Discovering ways to non-invasively early detect COVID-19 from heart rate, sleep, or a cough sound, even among the asymptomatic

Heart rate, sleep quality, daily movement–cough sound frequency? Several studies in the US and UK are attempting to turn up ways to early diagnose mildly symptomatic, asymptomatic, or even pre-symptomatic COVID-19 cases, without the PCR swab or a blood test.

The more obvious of the two comes out of the Scripps Research Translational Institute. The DETECT study started in March (!) with 30,500 participants sending in data in the first six weeks of the study on heart rate, sleep quality, and daily movement. This information was then matched with self-reported symptoms and diagnostic tests taken if any. In this way, new infections and outbreaks could be detected at an earlier stage.  The study is attempting to confirm if changes in those metrics in an individual’s pattern can identify those even at a pre-symptomatic or asymptomatic stage. 3,811 reported symptoms, 54 reported testing positive, and 279 negative for COVID-19. The numbers seem small, but the analysis carries out that the combination of sensor and symptom data performed better in discriminating between positive and negative individuals than symptom reporting alone. The symptom data were taken from Fitbits and any device connected through Apple HealthKit or Google Fit data aggregators, then reported on the research app MyDataHelps. FierceBiotech, Nature Medicine (study)

Also using vital signs, back in August, Fitbit released early data on a 100,000+ study where changes in heart rate and breathing could detect about half of diagnosed cases at least one day to a week before diagnosis. Symptomatic cases were 1,100 in this sample. Heart rate and breathing were detected to become more frequent in the symptomatic, with the variability in time between each heartbeat dropping, resulting in a more steady pulse. The preferred tracking was at night during rest. However, there was a 30 percent false positive rate on the algorithm used, which is extremely high. FierceBiotech Related to this work, Fitbit was selected at the end of October by the US Army Medical Research and Development Command (USAMRDC) to receive nearly $2.5 million from the US Department of Defense through a Medical Technology Enterprise Consortium (MTEC) award to advance a wearable diagnostic capability for the early detection of a COVID-19 infection. Fitbit will be working with Northwell Health’s Feinstein Institutes for Medical Research to validate their early detection algorithm. Business Wire

And what about that ‘Covid Cough’? MIT is researching that this cough is different than other coughs, like from cold or allergy. Their research found that there’s a difference in the sound of an asymptomatic individual’s cough–and that sound frequency difference could not be heard by human ears. (Dog ears perhaps?) MIT researchers created “the largest audio COVID-19 cough balanced dataset reported to date with 5,320 subjects” out of 70,000 cough samples. The algorithm performed well. “When validated with subjects diagnosed using an official test, the model achieves COVID-19 sensitivity of 98.5% with a specificity of 94.2% (AUC: 0.97). For asymptomatic subjects it achieves sensitivity of 100% with a specificity of 83.2%.” This sure sounds like an AI screening tool that is inexpensive and convenient to use with multiple populations even daily. IEEE-EMB  BBC News reports that similar studies are taking place at Cambridge University, Carnegie Mellon University, and UK health start-up Novoic. The Cambridge study used a combination of breath and cough sounds and had an 80 percent success rate in identifying positive coronavirus cases from their base of 30,000 recordings.

All of these will be useful, but still need to be validated–and that takes time, for which this Editor thinks is short as this virus, like others, will eventually 1) mutate out or 2) be effectively treated as we do with normal flus. But down the road, these will serve as a template for new ways for early screening or even diagnosis of other respiratory diseases.

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