An AI framework analyzing heart rate variability and textual logs from wearables predicted COVID-19 infection two days before symptom onset with an accuracy of 83.34 ± 1.68%.
Can an AI framework using HRV measurements from wearables and user textual logs predict COVID-19 infection before symptom onset?
An AI framework analyzing HRV from wearables and user textual logs can predict COVID-19 infection up to two days before symptom onset with high accuracy.
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).
Hijazi et al. (Fri,) conducted a other in COVID-19 (n=186). AI framework using HRV and textual logs from wearables and smartphones was evaluated on Predicting infection two days before the onset of symptoms. An AI framework analyzing heart rate variability and textual logs from wearables predicted COVID-19 infection two days before symptom onset with an accuracy of 83.34 ± 1.68%.