Relevant bibliographies by topics / Respiratory pattern classification

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

Academic literature on the topic 'Respiratory pattern classification'

Author: Grafiati
Published: 27 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Respiratory pattern classification"

1

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 (August 31, 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 female/male ratio was 26:10). All the patients underwent traditional X-ray studies (radiography in two projections), high-resolution computed tomography (CT), complex external respiratory function examination, and echocardiography.Results. Analysis of the results of radiation examinations revealed the following rare forms of SRO: interstitial edematous, fibrous, and cavitary ones that had recognizable CT patterns. Each of these forms had clinical and functional features. In single cases, the CT pattern combined the features incompatible with the generally accepted classification (Stages 1 and 4 SRO); this was an offstage form. The features of the radiation pattern and clinical course required the differential diagnosis of these patients with more severe diseases (idiopathic pulmonary fibrosis, lymphogenic carcinomatosis, pulmonary edema, diffuse connective tissue diseases, pneumoconiosis, mycobacteriosis, and exogenous allergic alveolitis) and morphological verification.Conclusion. The accumulation of experience with clinical and radiological examination of patients with SRO revealed its rare forms that are difficult to diagnose and necessitate the development of new approaches to therapy policy.
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Dokur, Zümray. "Respiratory sound classification by using an incremental supervised neural network." Pattern Analysis and Applications 12, no. 4 (June 10, 2008): 309–19. http://dx.doi.org/10.1007/s10044-008-0125-y.

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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 (September 2009): 824–43. http://dx.doi.org/10.1016/j.compbiomed.2009.06.011.

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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 (August 31, 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 propose that the following system be used for classification. 1) Hyperventilation syndrome: associated with symptoms both related to respiratory alkalosis and independent of hypocapnia. 2) Periodic deep sighing: frequent sighing with an irregular breathing pattern. 3) Thoracic dominant breathing: can often manifest in somatic disease, if occurring without disease it may be considered dysfunctional and results in dyspnoea. 4) Forced abdominal expiration: these patients utilise inappropriate and excessive abdominal muscle contraction to aid expiration. 5) Thoraco-abdominal asynchrony: where there is delay between rib cage and abdominal contraction resulting in ineffective breathing mechanics.This review highlights the common abnormalities, current diagnostic methods and therapeutic implications in dysfunctional breathing. Future work should aim to further investigate the prevalence, clinical associations and treatment of these presentations.
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DOKUR, ZÜMRAY, and TAMER ÖLMEZ. "CLASSIFICATION OF RESPIRATORY SOUNDS BY USING AN ARTIFICIAL NEURAL NETWORK." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 04 (June 2003): 567–80. http://dx.doi.org/10.1142/s0218001403002526.

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In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. Wavelet transform is applied to a window containing 256 samples. Elements of the feature vectors are obtained from the wavelet coefficients. The best feature elements are selected by using dynamic programming. Grow and Learn (GAL) neural network, Kohonen network and multi-layer perceptron (MLP) are used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.
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Wang, Qisong, Zhening Dong, Dan Liu, Tianao Cao, Meiyan Zhang, Runqiao Liu, Xiaocong Zhong, and Jinwei Sun. "Frequency-Modulated Continuous Wave Radar Respiratory Pattern Detection Technology Based on Multifeature." Journal of Healthcare Engineering 2021 (August 9, 2021): 1–18. http://dx.doi.org/10.1155/2021/9376662.

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Respiratory diseases including apnea are often accompanied by abnormal respiratory depth, frequency, and rhythm. If different abnormal respiratory patterns can be detected and recorded, with their depth, frequency, and rhythm analyzed, the detection and diagnosis of respiratory diseases can be achieved. High-frequency millimeter-wave radar (76–81 GHz) has low environmental impact, high accuracy, and small volume, which is more suitable for respiratory signal detection and recognition compared with other contact equipment. In this paper, the experimental platform of frequency-modulated continuous wave (FMCW) radar was built at first, realizing the noncontact measurement of vital signs. Secondly, the energy intensity and threshold of respiration signal during each period were calculated by using the rectangular window, and the accurate judgment of apnea was realized via numerical comparison. Thirdly, the features of respiratory and heart rate signals, the number of peaks and valleys, the difference between peaks and valleys, the average and the standard deviation of normalized short-term energy, and the average and the standard deviation and the minimum of instantaneous frequency, were extracted and analyzed. Finally, support vector machine (SVM) and K-nearest neighbor (KNN) were used to classify the extracted features, and the accuracy was 98.25% and 88.75%, respectively. The classification and recognition of respiratory patterns have been successfully realized.
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Stacey, RM, A. Vyas, and SJ Fowler. "P231 Breathing pattern disorders in a complex breathlessness service; classification and clinical characteristics." Thorax 71, Suppl 3 (November 15, 2016): A212.1—A212. http://dx.doi.org/10.1136/thoraxjnl-2016-209333.374.

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Hung, Jung-Jyh, Yi-Chen Yeh, Wen-Juei Jeng, Kou-Juey Wu, Biing-Shiun Huang, Yu-Chung Wu, Teh-Ying Chou, and Wen-Hu Hsu. "Predictive Value of the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society Classification of Lung Adenocarcinoma in Tumor Recurrence and Patient Survival." Journal of Clinical Oncology 32, no. 22 (August 1, 2014): 2357–64. http://dx.doi.org/10.1200/jco.2013.50.1049.

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Purpose This study investigated the pattern of recurrence of lung adenocarcinoma and the predictive value of histologic classification in resected lung adenocarcinoma using the new International Association for the Study of Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) classification system. Patients and Methods Histologic classification of 573 patients undergoing resection for lung adenocarcinoma was determined according to the IASLC/ATS/ERS classification system, and the percentage of each histologic component (lepidic, acinar, papillary, micropapillary, and solid) was recorded. The pattern of recurrence of those components and their predictive value were investigated. Results The predominant histologic pattern was significantly associated with sex (P < .01), invasive tumor size (P < .01), T status (P < .01), N status (P < .01), TNM stage (P < .01), and visceral pleural invasion (P < .01). The percentage of recurrence was significantly higher in micropapillary- and solid-predominant adenocarcinomas (P < .01). Micropapillary- and solid-predominant adenocarcinomas had a significantly higher possibility of developing initial extrathoracic-only recurrence than other types (P < .01). The predominant pattern group (micropapillary or solid v lepidic, acinar, or papillary) was a significant prognostic factor in overall survival (OS; P < .01), probability of freedom from recurrence (P < .01), and disease-specific survival (P < .01) in multivariable analysis. For patients receiving adjuvant chemotherapy, solid-predominant adenocarcinoma was a significant predictor for poor OS (P = .04). Conclusion In lung adenocarcinoma, the IASLC/ATS/ERS classification system has significant prognostic and predictive value regarding death and recurrence. Solid-predominant adenocarcinoma was also a significant predictor in patients undergoing adjuvant chemotherapy. Prognostic and predictive information is important for stratifying patients for aggressive adjuvant chemoradiotherapy.
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Warth, Arne, Thomas Muley, Michael Meister, Albrecht Stenzinger, Michael Thomas, Peter Schirmacher, Philipp A. Schnabel, Jan Budczies, Hans Hoffmann, and Wilko Weichert. "The Novel Histologic International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society Classification System of Lung Adenocarcinoma Is a Stage-Independent Predictor of Survival." Journal of Clinical Oncology 30, no. 13 (May 1, 2012): 1438–46. http://dx.doi.org/10.1200/jco.2011.37.2185.

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Purpose Our aim was to analyze and validate the prognostic impact of the novel International Association for the Study of Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) proposal for an architectural classification of invasive pulmonary adenocarcinomas (ADCs) across all tumor stages. Patients and Methods The architectural pattern of a large cohort of 500 patients with resected ADCs (stages I to IV) was retrospectively analyzed in 5% increments and classified according to their predominant architecture (lepidic, acinar, solid, papillary, or micropapillary), as proposed by the IASLC/ATS/ERS. Subsequently, histomorphologic data were correlated with clinical data, adjuvant therapy, and patient outcome. Results Overall survival differed significantly between lepidic (78.5 months), acinar (67.3 months), solid (58.1 months), papillary (48.9 months), and micropapillary (44.9 months) predominant ADCs (P = .007). When patterns were lumped into groups, this resulted in even more pronounced differences in survival (pattern group 1, 78.5 months; group 2, 67.3 months; group 3, 57.2 months; P = .001). Comparable differences were observed for overall, disease-specific, and disease-free survival. Pattern and pattern groups were stage- and therapy-independent prognosticators for all three survival parameters. Survival differences according to patterns were influenced by adjuvant chemoradiotherapy; in particular, solid-predominant tumors had an improved prognosis with adjuvant radiotherapy. The predominant pattern was tightly linked to the risk of developing nodal metastases (P < .001). Conclusion Besides all recent molecular progress, architectural grading of pulmonary ADCs according to the novel IASLC/ATS/ERS scheme is a rapid, straightforward, and efficient discriminator for patient prognosis and may support patient stratification for adjuvant chemoradiotherapy. It should be part of an integrated clinical, morphologic, and molecular subtyping to further improve ADC treatment.
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Purnomo, Ariana Tulus, Ding-Bing Lin, Tjahjo Adiprabowo, and Willy Fitra Hendria. "Non-Contact Monitoring and Classification of Breathing Pattern for the Supervision of People Infected by COVID-19." Sensors 21, no. 9 (May 3, 2021): 3172. http://dx.doi.org/10.3390/s21093172.

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During the pandemic of coronavirus disease-2019 (COVID-19), medical practitioners need non-contact devices to reduce the risk of spreading the virus. People with COVID-19 usually experience fever and have difficulty breathing. Unsupervised care to patients with respiratory problems will be the main reason for the rising death rate. Periodic linearly increasing frequency chirp, known as frequency-modulated continuous wave (FMCW), is one of the radar technologies with a low-power operation and high-resolution detection which can detect any tiny movement. In this study, we use FMCW to develop a non-contact medical device that monitors and classifies the breathing pattern in real time. Patients with a breathing disorder have an unusual breathing characteristic that cannot be represented using the breathing rate. Thus, we created an Xtreme Gradient Boosting (XGBoost) classification model and adopted Mel-frequency cepstral coefficient (MFCC) feature extraction to classify the breathing pattern behavior. XGBoost is an ensemble machine-learning technique with a fast execution time and good scalability for predictions. In this study, MFCC feature extraction assists machine learning in extracting the features of the breathing signal. Based on the results, the system obtained an acceptable accuracy. Thus, our proposed system could potentially be used to detect and monitor the presence of respiratory problems in patients with COVID-19, asthma, etc.
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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 monitoring technology has a shortcoming that the collected respiratory signal will be easily distorted by the body movement of the monitoring subjects or disturbed by environment noise because of the contactless measurement attribute. This shortcoming limits the application of the respiratory pattern classification model, that is, the existing models cannot be applied automatically since the distorted respiratory signal needs to be manually filtered out ahead of the classification. In this study, a new respiratory pattern classification strategy, which can be implemented full-automatic, is proposed. In this strategy, a class “moving” is introduced to classify the distorted signal, and the sampling window length is shortened to reduce the effect caused by the signal distortion. A performance requirement for the continuous respiratory pattern classification is also proposed based on its expected function that can alert the occurrence of the abnormal breathing patterns. Several models which can meet the proposed performance requirement are developed in this thesis based on the state-of-the-art pattern classification technique and the time-series-based shapelet transform algorithm. The proposed models can classify four breathing patterns including eupnea, Cheyne Stokes respiration, Kussmaul breathing and apnea. A radar-collected respiratory signal database is built in this study, and a respiration simulation model which can generate breath samples for pattern classification is developed in this thesis. The proposed models were tested and validated in batch and stream processing manner with independently collected data and continuously collected data, respectively.
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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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Leder, O., and H. Kurz. "Description and Classification of Respiratory Patterns with Multivariate Explorative Statistics." In Studies in Classification, Data Analysis, and Knowledge Organization, 285–88. Berlin, Heidelberg: 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, 107–14. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9109-9_11.

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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, 1277–82. 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 from mechanical ventilation were studied: Group S of 79 patients with Successful trials, and Group F of 67 patients that Failed on the attempt to maintain spontaneous breathing and had to be reconnected. Applying a feature selection procedure based on the use of the support vector machine with leave-one-out cross-validation, it was obtained 86.67% of well classified patients into the Group S and 73.34% into Group F, using only eight of the 35 features. Therefore, support vector machines can be an interesting classification method in the study of the respiratory pattern variability.
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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, 119–40. 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), LR1- Appropriate Gestational Age (AGA), LR2- Intrauterine Growth Restriction (IUGR), HR1-Respiratory Distress Syndrome (RDS), and HR2- Premature Rupture of Membranes (PROM). An overall cry unit of 800 (n=20 per condition) was used for the proposed study. After appropriate pre-processing, Bark Frequency Cepstral Coefficient (BFCC) was estimated using three methods. Schroeder, Zwicker and Terhardt; and Transmiller; and a non-linear Support Vector Machine (SVM) Classifier were employed to discriminate low-risk and high-risk groups. From the simulation results, it was observed that sensitivity specificity and accuracy of 91.47%, 91.42%, and 92.9% respectively were obtained using the BFCC estimated for classifying high risk and low risk with SVM classification.
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Conference papers on the topic "Respiratory pattern classification"

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Guo, Yin, Nicha Dvornek, Yihuan Lu, Yu-Jung Tsai, James Hamill, Michael Casey, and Chi Liu. "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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