Abstract
Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users’ daily status (i.e., textual logs in a mobile application). Both classification decisions are integrated to generate the final decision as either “potentially COVID-19 infected” or “no evident signs of infection”. We used a publicly available dataset, which comprises 186 patients with more than 3200 HRV readings and numerous user textual logs. The first evaluation of the approach showed an accuracy of 83.34 ± 1.68% with 0.91, 0.88, 0.89 precision, recall, and F1-Score, respectively, in predicting the infection two days before the onset of the symptoms supported by a model interpretation using the local interpretable model-agnostic explanations (LIME).
Highlights
SARS-COV-2 (COVID-19) was first reported in Wuhan, China, at the end of 2019 [1]and spread across China and many countries globally in a few months, leading to a continuous pandemic throughout the world
This paper investigated a part of the whole dataset, namely the heart rate variability (HRV) measurements and the
This paper aimed at investigating the viability of using physiological signals such as HRV acquired from wearable devices over time and other symptoms classification to detect COVID-19 infection onset at least two days before the onset of tough symptoms
Summary
Spread across China and many countries globally in a few months, leading to a continuous pandemic throughout the world. As of March 2020, the World Health Organization (WHO) [2] has declared that this virus is a global epidemic and is spreading exponentially, as the number of infected people up to the date of preparing this research paper has exceeded 260 million cases. The ability to quickly identify, monitor, and isolate COVID-19 patients is one of the most significant challenges that persist even after nearly two years following the first announcement of the virus. The early detection of COVID-19 is predominant to minimize the widespread of the infection, for asymptotic patients, and take responsible isolation measures. Due to the limited capacity of laboratories, test kits, and health care units, and this test’s cost, early detection techniques of this disease became necessary. Since December 2019, numerous artificial intelligence (AI) techniques have already been proposed and developed to detect and classify this virus’s inflammatory signs, mainly using computed tomography (CT) and X-ray images [5,6,7], and recently using physiological signals such as in [8,9,10]
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