Kim Ki-Hong, Kim Heejin, Song Kyoung-Jun, Shin Sang-Do, Kim Hee-Chan, Lim Hyouk-Jae, Kim Yoonjic, Kang Hyun-Jeong, Hong Ki-Jeong
Department of Emergency Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea.
Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul 03080, Republic of Korea.
Diagnostics (Basel). 2023 Jan 20;13(3):386. doi: 10.3390/diagnostics13030386.
Continuous and non-invasive measurement of intracranial pressure (ICP) in traumatic brain injury (TBI) is important to recognize increased ICP (IICP), which can reduce treatment delays. The purpose of this study was to develop an electroencephalogram (EEG)-based prediction model for IICP in a porcine TBI model. Thirty swine were anaesthetized and underwent IICP by inflating a Foley catheter in the intracranial space. Single-channel EEG data were collected every 6 min in 10 mmHg increments in the ICP from baseline to 50 mmHg. We developed EEG-based models to predict the IICP (equal or over 25 mmHg) using four algorithms: logistic regression (LR), naive Bayes (NB), support vector machine (SVM), and random forest (RF). We assessed the performance of each model based on the accuracy, sensitivity, specificity, and AUC values. The accuracy of each prediction model for IICP was 0.773 for SVM, 0.749 for NB, 0.746 for RF, and 0.706 for LR. The AUC of each model was 0.860 for SVM, 0.824 for NB, 0.802 for RF, and 0.748 for LR. We developed a machine learning prediction model for IICP using single-channel EEG signals in a swine TBI experimental model. The SVM model showed good predictive power with the highest AUC value.
在创伤性脑损伤（TBI）中持续无创测量颅内压（ICP）对于识别颅内压升高（IICP）很重要，这可以减少治疗延迟。本研究的目的是在猪TBI模型中开发一种基于脑电图（EEG）的颅内压预测模型。30头猪被麻醉，并通过在颅内空间插入Foley导管来测量颅内压。从基线到50 mmHg，以10 mmHg的增量每6分钟收集一次单通道EEG数据。我们使用四种算法开发了基于EEG的模型来预测颅内压（等于或超过25 mmHg）：逻辑回归（LR）、朴素贝叶斯（NB）、支持向量机（SVM）和随机森林（RF）。我们根据准确性、敏感性、特异性和AUC值评估每个模型的性能。SVM对颅内压的每个预测模型的准确性为0.773，NB为0.749，RF为0.746，LR为0.706。每个模型的AUC值，SVM为0.860，NB为0.824，RF为0.802，LR为0.748。我们在猪TBI实验模型中使用单通道EEG信号开发了一种用于颅内压的机器学习预测模型。SVM模型显示出良好的预测能力，AUC值最高。
