Abstract
Objective: Controlling the spread of the COVID-19 pandemic largely depends on scaling up the testing infrastructure for identifying infected individuals. Consumer-grade wearables may present a solution to detect the presence of infections in the population, but the current paradigm requires collecting physiological data continuously and for long periods of time on each individual, which poses limitations in the context of rapid screening. Technology: Here, we propose a novel paradigm based on recording the physiological responses elicited by a short (~2 minutes) sequence of activities (i.e. “snapshot”), to detect symptoms associated with COVID-19. We employed a novel body-conforming soft wearable sensor placed on the suprasternal notch to capture data on physical activity, cardio-respiratory function, and cough sounds. Results: We performed a pilot study in a cohort of individuals (n=14) who tested positive for COVID-19 and detected altered heart rate, respiration rate and heart rate variability, relative to a group of healthy individuals (n=14) with no known exposure. Logistic regression classifiers were trained on individual and combined sets of physiological features (heartbeat and respiration dynamics, walking cadence, and cough frequency spectrum) at discriminating COVID-positive participants from the healthy group. Combining features yielded an AUC of 0.94 (95% CI=[0.92, 0.96]) using a leave-one-subject-out cross validation scheme. Conclusions and Clinical Impact: These results, although preliminary, suggest that a sensor-based snapshot paradigm may be a promising approach for non-invasive and repeatable testing to alert individuals that need further screening.
Highlights
Ongoing efforts are being directed at the development of THE COVID-19 pandemic is a global public health cri- novel rapid screening technologies [5], [6], but at present sis, with over 50 million confirmed cases and more the primary method to test an individual for the presence than 1.2 million deaths worldwide as of November 11th 2020. of the virus is based on molecular testing, known as Testing has continued to be a critical factor to control and RT-PCR, reduce the spread of the disease by timely isolating and/or which detects the virus genetic material in a biological sample treating individuals who are suspected of infection [1]
A. ‘‘SNAPSHOT’’ DETECTION OF COVID-19 To address limitations in continuous physiological monitoring with wearable devices, we propose an alternative solution to detecting changes in physiology related to COVID-19 infections
By collecting a range of physiological signals during a snapshot, including heart activity, respiration, physical activity, and cough sounds, we hypothesized that changes due to COVID-19 could be detected
Summary
Ongoing efforts are being directed at the development of THE COVID-19 pandemic is a global public health cri- novel rapid screening technologies [5], [6], but at present sis, with over 50 million confirmed cases and more the primary method to test an individual for the presence than 1.2 million deaths worldwide as of November 11th 2020. of the virus is based on molecular testing, known as Testing has continued to be a critical factor to control and RT-PCR (reverse transcription polymerase chain reaction), reduce the spread of the disease by timely isolating and/or which detects the virus genetic material in a biological sample treating individuals who are suspected of infection [1]. With from the patient respiratory tract or saliva [7]. This a proportion of asymptomatic infections estimated between is considered the most sensitive type of test, it has several. 20% to 30% [2], [3], rapid testing for pre-symptomatic or drawbacks: for many testing facilities, test samples must be asymptomatic patients could be key to ending the spread of transported to a lab for analysis, creating a delay period into COVID-19 [4]. The current testing capacity, as well as delays in processing and delivering test results, remain a bottleneck that is limiting the effectiveness of public health containment measures [8]
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