Department of Information Engineering, University of Florence, Florence, Italy.
UOs Anesthesiology and Reanimation Unit, San Giuseppe Hospital, Empoli, Italy.
Sci Rep. 2023 Jan 31;13(1):1713. doi: 10.1038/s41598-023-28681-2.
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.
COVID-19 已知是一种引起微血管疾病的原因，可归因于细胞因子风暴炎症反应和随之而来的血液凝固。在这项研究中，我们提出了一种基于随机森林 (RF) 和支持向量机 (SVM) 分类器评估 COVID-19 存在和严重程度的方法。分类器应用于从健康和 COVID-19 受影响的受试者的光体积描记 (PPG) 信号中提取的心率变异性 (HRV) 参数。监督分类器在 50 名健康受试者和 93 名 COVID-19 受影响受试者的 PPG 信号获得的 HRV 参数上进行训练和测试，这些受试者根据支持氧气治疗和/或通气分为轻度和中度两组。使用最小绝对收缩和选择算子 (LASSO) 技术确定每组比较的最具信息量的特征集。RF 和 SVM 分类器在组间比较中均表现出高准确率。特别是，RF 分类器在健康组和轻症 COVID-19 组之间的比较中达到了 94%的准确率。获得的结果表明 RF 和 SVM 具有很强的能力，可以区分健康受试者和 COVID-19 患者，并区分两种不同的 COVID-19 严重程度。该方法可能有助于以低成本和快速的方式检测 COVID-19 疾病的存在和严重程度；此外，这些原因使其成为未来研究的一个有趣起点，这些研究旨在研究其作为可能的筛查方法的有效性。
