Intracranial Pressure Prediction with a Recurrent Neural Network Model

Guochang Ye, Vignesh Balasubramanian, John K.J. Li, Mehmet Kaya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Abnormal elevation of intracranial pressure (ICP) can cause dangerous or even fatal outcomes. The early detection of high intracranial pressure events can be crucial in saving patients' life in an intensive care unit (ICU). This study proposes an efficient artificial recurrent neural network to predict intracranial pressure evaluation for thirteen patients. The learning model is generated uniquely for each patient to predict the occurrence of the ICP event (classified into high ICP or low ICP) for the upcoming 10 minutes by inputting the previous 20-minutes signal. The results showed that the minimal accuracy of predicting intracranial pressure events was 90% for 11 patients, whereas a minimum of 95% accuracy was obtained among five patients. This study introduces an efficient artificial recurrent neural network model on the early prediction of intracranial pressure evaluation supported by the high adaptive performance of the LSTM model.

Original languageEnglish (US)
Title of host publicationBioSMART 2021 - Proceedings
Subtitle of host publication4th International Conference on Bio-Engineering for Smart Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665408103
DOIs
StatePublished - 2021
Event4th International Conference on Bio-Engineering for Smart Technologies, BioSMART 2021 - Paris, France
Duration: Dec 8 2021Dec 10 2021

Publication series

NameBioSMART 2021 - Proceedings: 4th International Conference on Bio-Engineering for Smart Technologies

Conference

Conference4th International Conference on Bio-Engineering for Smart Technologies, BioSMART 2021
Country/TerritoryFrance
CityParis
Period12/8/2112/10/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Information Systems and Management
  • Biomedical Engineering
  • Media Technology

Keywords

  • Critical care
  • Forecasting
  • Intracranial Hypertension
  • Machine Learning
  • Traumatic Brain Injury

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