基于心率变异性分析的机器学习方法检测新冠长期症状
Machine Learning-based detection of long COVID using Heart Rate Variability Analysis
浏览论文内容
中文总结 AI 辅助
研究旨在用机器学习通过心率变异性分析检测新冠长期症状，对每位受试者在不同状态下获取心率记录并提取特征，训练多种模型，结果显示梯度提升模型效果最佳，证明了新冠长期症状与心率变异性的关联及机器学习模型的作用。
中文摘要 AI 辅助
新冠疫情肆虐全球后，约20%的感染者在治愈数月后仍有症状，即新冠长期症状。本文介绍了在奥伦塞大学医院开展的一项研究，旨在利用机器学习建立该疾病与心率变异性（HRV）参数变化之间的关系。每位受试者在静息、体力活动和压力状态下获取5份心率记录，每份记录经处理后提取15个HRV指标，为每位患者提供75个特征。从中选取16个特征训练10种不同的机器学习模型。结果表明，最佳模型梯度提升的准确率达85.2%，F1分数为84.9%，受试者工作特征曲线下面积（AUC）为0.907，所有模型的AUC均超过0.833。该研究证明了新冠长期症状与心率变异性之间的关联，凸显了机器学习模型在识别这种关系及辅助诊断方面的作用。
英文摘要
After COVID epidemic has ravaged the world, around 20% of infected subjects continue to manifest symptoms several months after their cure. This disorder is called long COVID. This paper presents a study carried out at the University Hospital of Ourense with the aim of establishing a relationship among the disease and variations in Heart rate variability (HRV) parameters using machine learning (ML). Five heart rate recordings were obtained per subject, both at rest and under conditions of physical effort and stress. Each record was processed and 15 HRV indices were extracted, giving 75 features per patient. Of these features, 16 were selected to train 10 different ML models: Support Vector Classification, Linear Support Vector Classification, Logistic Regression, Linear Discriminant Analysis, Stochastic Gradient Descent, Multiple Layer Perceptron, Naive Bayes, Random Forest, and Gradient and ADA Boost Classifiers. Results show that the best model, Gradient Boost, achieves an accuracy of 85.2%, F1-score of 84.9%, and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.907, and that all models exceed 0.833 AUC. This study demonstrates an association between long COVID and heart rate variability (HRV), highlighting the utility of machine learning models in identifying this relationship and supporting its diagnose.
