{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T14:55:17Z","timestamp":1769525717983,"version":"3.49.0"},"reference-count":38,"publisher":"Walter de Gruyter GmbH","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Chronic obstructive pulmonary disease (COPD) and pneumonia are lethal pulmonary illnesses with equivocal nature of abnormal pulmonic acoustics. Using lung sound signals, the classification of pulmonary abnormalities is a difficult task. A standalone system was conceived for screening COPD and Pneumonia patients through signal processing and machine learning methodologies. The proposed system will assist practitioners and pulmonologists in the accurate classification of disease. In this research work, ICBHI\u2019s and self-collected lung sound (LS) databases are used to investigate COPD and pneumonia patient. In this scheme, empirical mode decomposition (EMD), discrete wavelet transform (DWT), and analysis of variance (ANOVA) techniques are employed for segmentation, noise elimination, and feature selection, respectively. To overcome the inherent limitation of ICBHI\u2019s LS database, the adaptive synthetic (ADASYN) sampling technique is used to eradicate class imbalance. Lung sound features are used to train fine Gaussian support vector machine (FG-SVM) for classification of COPD, pneumonia, and heathy healthy subjects. This machine learning scheme is implemented on low cost and portable Raspberry pi 3 model B+ (Cortex-A53 (ARMv8) 64-bit SoC @ 1.4\u00a0GHz through hardware-supported language. Resultant hardware is capable of screening COPD and pneumonia patients accurately and assist health professionals.<\/jats:p>","DOI":"10.1515\/bmt-2022-0011","type":"journal-article","created":{"date-parts":[[2022,4,11]],"date-time":"2022-04-11T16:49:23Z","timestamp":1649695763000},"page":"201-218","source":"Crossref","is-referenced-by-count":6,"title":["Embedded system design for classification of COPD and pneumonia patients by lung sound analysis"],"prefix":"10.1515","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7771-8294","authenticated-orcid":false,"given":"Syed Zohaib","family":"Hassan Naqvi","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering , University of Engineering and Technology Taxila , Taxila , Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9901-6309","authenticated-orcid":false,"given":"Mohmmad Ahmad","family":"Choudhry","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering , University of Engineering and Technology Taxila , Taxila , Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2022,4,11]]},"reference":[{"key":"2023033112173759290_j_bmt-2022-0011_ref_001","doi-asserted-by":"crossref","unstructured":"Rai, DK, Sharma, P, Kumar, R. 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