Novel Coronavirus Cough Database: NoCoCoDa

Abstract

The current pandemic associated with the novel coronavirus (COVID-19) presents a new area of research with its own set of challenges. Creating unobtrusive remote monitoring tools for medical professionals that may aid in diagnosis, monitoring and contact tracing could lead to more efficient and accurate treatments, especially in this time of physical distancing. Audio based sensing methods can address this by measuring the frequency, severity and characteristics of the COVID-19 cough. However, the feasibility of accumulating coughs directly from patients is low in the short term. This paper introduces a novel database (NoCoCoDa), which contains COVID-19 cough events obtained through public media interviews with COVID-19 patients, as an interim solution. After manual segmentation of the interviews, a total of 73 individual cough events were extracted and cough phase annotation was performed. Furthermore, the COVID-19 cough is typically dry but can present as a more productive cough in severe cases. Therefore, an investigation of cough sub-type (productive vs. dry) of the NoCoCoDa was performed using methods previously published by our research group. Most of the NoCoCoDa cough events were recorded either during or after a severe period of the disease, which is supported by the fact that 77% of the COVID-19 coughs were classified as productive based on our previous work. The NoCoCoDa is designed to be used for rapid exploration and algorithm development, which can then be applied to more extensive datasets and potentially real time applications. The NoCoCoDa is available for free to the research community upon request. INDEX TERMS Acoustic signal processing, audio databases, audio systems, biomedical measurement, biomedical monitoring, data analysis, data collection, medical conditions, medical diagnosis, patient monitoring, smart homes.

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

“…The data can be collected using specific sensors for each input. For example, collecting cough sound requires a simple microphone for recording the sound via smartphone app or a web browser ( 49 , 50 ). However, it is still a challenge to record big data for each respiratory illness.…”

Vision-Based Human Detection Techniques: A Descriptive Review

“…A novel coronavirus (COVID-19) pandemic [1], [2] affecting the respiratory portion of the human system is currently ongoing [3] causing a high degree of mortality and morbidity globally [4]. More than 2.5 million people lost their lives and 113 million people are infected by this virus across the globe [5] as of February 2021.…”

A Fully-Automated Method to Evaluate Coronavirus Disease Progression with COVID-19 Cough Sounds Using Minimal Phase Information

“…In Ref. 6 , a COVID-19 cough type detection method is proposed based on frequency-domain features including power ratio between phases 1 & 2 and number of spectral peaks in the energy spectrum using NoCoCoDa database. 6 It is found in Ref.…”

“… 6 , a COVID-19 cough type detection method is proposed based on frequency-domain features including power ratio between phases 1 & 2 and number of spectral peaks in the energy spectrum using NoCoCoDa database. 6 It is found in Ref. 6 that 77% of the recorded COVID-19 coughs are detected as more wet (productive) in nature, whereas the rest of the COVID-19 coughs are detected as more dry (non-productive) in nature.…”

“… 6 It is found in Ref. 6 that 77% of the recorded COVID-19 coughs are detected as more wet (productive) in nature, whereas the rest of the COVID-19 coughs are detected as more dry (non-productive) in nature. The work in Ref.…”