Applications of machine learning for nursing monitoring of electroencephalography

The nursing monitoring of electroencephalography (EEG) during neurosurgery includes verifying the proper placement of electrodes on the patient's scalp and ensuring the accurate display of EEG readings on the monitoring apparatus. This study aims to examine the use of machine learning (ML) in EEG monitoring by analyzing the R programming language. The results will provide insights into surgical nursing care by evaluating EEG patterns. The preceding evidence was collected following the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines in the present study. The logical analysis of the data was conducted using the R programming language. ML algorithms based on usage rate included logistic regression (LR), support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and convolutional neural network (CNN). Also, the use of ML in nursing monitoring of EEG is categorized into three indications rehabilitation measurement (post-operation), delayed cerebral ischemia (DCI) detection (pre-operation), hypotension identification (intra-operation), surgical outcomes measurement(post-operation), and seizure prediction. In sum, the algorithm, including LR and SVM, have been frequently utilized in the realm of EEG evaluation, as indicated by the results obtained.