scholarly journals Diagnosis of Chronic Obstructive Pulmonary Disease from Lung Sounds using Support Vector Machine

2020 ◽  
Vol V9 (09) ◽  
Author(s):  
John Amose ◽  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Support Vector Machine ◽  
Pulmonary Disease ◽  
Support Vector ◽  
Chronic Obstructive ◽  
Lung Sounds ◽  
Obstructive Pulmonary Disease

scholarly journals Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hui Yu ◽  
Jing Zhao ◽  
Dongyi Liu ◽  
Zhen Chen ◽  
Jinglai Sun ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Sensitivity And Specificity ◽  
Classification Performance ◽  
Support Vector ◽  
Chronic Obstructive ◽  
Lung Sounds ◽  
Obstructive Pulmonary Disease ◽  
Preliminary Diagnosis ◽  
Severe Copd

Abstract Background Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously threatens people’s health, with high morbidity and mortality worldwide. At present, the clinical diagnosis methods of COPD are time-consuming, invasive, and radioactive. Therefore, it is urgent to develop a non-invasive and rapid COPD severity diagnosis technique suitable for daily screening in clinical practice. Results This study established an effective model for the preliminary diagnosis of COPD severity using lung sounds with few channels. Firstly, the time-frequency-energy features of 12 channels lung sounds were extracted by Hilbert–Huang transform. And then, channels and features were screened by the reliefF algorithm. Finally, the feature sets were input into a support vector machine to diagnose COPD severity, and the performance with Bayes, decision tree, and deep belief network was compared. Experimental results show that high classification performance using only 4-channel lung sounds of L1, L2, L3, and L4 channels can be achieved by the proposed model. The accuracy, sensitivity, and specificity of mild COPD and moderate + severe COPD were 89.13%, 87.72%, and 91.01%, respectively. The classification performance rates of moderate COPD and severe COPD were 94.26%, 97.32%, and 89.93% for accuracy, sensitivity, and specificity, respectively. Conclusion This model provides a standardized evaluation with high classification performance rates, which can assist doctors to complete the preliminary diagnosis of COPD severity immediately, and has important clinical significance.

Classification of Exacerbation Episodes in Chronic Obstructive Pulmonary Disease Patients

2014 ◽  
Vol 53 (02) ◽  
pp. 108-114 ◽  
Author(s):  
L. Gorzelniak ◽  
K. Schultz ◽  
M. Wittmann ◽  
J. Rudnik ◽  
R. Jörres ◽  
...  
Keyword(s):  
Physical Activity ◽  
Neural Networks ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Feature Selection Method ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Copd Patients

Summary Background: Chronic obstructive pulmonary disease (COPD) is a progressive disease affecting the airways, which constitutes a major cause of chronic morbidity and a significant economic and social burden throughout the world. Despite the fact that in COPD patients exacerbations are common acute events causing significant and often fatal worsening of symptoms, an accurate prognostication continues to be difficult. Objectives: To build computational models capable of distinguishing between normal life days from exacerbation days in COPD patients, based on physical activity measured by accelerometers. Methods: We recruited 58 patients suffering from COPD and measured their physical activity with accelerometers for 10 days or more, from August 2009 to March 2010. During this period we recorded six exacerbation episodes in the patients, accounting for 37 days. We were able to analyse data for 52 patients (369 patient days), and extracted three distinct sets of features from the data, one set of basic features such as average, one set based on the frequency domain and the last exploring the cross-information among sensors pairs. These were used by three machine-learning techniques (logarith mic regression, neural networks, support vector machines) to distinguish days with exacerbation events from normal days. Results: The support vector machine clas -sifier achieved an AUC of 90% ± 9, when supplied with a set of features resulting from sequential feature selection method. Neural networks achieved an AUC of 83% ± 16 and the logarithmic regression an AUC of 67% ± 15. Conclusions: None of the individual feature sets provided robust for reasonable classi -fication of PA recording days. Our results indicate that this approach has the potential to extract useful information for, but are not robust enough for medical application of the system.

scholarly journals Multi-omics subtyping pipeline for chronic obstructive pulmonary disease

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255337
Author(s):  
Lucas A. Gillenwater ◽  
Shahab Helmi ◽  
Evan Stene ◽  
Katherine A. Pratte ◽  
Yonghua Zhuang ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
The United States ◽  
Support Vector ◽  
Chronic Obstructive ◽  
Data Sets ◽  
Omics Data ◽  
Obstructive Pulmonary Disease ◽  
Clinical Phenotypes ◽  
Molecular Features

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity.

Oral and Oropharyngeal Sensory Function in Adults With Chronic Obstructive Pulmonary Disease

2020 ◽  
Vol 29 (2) ◽  
pp. 864-872
Author(s):  
Fernanda Borowsky da Rosa ◽  
Adriane Schmidt Pasqualoto ◽  
Catriona M. Steele ◽  
Renata Mancopes
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Oral Cavity ◽  
Pulmonary Disease ◽  
Thermal Sensation ◽  
Sensory Function ◽  
Control Group ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Age Related ◽  
Copd Patients

Introduction The oral cavity and pharynx have a rich sensory system composed of specialized receptors. The integrity of oropharyngeal sensation is thought to be fundamental for safe and efficient swallowing. Chronic obstructive pulmonary disease (COPD) patients are at risk for oropharyngeal sensory impairment due to frequent use of inhaled medications and comorbidities including gastroesophageal reflux disease. Objective This study aimed to describe and compare oral and oropharyngeal sensory function measured using noninstrumental clinical methods in adults with COPD and healthy controls. Method Participants included 27 adults (18 men, nine women) with a diagnosis of COPD and a mean age of 66.56 years ( SD = 8.68). The control group comprised 11 healthy adults (five men, six women) with a mean age of 60.09 years ( SD = 11.57). Spirometry measures confirmed reduced functional expiratory volumes (% predicted) in the COPD patients compared to the control participants. All participants completed a case history interview and underwent clinical evaluation of oral and oropharyngeal sensation by a speech-language pathologist. The sensory evaluation explored the detection of tactile and temperature stimuli delivered by cotton swab to six locations in the oral cavity and two in the oropharynx as well as identification of the taste of stimuli administered in 5-ml boluses to the mouth. Analyses explored the frequencies of accurate responses regarding stimulus location, temperature and taste between groups, and between age groups (“≤ 65 years” and “> 65 years”) within the COPD cohort. Results We found significantly higher frequencies of reported use of inhaled medications ( p < .001) and xerostomia ( p = .003) in the COPD cohort. Oral cavity thermal sensation ( p = .009) was reduced in the COPD participants, and a significant age-related decline in gustatory sensation was found in the COPD group ( p = .018). Conclusion This study found that most of the measures of oral and oropharyngeal sensation remained intact in the COPD group. Oral thermal sensation was impaired in individuals with COPD, and reduced gustatory sensation was observed in the older COPD participants. Possible links between these results and the use of inhaled medication by individuals with COPD are discussed.

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