Why the study?
COVID-19 causes microvascular disease through inflammatory response and blood coagulation, prompting the evaluation of machine learning applied to HRV parameters to assess disease presence and severity.
Can machine learning classifiers applied to HRV parameters from PPG signals accurately detect the presence and severity of COVID-19?
Can machine learning classifiers applied to HRV parameters from PPG signals accurately detect the presence and severity of COVID-19?
Machine learning classifiers using HRV parameters extracted from simple, non-invasive PPG signals can accurately distinguish between healthy individuals and COVID-19 patients of varying severity.
May support non-invasive COVID-19 screening via PPG-HRV; leaves open prospective validation before clinical adoption.
COVID-19 is known to be a cause of microvascular disease imputable to, for instance, the cytokine storm inflammatory response and the consequent blood coagulation. In this study, we propose a methodological approach for assessing the COVID-19 presence and severity based on Random Forest (RF) and Support Vector Machine (SVM) classifiers. Classifiers were applied to Heart Rate Variability (HRV) parameters extracted from photoplethysmographic (PPG) signals collected from healthy and COVID-19 affected subjects. The supervised classifiers were trained and tested on HRV parameters obtained from the PPG signals in a cohort of 50 healthy subjects and 93 COVID-19 affected subjects, divided into two groups, mild and moderate, based on the support of oxygen therapy and/or ventilation. The most informative feature set for every group's comparison was determined with the Least Absolute Shrinkage and Selection Operator (LASSO) technique. Both RF and SVM classifiers showed a high accuracy percentage during groups' comparisons. In particular, the RF classifier reached 94% of accuracy during the comparison between the healthy and minor severity COVID-19 group. Obtained results showed a strong capability of RF and SVM to discriminate between healthy subjects and COVID-19 patients and to differentiate the two different COVID-19 severity. The proposed method might be helpful for detecting, in a low-cost and fast fashion, the presence and severity of COVID-19 disease; moreover, these reasons make this method interesting as a starting point for future studies that aim to investigate its effectiveness as a possible screening method.
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Aliani et al. (2023) studied this question.
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