniques is discussed by adding incremental complexity, starting with 2D phase contrast (2D spatial and one-dimensional velocity) and ending with four-dimensional flow encoding (three-dimensional spatial and velocity). Methods to accelerate phase contrast, such as parallel imaging, are briefly discussed. Finally, the chapter concludes with a summary of emerging topics for accelerated scanning and special applications such as compressed sensing, real-time phase contrast, and ultra-short echo time imaging.
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Conference papers on the topic "Respiratory signal processing"

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Lee, E. M., N. H. Kim, N. T. Trang, J. H. Hong, E. J. Cha, and T. S. Lee. "Respiratory rate detection algorithms by photoplethysmography signal processing." In 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.

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Tseng, Hsien-Wei, Yang-Han Lee, Yi-Lun Chen, and Chih-Hsien Hsia. "Analysis between ECG and respiratory signal." In 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE, 2017. http://dx.doi.org/10.1109/ispacs.2017.8266513.

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"Multifractality Analysis of Respiratory Signals." In 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE, 2020. http://dx.doi.org/10.1109/siu49456.2020.9302342.

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Rady, Radwa Magdy, Ibrahim Mohamed El Akkary, Ahmed Nashaat Haroun, Nader Abd Elmoneum Fasseh, and Mohamed Moustafa Azmy. "Respiratory Wheeze Sound Analysis Using Digital Signal Processing Techniques." In 2015 7th International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN). IEEE, 2015. http://dx.doi.org/10.1109/cicsyn.2015.38.

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Jarchi, Delaram, and Saeid Sanei. "Derivation of Respiratory Effort from Photoplethysmography." In 2019 27th European Signal Processing Conference (EUSIPCO). IEEE, 2019. http://dx.doi.org/10.23919/eusipco.2019.8902606.

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Le Cam, S., Ch Collet, and F. Salzenstein. "Acoustical respiratory signal analysis and phase detection." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518438.

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Momot, Michal, Alina Momot, and Ewelina Piekar. "Robust estimation of respiratory rate based on linear regression." In 2015 Signal Processing Symposium (SPSympo). IEEE, 2015. http://dx.doi.org/10.1109/sps.2015.7168261.

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Nallanthighal, Venkata Srikanth, Aki Harma, Helmer Strik, and Mathew Magimai Doss. "Phoneme Based Respiratory Analysis of Read Speech." In 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9615986.

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Alinovi, Davide, Gianluigi Ferrari, Francesco Pisani, and Riccardo Raheli. "Respiratory rate monitoring by maximum likelihood video processing." In 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2016. http://dx.doi.org/10.1109/isspit.2016.7886029.

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Ma, Ganjun, Biao Xue, Hong Hong, Xiaohua Zhu, and Zhiyong Wang. "Unsupervised snore detection from respiratory sound signals." In 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE, 2015. http://dx.doi.org/10.1109/icdsp.2015.7251905.

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