Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients

Abstract

Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index (BI), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily BI was linearly associated with respiratory tract viral load (p < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 in predicting clinical worsening events (sensitivity 94.1% and specificity 88.9%) and prolonged hospitalization (sensitivity 66.7% and specificity 72.7%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.

Use of a Deep Learning Approach for the Sensitive Prediction of Hepatitis B Surface Antigen Levels in Inactive Carrier Patients

“…In terms of the prediction of viral spread using AI, there are several leading reports involving COVID-19. The prediction of coronavirus infections using DNN has been reported [31], and DNN is likely to be the best model for predicting trends in viral diseases that continually show diversity and are influenced by multiple factors. There are several advantages of using AI to fight against pathogens such as viruses and bacteria that cause a complex reaction in the host [32].…”

Machine Learning for Healthcare Wearable Devices: The Big Picture

“…To capture features of the electrical activity of the brain and the health of muscles and the nerve cells, electroencephalogram (EEG) and electromyography (EMG) sensors are used [ 13 – 19 ]. Blood volume pulse (BVP) can be captured using an optical photoplethysmography (PPG) sensor to estimate heart rate and heart rate variation as in [ 1 , 20 – 22 ]. PPG sensor [ 23 ] is also used to give an approximation for the oxygen saturation in blood ( SpO 2 ) as in [ 22 , 24 ].…”

“…Blood volume pulse (BVP) can be captured using an optical photoplethysmography (PPG) sensor to estimate heart rate and heart rate variation as in [ 1 , 20 – 22 ]. PPG sensor [ 23 ] is also used to give an approximation for the oxygen saturation in blood ( SpO 2 ) as in [ 22 , 24 ]. Accelerometers, gyroscopes, and magnetometers are often used in a wide variety of applications to capture or recognize body movement and activities, which can tell a lot about the health and the lifestyle of the person [ 1 , 25 – 39 ].…”

Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder

“…Un et al . [22] proposed a machine learning-derived index reflecting overall health status of the patients with mild COVID-19, using the data captured from wearable biosensors. Hirten and colleagues [23] performed an evaluation of heart rate variablity (HRV) collected by a wearable device to identify and predict COVID-19 and its related symptoms.…”