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They extracted several features, such as Mel-frequency cepstral coefficients (MFCCs) and the zero crossing rate (ZCR), and further used a logistic regression (LR) model-based classifier. (2016), a pertussis identification algorithm is proposed by using cough and whoop sounds. Thus, respiratory sounds such as cough, speech, and breath may be an important biomarker for COVID-19 diagnosis.Īudio signals generated by body structures and organs have been widely explored for diagnosis and monitoring of diseases by clinicians and clinical researchers ( Brown et al., 2020). About 25% of patients with mild-to-moderate COVID-19 have been found to have dysphonia since multiple body structures are linked to human voice generation, such as the lungs, vocal folds, and laryngeal muscle ( Suppakitjanusant et al., 2021). This infection affects the respiratory system and includes symptoms such as fever, dry cough, dyspnea, headache, sputum production, hemoptysis, myalgia, fatigue, nausea, vomiting, diarrhea, abdominal pain, and loss of smell and taste ( Oliveira et al., 2020 Rai et al., 2021). In Brazil, more than 33 million cases have been reported (with more than 678,000 deaths), and in Canada, more than 4 million cases have been reported (and almost 43,000 deaths) (Johns Hopkins University, 2022).Ĭlinical manifestations of COVID-19 infection vary from asymptomatic to symptomatic. According to information provided by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), accessed on 31 July 2022, almost 548 million people have been infected and more than 6.4 million people have died in the last two years. The results show that the proposed method has performed well for cough, speech, and breath sound classification, with a UAR up to 100.00%, 60.67%, and 95.00%, respectively, to infer COVID-19 infection, which serves as an effective tool to perform a preliminary screening of COVID-19.ĬOVID-19 (coronavirus disease 2019) is a contagious infectious disease caused by the new SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus, which was declared a global pandemic on 11 February 2020, by the World Health Organization (WHO) ( World Health Organization, 2020). The results have been evaluated in terms of an unweighted average recall (UAR). Cough, speech, and breath sounds from the INTERSPEECH 2021 ComParE and Cambridge KDD databases have been processed and analyzed to evaluate our proposed feature extraction method with ML techniques in order to distinguish between positive or negative for COVID-19 sounds. We hypothesize that this textural sound analysis based on local binary patterns and local ternary patterns enables us to obtain a better classification model by discriminating both people with COVID-19 and healthy subjects. This approach of the feature extraction method has not been widely used for biomedical sounds, particularly for COVID-19 or respiratory diseases. In this work, we propose an audio texture analysis of sounds emitted by subjects in suspicion of COVID-19 infection using time–frequency spectrograms. In recent years, a variety of techniques for discriminating textures and computationally efficient local texture descriptors have been introduced, such as local binary patterns and local ternary patterns, among others. Joint time–frequency feature extraction techniques and machine learning (ML)-based models have been widely explored in respiratory diseases such as influenza, pertussis, and COVID-19 to find biomarkers from human respiratory system-generated acoustic sounds. Since the COVID-19 outbreak, a major scientific effort has been made by researchers and companies worldwide to develop a digital diagnostic tool to screen this disease through some biomedical signals, such as cough, and speech. 4Edmond and Lily Safra International Institute of Neurosciences, Santos Dumont Institute, Macaiba, Brazil.3Signal Analysis Research Group, Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.
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Leticia Silva 2,3* Carlos Valadão 2 Lucas Lampier 2 Denis Delisle-Rodríguez 4 Eliete Caldeira 1 Teodiano Bastos-Filho 1,2 Sridhar Krishnan 3