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Prediction of Increased Intracranial Pressure in Traumatic Brain Injury Using Quantitative Electroencephalogram in a Porcine Experimental Model
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 porcin...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914917/ https://www.ncbi.nlm.nih.gov/pubmed/36766491 http://dx.doi.org/10.3390/diagnostics13030386 |
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author | 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 |
author_facet | 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 |
author_sort | Kim, Ki-Hong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9914917 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99149172023-02-11 Prediction of Increased Intracranial Pressure in Traumatic Brain Injury Using Quantitative Electroencephalogram in a Porcine Experimental Model 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 Diagnostics (Basel) Article 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. MDPI 2023-01-20 /pmc/articles/PMC9914917/ /pubmed/36766491 http://dx.doi.org/10.3390/diagnostics13030386 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article 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 Prediction of Increased Intracranial Pressure in Traumatic Brain Injury Using Quantitative Electroencephalogram in a Porcine Experimental Model |
title | Prediction of Increased Intracranial Pressure in Traumatic Brain Injury Using Quantitative Electroencephalogram in a Porcine Experimental Model |
title_full | Prediction of Increased Intracranial Pressure in Traumatic Brain Injury Using Quantitative Electroencephalogram in a Porcine Experimental Model |
title_fullStr | Prediction of Increased Intracranial Pressure in Traumatic Brain Injury Using Quantitative Electroencephalogram in a Porcine Experimental Model |
title_full_unstemmed | Prediction of Increased Intracranial Pressure in Traumatic Brain Injury Using Quantitative Electroencephalogram in a Porcine Experimental Model |
title_short | Prediction of Increased Intracranial Pressure in Traumatic Brain Injury Using Quantitative Electroencephalogram in a Porcine Experimental Model |
title_sort | prediction of increased intracranial pressure in traumatic brain injury using quantitative electroencephalogram in a porcine experimental model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914917/ https://www.ncbi.nlm.nih.gov/pubmed/36766491 http://dx.doi.org/10.3390/diagnostics13030386 |
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