Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning
Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning
复制标题
DOI：
10.1088/1361-6579/aaab07
复制
发表时间：
2018-03-01
影响因子：
3.2
通讯作者：
Lee, Boreom
中科院分区：
文献类型：
--
作者：
Oh, Jooyoung;Cho, Dongrae;Lee, Boreom
目的：谵妄是重症监护病房（ICU）患者中常见的一种重要综合征，但在治疗过程中通常未被充分认识。本研究旨在探讨是否可以通过心率变异性 (HRV) 和机器学习成功区分神志不清的患者与非神志不清的患者。方法：在日常ICU护理过程中获取140例患者的心电图数据，并对HRV数据进行分析。训练有素的精神科医生每天都会评估谵妄，包括其类型、严重程度和病因。利用HRV数据和各种机器学习算法，包括线性支持向量机（SVM）、具有径向基函数（RBF）核的SVM、线性极限学习机（ELM）、具有RBF核的ELM、线性判别分析和二次判别分析来区分谵妄患者和非谵妄患者。主要结果：纳入了 4797 份心电图的 HRV 数据，其中 39 名患者在 ICU 住院期间至少出现过一次谵妄。使用带有 RBF 核的 SVM 获得了最大分类精度。我们基于 HRV 和机器学习的预测方法与之前使用大量临床信息的谵妄预测模型相当。意义：我们的结果表明，自主神经改变可能是 ICU 谵妄患者的一个显着特征，这表明基于 HRV 和机器学习的谵妄自动预测和早期检测的潜力。
Objective: Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Approach: Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. Main results: HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Significance: Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.