COVID-19 Sounds App

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The power of audio in diagnosis

May 18, 2020 — Audio signals generated by the human body have often been used by clinicians and clinical researchers as diagnostic or progression indicators for diseases and disease onset. The human body’s catalogue of sounds includes sighs, breathing, the heart, digestive system and vibration sounds, as well as voice.

Until recently, such signals were usually collected with manual stethoscopes during scheduled medical appointments. Research has now started to use digital technology to gather bodily sounds, and run automatic analysis on the data. For example, digital stethoscopes developed by Littmann and Thinklab have aided detection of asthma and wheezing.

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Researchers have also been piloting the use of the human voice as a proxy for early diagnosis of a variety of illnesses. Parkinson’s disease correlates to softness of speech, resulting from lack of coordination over the vocal muscles^{1, 1b}. Coronary artery disease can be indicated by voice frequency, as hardening of the arteries may affect voice production ². Invisible injuries like post-traumatic stress disorder³, traumatic brain injury and psychiatric disorders^{4, 4b} correlate to changes in the tone, pitch, rhythm, rate and volume of the human voice.

The use of human-generated audio as a biomarker for various illnesses offers enormous potential for early diagnosis. The technology could offer an inexpensive solution with the potential to be rolled out on a mass scale, if it could be embedded in commodity devices. This is even more true if such solutions could monitor individuals throughout their daily lives in an unobtrusive way.

Why have we initiated data collection of audio sounds for COVID-19?

Respiratory sounds are already known to have diagnostic properties⁵. Moreover, there is some initial evidence that respiratory sounds, especially coughing, could be indicative of COVID-19 ⁶.

However there is insufficient data to determine whether this is generally true, and to date there is no research investigating whether breathing patterns, voice features and coughs could help the diagnosis of COVID-19. While the single audio features might not serve as robust diagnostics alone, we want to see whether they could be powerful taken all together, and add information to the self-declared symptoms of the individual.

We have therefore initiated a data collection which collects the sounds of voice, breathing and coughing from participants, via our COVID-19 Sounds App.

What do we hope to achieve?

A large sounds dataset from users across different demographics and with different medical histories, containing thousands of samples crowdsourced in the “wild”, is unprecedented.

We hope to develop models which use the audio inputs to see whether COVID-19 could be detected primarily through audio. We believe the most promising avenue of this is the detection of disease progression. Having multiple samples of individuals at different stages might allow researchers to build a better picture of COVID-19’s progression in our respiratory organs.

Secondly, this effort will offer the community a dataset for research into COVID-19 and, as our dataset grows, perhaps it will also be of use in research into other respiratory diseases.

[1] R. Vikas and R. Sharma. Early detection of Parkinson's disease through Voice. 2014 International Conference on Advances in Engineering and Technology (ICAET), Nagapattinam, 2014, pp. 1-5.

[1b] Brabenec L, Mekyska J, Galaz Z, Rektorova I. Speech disorders in Parkinson's disease: early diagnostics and effects of medication and brain stimulation. J Neural Transm (Vienna). 2017 Mar;124(3):303- 334. doi: 10.1007/s00702-017-1676-0. Epub 2017 Jan 18.

[2] E. Maor, J. Sara, D. Orbelo, L. Lerman, Y. Levanon, A. Lerman . Voice Signal Characteristics Are Independently Associated With Coronary Artery Disease. Mayo Clin Proc. 2018 Jul;93(7):840-847. doi: 10.1016/j.mayocp.2017.12.025. Epub 2018 Apr 12.

[3] D. Banerjee, I. Debrup, A. Kazi, G. Mei, L. Xiao, G. Zhang, R. Xu, S. Ji, J. Li. (2017). A Deep Transfer Learning Approach for Improved Post-Traumatic Stress Disorder Diagnosis. In Proceedings of Int. Conference on Data Mining (ICDM’17).

[4] D. Vergyri, B. Knoth, E. Shriberg, V. Mitra, M. McLaren, L. Ferrer, P. Garcia and C. Marmar. Speech-based assessment of PTSD in a military population using diverse feature classes. Interspeech, 3729- 3733, 2015.

[4b] M. Faurholt-Jepsen, J. Busk, M. Frost, M Vinberg, E. Christensen, O. Winther, J. Bardram, L. Kessing. Voice analysis as an objective state marker in bipolar disorder. Translational Psychiatry. volume 6, page e856 (2016).

[5] Pramono RXA, Bowyer S, Rodriguez-Villegas E (2017) Automatic adventitious respiratory sound analysis: A systematic review. PLOS ONE 12(5): e0177926.
https://doi.org/10.1371/journal.pone.0177926

[6] A. Imran, I. Posokhova, H. Qureshi, U. Masood, S. Riaz, K. Ali, C. John, M. Nabeel, I. Hussain. AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App.
https://arxiv.org/abs/2004.01275.

[7] B. Schuller, D. Schuller, K. Qian, J. Liu, H. Zheng, X. Li. COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis.
https://arxiv.org/pdf/2003.11117.pdf