A Framework for Biomarkers of COVID-19 Based on Coordination of Speech-Production Subsystems

Quatieri, Thomas F.; Talkar, Tanya; Palmer, Jeffrey S.

doi:10.1109/ojemb.2020.2998051

Cited by 91 publications

(84 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [14] speech recordings from COVID-19 patients are analyzed to categorize automatically the health state of patients from four aspects, namely severity of illness, sleep quality, fatigue, and anxiety. Quatieri et al [26] showed that changes in vocal patterns could be a potential biomarker for COVID-19.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Brown

Chauhan

Grammenos

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

368

450

View full text Add to dashboard Cite

Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to understand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis. CCS CONCEPTS • Information systems → Data mining; • Human-centered computing → User studies; Ubiquitous and mobile computing; • Computing methodologies → Machine learning. * Ordered alphabetically, equal contribution.

show abstract

Section: Motivation and Related Workmentioning

confidence: 99%

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Brown

Chauhan

Grammenos

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

368

450

View full text Add to dashboard Cite

show abstract

“…As the National Institute of Health is aiming at the 6 million PCR test per day, such aggressive research plan is promising to suppress this pandemic (Tromberg et al, 2020). Recently, innovative technique such as vocal biomarkars is proposed by MIT Lincoln Laboratory to identify the asymptomatic people with COVID-19 positive (Quatieri et al, 2020). Vocal change before and after infection could be detected by processing speech recording.…”

Section: Discussion and Summarymentioning

confidence: 99%

Suppression of COVID-19 infection by isolation time control based on the SIR model and an analogy from nuclear fusion research

Mitarai

Yanagi

2020

Preprint

View full text Add to dashboard Cite

The coronavirus disease 2019 (COVID-19) has been damaging our daily life after declaration of pandemic. Therefore, we have started studying on the characteristics of Susceptible-Infectious-Recovered (SIR) model to know about the truth of infectious disease and our future. 　After detailed studies on the characteristics of the SIR model for the various parameter dependencies with respect to such as the outing restriction (lockdown) ratio and vaccination rate, we have finally noticed that the second term (isolation term) in the differential equation of the number of the infected is quite similar to the "helium ash particle loss term" in deuterium-tritium (D-T) nuclear fusion. Based on this analogy, we have found that isolation of the infected is not actively controlled in the SIR model. Then we introduce the isolation control time parameter q and have studied its effect on this pandemic. Required isolation time to terminate the COVID-19 can be estimated by this proposed method. 　To show this isolation control effect, we choose Tokyo for the model calculation because of high population density. We determine the reproduction number and the isolation ratio in the initial uncontrolled phase, and then the future number of the infected is estimated under various conditions. If the confirmed case can be isolated in 3~8 days by widely performed testing, this pandemic could be suppressed without awaiting vaccination. If the mild outing restriction and vaccination are taken together, the isolation control time can be longer. We consider this isolation time control might be the only solution to overcome the pandemic when vaccine is not available.

show abstract

“…A speech modeling and signal-processing framework have been proposed by Quatieri et al from MIT to detect asymptomatic and symptomatic states of COVID-19 [ 64 ]. This framework based on complex coordination pattern among neuromotor of speech subsystems indulged in articulation, respiration, and phonation which is motivated by the specific feature of COVID-19 which involves lower (bronchial, diaphragm, lower tracheal) concerning the upper (laryngeal, pharyngeal, oral and nasal) respiratory tract inflammation, also by the increased prove of the virus' neurological display.…”

Section: Digital Methods Of Sars-cov-2 Detection Mostly In Asymptomatmentioning

confidence: 99%

Recent trends in analytical and digital techniques for the detection of the SARS-Cov-2

Mukhopadhyay

Sharma

Sinha

et al. 2021

Biophysical Chemistry

View full text Add to dashboard Cite

The current global outbreak of COVID-19 due to SARS-CoV-2 is an unprecedented humanitarian crisis. Considering the gravity of its impact there is an immediate need to develop a detection technique that is sensitive, specific, fast, and affordable for the clinical diagnosis of the disease. Real time Polymerase Chain Reaction (RT-PCR)-based detection platforms are contemplated to be the gold standard to detect viral RNA. However, that may be susceptible to errors, and there is a risk of obtaining false results, which ultimately compromises the strategy of efficient disease management. Several modern techniques exhibiting assured results with enhanced sensitivity and specificity against the SARS-CoV-2 associated viral components or immune response against it have been developed and may be implemented. The review deals with the conventional RT-PCR detection techniques and compares them to other detection platforms viz., biosensor based detection of antigens, fluorescent or colorimetric detection systems including CRISPR-Cas 13 based SHERLOCK kit, CRISPR Cas-9 based FELUDA test kit, CRISPR DETECTR kit, Next Generation Sequencing or microarray-based kits. These modern techniques are great as a point of care detection methods but should be followed by RT PCR based detection for the confirmation of COVID-19 status.

show abstract

A Framework for Biomarkers of COVID-19 Based on Coordination of Speech-Production Subsystems

Cited by 91 publications

References 16 publications

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Suppression of COVID-19 infection by isolation time control based on the SIR model and an analogy from nuclear fusion research

Recent trends in analytical and digital techniques for the detection of the SARS-Cov-2

Contact Info

Product

Resources

About