scholarly journals Effects of Bronchodilators on Regional Lung Sound Distribution in Patients with Chronic Obstructive Pulmonary Disease

Respiration ◽  
2014 ◽  
Vol 87 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Masamichi Mineshita ◽  
Shin Matsuoka ◽  
Teruomi Miyazawa
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Chronic Obstructive ◽  
Lung Sound ◽  
Obstructive Pulmonary Disease ◽  
Regional Lung

scholarly journals Regional Lung Sound Asynchrony in Chronic Obstructive Pulmonary Disease Patients

Respiration ◽  
2016 ◽  
Vol 92 (4) ◽  
pp. 252-257 ◽  
Author(s):  
Masamichi Mineshita ◽  
Hirotaka Kida ◽  
Hiroshi Handa ◽  
Hiroki Nishine ◽  
Naoki Furuya ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Chronic Obstructive ◽  
Lung Sound ◽  
Obstructive Pulmonary Disease ◽  
Regional Lung

scholarly journals An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6512
Author(s):  
Syed Zohaib Hassan Naqvi ◽  
Mohammad Ahmad Choudhry
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Sampling Technique ◽  
Automated System ◽  
Chronic Obstructive ◽  
Discrete Wavelet ◽  
Lung Sound ◽  
Noninvasive Technique ◽  
Sound Analysis ◽  
Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) and pneumonia are two of the few fatal lung diseases which share common adventitious lung sounds. Diagnosing the disease from lung sound analysis to design a noninvasive technique for telemedicine is a challenging task. A novel framework is presented to perform a diagnosis of COPD and Pneumonia via application of the signal processing and machine learning approach. This model will help the pulmonologist to accurately detect disease A and B. COPD, normal and pneumonia lung sound (LS) data from the ICBHI respiratory database is used in this research. The performance analysis is evidence of the improved performance of the quadratic discriminate classifier with an accuracy of 99.70% on selected fused features after experimentation. The fusion of time domain, cepstral, and spectral features are employed. Feature selection for fusion is performed through the back-elimination method whereas empirical mode decomposition (EMD) and discrete wavelet transform (DWT)-based techniques are used to denoise and segment the pulmonic signal. Class imbalance is catered with the implementation of the adaptive synthetic (ADASYN) sampling technique.

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