{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pay-attention-to-the-cough-early-diagnosis-of","title":"Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing","arxiv_id":"2010.02417","date":"2020-10-06","proceeding":null,"authors":["Ankit Pal","Malaikannan Sankarasubbu"],"abstract":"COVID-19 (coronavirus disease 2019) pandemic caused by SARS-CoV-2 has led to a treacherous and devastating catastrophe for humanity. At the time of writing, no specific antivirus drugs or vaccines are recommended to control infection transmission and spread. The current diagnosis of COVID-19 is done by Reverse-Transcription Polymer Chain Reaction (RT-PCR) testing. However, this method is expensive, time-consuming, and not easily available in straitened regions. An interpretable and COVID-19 diagnosis AI framework is devised and developed based on the cough sounds features and symptoms metadata to overcome these limitations. The proposed framework's performance was evaluated using a medical dataset containing Symptoms and Demographic data of 30000 audio segments, 328 cough sounds from 150 patients with four cough classes ( COVID-19, Asthma, Bronchitis, and Healthy). Experiments' results show that the model captures the better and robust feature embedding to distinguish between COVID-19 patient coughs and several types of non-COVID-19 coughs with higher specificity and accuracy of 95.04 $\\pm$ 0.18% and 96.83$\\pm$ 0.18% respectively, all the while maintaining interpretability.","url_abs":"https://arxiv.org/abs/2010.02417v2","url_pdf":"https://arxiv.org/pdf/2010.02417v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pay-attention-to-the-cough-early-diagnosis-of","repo_url":"https://github.com/coughresearch/Cough-signal-processing","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"covid-19-detection","task_name":"COVID-19 Diagnosis"},{"task_slug":"specificity","task_name":"Specificity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}