Relevant bibliographies by topics / Respiratory pattern classification

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Respiratory pattern classification'

Author: Grafiati
Published: 27 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Respiratory pattern classification"

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Tasar, Beyda, Orhan Yaman, and Turker Tuncer. "Accurate respiratory sound classification model based on piccolo pattern." Applied Acoustics 188 (January 2022): 108589. http://dx.doi.org/10.1016/j.apacoust.2021.108589.

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Lee, Do-Kyeong, Jae-Sung Choi, Seong-Jun Choi, Min-Hyung Choi, and Min Hong. "Classification of Chronic Obstructive Pulmonary Disease (COPD) Through Respiratory Pattern Analysis." Diagnostics 15, no. 3 (2025): 313. https://doi.org/10.3390/diagnostics15030313.

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Background: This study proposes a classification system for predicting chronic obstructive pulmonary disease (COPD) patients and non-patients based on image and text data. Method: This study measured the respiratory volume based on thermal images, stored the respiratory data, and derived features related to respiratory patterns, including the total respiratory volume, average distance between expirations, average distance between inspirations, and total respiratory rate. The data for each feature were stored in text format. The four features saved as text were scaled using Z-score normalizatio
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Hong, Jin-Woo, Seong-Hoon Kim, and Gi-Tae Han. "Detection of Multiple Respiration Patterns Based on 1D SNN from Continuous Human Breathing Signals and the Range Classification Method for Each Respiration Pattern." Sensors 23, no. 11 (2023): 5275. http://dx.doi.org/10.3390/s23115275.

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Human respiratory information is being used as an important source of biometric information that can enable the analysis of health status in the healthcare domain. The analysis of the frequency or duration of a specific respiration pattern and the classification of respiration patterns in the corresponding section for a certain period of time are important for the utilization of respiratory information in various ways. Existing methods require window slide processing to classify sections for each respiration pattern from the breathing data for a certain time period. In this case, when multiple
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Kunczik, Janosch, Kerstin Hubbermann, Lucas Mösch, Andreas Follmann, Michael Czaplik, and Carina Barbosa Pereira. "Breathing Pattern Monitoring by Using Remote Sensors." Sensors 22, no. 22 (2022): 8854. http://dx.doi.org/10.3390/s22228854.

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The ability to continuously and unobtrusively monitor and classify breathing patterns can be very valuable for automated health assessments because respiration is tightly coupled to many physiological processes. Pathophysiological changes in these processes often manifest in altered breathing patterns and can thus be immediately detected. In order to develop a breathing pattern monitoring system, a study was conducted in which volunteer subjects were asked to breathe according to a predefined breathing protocol containing multiple breathing patterns while being recorded with color and thermal
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Park, Jinho, Thien Nguyen, Soongho Park, Brian Hill, Babak Shadgan, and Amir Gandjbakhche. "Two-Stream Convolutional Neural Networks for Breathing Pattern Classification: Real-Time Monitoring of Respiratory Disease Patients." Bioengineering 11, no. 7 (2024): 709. http://dx.doi.org/10.3390/bioengineering11070709.

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A two-stream convolutional neural network (TCNN) for breathing pattern classification has been devised for the continuous monitoring of patients with infectious respiratory diseases. The TCNN consists of a convolutional neural network (CNN)-based autoencoder and classifier. The encoder of the autoencoder generates deep compressed feature maps, which contain the most important information constituting data. These maps are concatenated with feature maps generated by the classifier to classify breathing patterns. The TCNN, single-stream CNN (SCNN), and state-of-the-art classification models were
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Speranskaya, A. A., O. P. Baranova, M. A. Vasilyeva, and I. V. Amosov. "RADIATION DIAGNOSIS OF RARE FORMS OF RESPIRATORY ORGAN SARCOIDOSIS." Journal of radiology and nuclear medicine 99, no. 4 (2018): 175–83. http://dx.doi.org/10.20862/0042-4676-2018-99-4-175-183.

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Objective: to evaluate the clinical and radiological features of rare forms of sarcoidosis of the respiratory organs (SRO).Material and methods. In 2006 to 2016, the Research Institute of Interstitial and Orphan Lung Diseases followed up 599 patients with sarcoidosis. 36 patients (6.0%) of them had atypical clinical and radiation manifestations that did not correspond to the traditional radiation pattern and the existing X-ray classification of SRO. Stages 2, 3, and 4 pulmonary sarcoidosis was diagnosed in 26, 7, and 3 patients, respectively. The patients’ mean age was 38.2±7.4 years (the fema
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Boulding, Richard, Rebecca Stacey, Rob Niven, and Stephen J. Fowler. "Dysfunctional breathing: a review of the literature and proposal for classification." European Respiratory Review 25, no. 141 (2016): 287–94. http://dx.doi.org/10.1183/16000617.0088-2015.

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Dysfunctional breathing is a term describing breathing disorders where chronic changes in breathing pattern result in dyspnoea and other symptoms in the absence or in excess of the magnitude of physiological respiratory or cardiac disease. We reviewed the literature and propose a classification system for the common dysfunctional breathing patterns described. The literature was searched using the terms: dysfunctional breathing, hyperventilation, Nijmegen questionnaire and thoraco-abdominal asynchrony. We have summarised the presentation, assessment and treatment of dysfunctional breathing, and
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Hao, Zhanjun, Yue Wang, Fenfang Li, Guozhen Ding, and Yifei Gao. "mmWave-RM: A Respiration Monitoring and Pattern Classification System Based on mmWave Radar." Sensors 24, no. 13 (2024): 4315. http://dx.doi.org/10.3390/s24134315.

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Breathing is one of the body’s most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the sy
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Bahoura, Mohammed. "Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes." Computers in Biology and Medicine 39, no. 9 (2009): 824–43. http://dx.doi.org/10.1016/j.compbiomed.2009.06.011.

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Dokur, Zümray. "Respiratory sound classification by using an incremental supervised neural network." Pattern Analysis and Applications 12, no. 4 (2008): 309–19. http://dx.doi.org/10.1007/s10044-008-0125-y.

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Dissertations / Theses on the topic "Respiratory pattern classification"

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Han, Zixiong. "Respiratory Patterns Classification using UWB Radar." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42332.

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Radar-based respiration monitoring has been increasingly popular among researchers in biomedical fields during the last decades since it is a contactless monitoring technique. It is very convenient for subjects because it does not impose any restrictions on subjects or require their cooperation. Meanwhile, recognizing alternations in respiratory patterns is an important early clue of the diagnosis of several cardiorespiratory diseases. Thus, a study of biomedical radar-based respiration monitoring and respiratory pattern classification is carried out in this thesis. Radar-based respiration mo
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Steuer, Michal. "A modified neocognitron for pattern recognition with an application to respiratory signal classification." Thesis, University of the West of England, Bristol, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275892.

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Book chapters on the topic "Respiratory pattern classification"

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Castorena, Carlos, Francesc J. Ferri, and Maximo Cobos. "On the Performance of Deep Learning Models for Respiratory Sound Classification Trained on Unbalanced Data." In Pattern Recognition and Image Analysis. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04881-4_12.

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Leder, O., and H. Kurz. "Description and Classification of Respiratory Patterns with Multivariate Explorative Statistics." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-46757-8_29.

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Fujikura, Yuji. "Classification of Pneumonia Complicated with Influenza Viral Infection: What Are the Patterns of Pneumonia?" In Respiratory Disease Series: Diagnostic Tools and Disease Managements. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9109-9_11.

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Buaruk, Suphachok, Chayud Srisumarnk, Sivakorn Seinglek, Warisa Thaweekul, and Somrudee Deepaisarn. "Respiratory Disease Classification Using Chest Movement Patterns Measured by Non-contact Sensor." In Advances and Trends in Artificial Intelligence. Theory and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36822-6_34.

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Arabalibeik Hossein, Jafari Samaneh, and Agin Khosro. "Classification of Pulmonary System Diseases Patterns Using Flow-Volume Curve." In Studies in Health Technology and Informatics. IOS Press, 2011. https://doi.org/10.3233/978-1-60750-706-2-25.

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Spirometry is the most common pulmonary function test. It provides useful information for early detection of respiratory system abnormalities. While decision support systems use normally calculated parameters such as FEV1, FVC, and FEV1% to diagnose the pattern of respiratory system diseases, expert physicians pay close attention to the pattern of the flow-volume curve as well. Fisher discriminant analysis shows that coefficients of a simple polynomial function fitted to the curve, can capture the information about the disease patterns much better than the familiar single point parameters. A n
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Giraldo, B. F., A. Garde, C. Arizmendi, R. Jané, I. Diaz, and S. Benito. "Support Vector Machine Classification applied on Weaning Trials Patients." In Encyclopedia of Healthcare Information Systems. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-889-5.ch160.

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The most common reason for instituting mechanical ventilation is to decrease a patient’s work of breathing. Many attempts have been made to increase the effectiveness on the evaluation of the respiratory pattern by means of respiratory signal analysis. This work suggests a method of studying the lying differences in respiratory pattern variability between patients on weaning trials. The core of the proposed method is the use of support vector machines to classify patients into two groups, taking into account 35 features of each one, previously extracted from the respiratory flow. 146 patients
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Kent, Brian. "Sleep-disordered breathing and its associations." In Oxford Handbook of Sleep Medicine. Oxford University Press, 2022. http://dx.doi.org/10.1093/med/9780192848253.003.0008.

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Respiratory function is significantly altered by sleep. People with entirely normal breathing when awake can develop very significant respiratory compromise during sleep. Following sleep onset, central motor neuron output falls. This means that diminished upper airway dilator muscle function leads to a narrower upper airway, while reduced inspiratory muscle activity causes a shallower breathing pattern compared to the awake state. Within this chapter, we discuss the classification of sleep-disordered breathing, its epidemiology, clinical features and associated co-morbidities.
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Arizmendi Carlos, Viviescas Juan, González Hernando, and Giraldo Beatriz. "Patients Classification on Weaning Trials Using Neural Networks and Wavelet Transform." In Studies in Health Technology and Informatics. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-423-7-107.

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The determination of the optimal time of the patients in weaning trial process from mechanical ventilation, between patients capable of maintaining spontaneous breathing and patients that fail to maintain spontaneous breathing, is a very important task in intensive care unit. Wavelet Transform (WT) and Neural Networks (NN) techniques were applied in order to develop a classifier for the study of patients on weaning trial process. The respiratory pattern of each patient was characterized through different time series. Genetic Algorithms (GA) and Forward Selection were used as feature selection
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Hernández-Pereira Elena, Álvarez-Estévez Diego, and Moret-Bonillo Vicente. "Improving detection of apneic events by learning from examples and treatment of missing data." In Studies in Health Technology and Informatics. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-474-9-213.

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This paper presents a comparative study over the respiratory pattern classification task involving three missing data imputation techniques, and four different machine learning algorithms. The main goal was to find a classifier that achieves the best accuracy results using a scalable imputation method in comparison to the method used in a previous work of the authors. The results obtained show that the Self-organization maps imputation method allows any classifier to achieve improvements over the rest of the imputation methods, and that the Feedforward neural network classifier offers the best
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Tejaswini, S., N. Sriraam, and Pradeep G. C. M. "Identification of High Risk and Low Risk Preterm Neonates in NICU." In Biomedical and Clinical Engineering for Healthcare Advancement. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0326-3.ch007.

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Infant cries are referred as the biological indicator where infant distress is expressed without any external stimulus. One can assess the physiological changes through cry characteristics that help in improving clinical decision. In a typical Neonatal Intensive Care Unit (NICU), recognizing high-risk and low-risk admitted preterm neonates is quite challenging and complex in nature. This chapter attempts to develop pattern recognition-based approach to identify high-risk and low-risk preterm neonates in NICU. Four clinical conditions were considered: two Low Risk (LR) and two High Risk (HR), L
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Conference papers on the topic "Respiratory pattern classification"

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He, Mingxu, Zongjie Cao, Qian Liu, and Zongyong Cui. "A Real-Time Respiratory Pattern Classification System Based on Edge Computing for 60 Ghz mmWave Radar." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642053.

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Andrade Rodriguez, Rafael, Jireh Ferroa-Guzman, and Willy Ugarte. "Classification of Respiratory Diseases Using the NAO Robot." In 12th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011782700003411.

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Yao, Yao, Bo Li, Rongchuan Sun, Shumei Yu, and Lining Sun. "SVM Based Human Respiratory Pattern Classification Method for Stereo Radiotherapy Robot." In 2021 China Automation Congress (CAC). IEEE, 2021. http://dx.doi.org/10.1109/cac53003.2021.9727363.

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Latifi, Seyed Amir, Hassan Ghassemian, and Maryam Imani. "Feature Extraction and Classification of Respiratory Sound and Lung Diseases." In 2023 6th International Conference on Pattern Recognition and Image Analysis (IPRIA). IEEE, 2023. http://dx.doi.org/10.1109/ipria59240.2023.10147191.

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Guo, Yin, Nicha Dvornek, Yihuan Lu, et al. "Deep Learning based Respiratory Pattern Classification and Applications in PET/CT Motion Correction." In 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2019. http://dx.doi.org/10.1109/nss/mic42101.2019.9059783.

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Chu, Yun, QiuHao Wang, EnZe Zhou, Gang Zheng, and Qian Liu. "Hybrid Spectrogram for the Automatic Respiratory Sound Classification with Group Time Frequency Attention Network." In 2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2023. http://dx.doi.org/10.1109/prai59366.2023.10332031.

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Park, Jinho, Thien Nguyen, and Amir Gandjbakhche. "Deep-learning-based breathing pattern classification method for real-time monitoring of patients with infectious respiratory disease." In Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables V, edited by Babak Shadgan and Amir H. Gandjbakhche. SPIE, 2024. http://dx.doi.org/10.1117/12.3003602.

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Reports on the topic "Respiratory pattern classification"

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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7587232.bard.

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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel se
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