ACADSTAFF UGM

CREATION
Title : Fast and noninvasive electronic nose for sniffing-out COVID-19 based on exhaled breath-print recognition
Author :

dr. Dian Kesumapramudya Nurputra, M.Sc, Ph.D, SpA (1) Dr.Eng. Ahmad Kusumaatmaja, S.Si., M.Sc. (2) dr. Mohamad Saifudin Hakim, M.Sc, Ph.D. (3) SHIDIQ NUR HIDAYAT (4) TRISNA JULIAN (5) BUDI SUMANTO (6) Prof. dr. Yodi Mahendradhata, M.Sc., PhD, FRSPH (7) dr. Antonia Morita Iswari Saktiawati., Ph.D (8) Hutomo Suryo Wasisto (9) Prof. Dr. Eng. Kuwat Triyana, M.Si. (10)
Date : 16 2022
Keyword : electronic nose, breathalyzer, SARS-CoV-2, COVID-19, GeNose, profiling test, pattern recognition, gas sensor, machine learning electronic nose, breathalyzer, SARS-CoV-2, COVID-19, GeNose, profiling test, pattern recognition, gas sensor, machine learning
Abstract : Despite its high accuracy to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach possesses several limitations (e.g., the lengthy invasive procedure, the reagent availability, and the requirement of specialized laboratory, equipment, and trained staffs). We developed and employed a low-cost, noninvasive method to rapidly sniff out the coronavirus disease 2019 (COVID-19) based on a portable electronic nose (GeNose C19) integrating metal oxide semiconductor gas sensor array, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total number of 615 breath samples (i.e., 333 positive and 282 negative COVID-19 confirmed by RT-qPCR) obtained from 83 patients in two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis (LDA), support vector machine (SVM), stacked multilayer perceptron (MLP), and deep neural network (DNN)) were utilized to identify the top-performing pattern recognition methods and to obtain high system detection accuracy (88–95%), sensitivity (86–94%), specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.
Group of Knowledge : Ilmu Kedokteran Klinik
Original Language : English
Level : Internasional
Status :
Published
Document
No Title Document Type Action
1 s41746-022-00661-2.pdf
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2 Fast and noninvasive electronic nose for sniffing-out COVID-19 based on exhaled breath-print recognition.pdf
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3 Dokumen Korespondensi Paper GeNose NPJDigital MEdicine.pdf
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4 Similarity_Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition.pdf
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