Literature DB >> 32487005

Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds.

Miad Faezipour1,2, Abdelshakour Abuzneid1.   

Abstract

Telemedicine could be a key to control the world-wide disruptive and spreading novel coronavirus disease (COVID-19) pandemic. The COVID-19 virus directly targets the lungs, leading to pneumonia-like symptoms and shortness of breath with life-threatening consequences. Despite the fact that self-quarantine and social distancing are indispensable during the pandemic, the procedure for testing COVID-19 contraction is conventionally available through nasal swabs, saliva test kits, and blood work at healthcare settings. Therefore, devising personalized self-testing kits for COVID-19 virus and other similar viruses is heavily admired. Many e-health initiatives have been made possible by the advent of smartphones with embedded software, hardware, high-performance computing, and connectivity capabilities. A careful review of breathing sounds and their implications in identifying breathing complications suggests that the breathing sounds of COVID-19 contracted users may reveal certain acoustic signal patterns, which is worth investigating. To this end, acquiring respiratory data solely from breathing sounds fed to the smartphone's microphone strikes as a very appealing resolution. The acquired breathing sounds can be analyzed using advanced signal processing and analysis in tandem with new deep/machine learning and pattern recognition techniques to separate the breathing phases, estimate the lung volume, oxygenation, and to further classify the breathing data input into healthy or unhealthy cases. The ideas presented have the potential to be deployed as self-test breathing monitoring apps for the ongoing global COVID-19 pandemic, where users can check their breathing sound pattern frequently through the app.

Entities:  

Keywords:  e-health; home health monitoring; sensor technology; technology; telemedicine

Mesh:

Year:  2020        PMID: 32487005     DOI: 10.1089/tmj.2020.0114

Source DB:  PubMed          Journal:  Telemed J E Health        ISSN: 1530-5627            Impact factor:   3.536


  18 in total

1.  A systematic review on AI/ML approaches against COVID-19 outbreak.

Authors:  Onur Dogan; Sanju Tiwari; M A Jabbar; Shankru Guggari
Journal:  Complex Intell Systems       Date:  2021-07-05

2.  A Generic Deep Learning Based Cough Analysis System From Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels.

Authors:  Javier Andreu-Perez; Humberto Perez-Espinosa; Eva Timonet; Mehrin Kiani; Manuel I Giron-Perez; Alma B Benitez-Trinidad; Delaram Jarchi; Alejandro Rosales-Perez; Nick Gatzoulis; Orion F Reyes-Galaviz; Alejandro Torres-Garcia; Carlos A Reyes-Garcia; Zulfiqar Ali; Francisco Rivas
Journal:  IEEE Trans Serv Comput       Date:  2021-02-23       Impact factor: 11.019

3.  COVID-19 open source data sets: a comprehensive survey.

Authors:  Junaid Shuja; Eisa Alanazi; Waleed Alasmary; Abdulaziz Alashaikh
Journal:  Appl Intell (Dordr)       Date:  2020-09-21       Impact factor: 5.086

4.  Capillary whole-blood IgG-IgM COVID-19 self-test as a serological screening tool for SARS-CoV-2 infection adapted to the general public.

Authors:  Serge Tonen-Wolyec; Raphael Dupont; Salomon Batina-Agasa; Marie-Pierre Hayette; Laurent Bélec
Journal:  PLoS One       Date:  2020-10-15       Impact factor: 3.240

5.  Preparing for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Self-Testing Implementation: Lessons Learned From HIV Self-Testing.

Authors:  Donaldson F Conserve; Allison Mathews; Augustine T Choko; LaRon E Nelson
Journal:  Front Med (Lausanne)       Date:  2020-12-07

6.  End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework.

Authors:  Abdelkader Nasreddine Belkacem; Sofia Ouhbi; Abderrahmane Lakas; Elhadj Benkhelifa; Chao Chen
Journal:  Front Med (Lausanne)       Date:  2021-03-31

7.  Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool.

Authors:  Mohanad Alkhodari; Ahsan H Khandoker
Journal:  PLoS One       Date:  2022-01-13       Impact factor: 3.240

8.  Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System.

Authors:  Uduak Z George; Kee S Moon; Sung Q Lee
Journal:  Sensors (Basel)       Date:  2021-02-17       Impact factor: 3.576

9.  Automatic Detection of COVID-19 Based on Short-Duration Acoustic Smartphone Speech Analysis.

Authors:  Brian Stasak; Zhaocheng Huang; Sabah Razavi; Dale Joachim; Julien Epps
Journal:  J Healthc Inform Res       Date:  2021-03-11

Review 10.  Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic.

Authors:  Nora El-Rashidy; Samir Abdelrazik; Tamer Abuhmed; Eslam Amer; Farman Ali; Jong-Wan Hu; Shaker El-Sappagh
Journal:  Diagnostics (Basel)       Date:  2021-06-24
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