CovidDeep: Testing for SARS-CoV-2/COVID-19 using Wearable Medical Sensors

CovidDeep

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This article was first published on AZASensors on March 21, 2022
Interview conducted by Joan Nugent
AZoSensors speaks with Niraj K. Jha from the Electrical and Computer Engineering faculty at Princeton University. This interview explores the research that proposes a framework called CovidDeep. CovidDeep combines efficient deep neural networks with commercially available wearable medical sensors for pervasive testing of the virus and the resultant disease.

Can you give us an insight into your career background and your role in the research?

I received my B.Tech. from the Indian Institute of Technology, Kharagpur, India in 1981 and Ph.D. from the University of Illinois at Urbana-Champaign in 1985. I have been part of the Electrical and Computer Engineering faculty at Princeton University since 1987. My research interests span machine learning, smart healthcare, and cybersecurity. I conceived the CovidDeep concept and supervised the development of the neural network model.

Schematic diagram of the CovidDeep framework

Schematic diagram of the CovidDeep framework (GSR: Galvanic skin response, Ox.: oxygen saturation, BP: blood pressure, DT/RF: decision tree/random forest, NN: neural network, KB: knowledge-base, MND: multi-variate Normal distribution, GMM: Gaussian mixture model, KDE: kernel density estimation).

Can you give us an overview of the research and how CovidDeep was developed?

I conceived the idea for CovidDeep in early March 2020. CovidDeep is based on data collection using sensors (e.g., galvanic skin response, inter-beat interval, and skin temperature) embedded in a smartwatch, two discrete sensors (pulse oximetry, blood pressure), and a questionnaire (with yes/no answers for 11 questions).

The data was collected from healthy, asymptomatic, and symptomatic individuals from a hospital in Northern Italy. Physicians labeled them at that hospital to enable supervised machine learning. Then neural networks were trained using the data based on our grow-and-prune neural network synthesis procedure.

The grow-and-prune approach boosts the accuracy of the neural network significantly relative to traditional neural networks, while at the same time also significantly reducing the size of the neural network and improving its energy efficiency. The approach mimics how human brains grow from a baby to toddler to adult brain. The best neural network had 98% diagnostic accuracy for the SARS-CoV-2 virus and the resultant COVID-19 disease. A field trial with this neural network in France also yielded a high diagnostic accuracy of 97%. CovidDeep is currently awaiting FDA approval.

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