Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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
_version_ 1784885779566166016
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
work_keys_str_mv AT kimkihong predictionofincreasedintracranialpressureintraumaticbraininjuryusingquantitativeelectroencephalograminaporcineexperimentalmodel
AT kimheejin predictionofincreasedintracranialpressureintraumaticbraininjuryusingquantitativeelectroencephalograminaporcineexperimentalmodel
AT songkyoungjun predictionofincreasedintracranialpressureintraumaticbraininjuryusingquantitativeelectroencephalograminaporcineexperimentalmodel
AT shinsangdo predictionofincreasedintracranialpressureintraumaticbraininjuryusingquantitativeelectroencephalograminaporcineexperimentalmodel
AT kimheechan predictionofincreasedintracranialpressureintraumaticbraininjuryusingquantitativeelectroencephalograminaporcineexperimentalmodel
AT limhyoukjae predictionofincreasedintracranialpressureintraumaticbraininjuryusingquantitativeelectroencephalograminaporcineexperimentalmodel
AT kimyoonjic predictionofincreasedintracranialpressureintraumaticbraininjuryusingquantitativeelectroencephalograminaporcineexperimentalmodel
AT kanghyunjeong predictionofincreasedintracranialpressureintraumaticbraininjuryusingquantitativeelectroencephalograminaporcineexperimentalmodel
AT hongkijeong predictionofincreasedintracranialpressureintraumaticbraininjuryusingquantitativeelectroencephalograminaporcineexperimentalmodel