Applications of machine learning for nursing monitoring of electroencephalography
Publish Year: 1403
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JNRCP-2-1_001
تاریخ نمایه سازی: 16 بهمن 1402
Abstract:
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.
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Authors
Mohammad Reza Zabihi
Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Kambiz Rohampour
Department of Physiology, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
Samira Rashtiani
Department of Physiology, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
Tara Alizadeh
Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran
Mohammad Akhoondian
Department of Physiology, School of Medicine, Cellular and the Molecular Research Center, Guilan University of Medical Sciences, Rasht, Iran
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