University of Oulu

Towards developing a reliable medical device for automated epileptic seizure detection in the ICU

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Author: Afsari, Sanaz1
Organizations: 1University of Oulu, Faculty of Information Technology and Electrical Engineering, Department of Computer Science and Engineering, Computer Science
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.3 MB)
Pages: 66
Persistent link:
Language: English
Published: Oulu : S. Afsari, 2023
Publish Date: 2023-06-16
Thesis type: Master's thesis (tech)
Tutor: Kortelainen, Jukka
Seppänen, Tapio
Reviewer: Kortelainen, Jukka
Seppänen, Tapio


Epilepsy is a prevalent neurological disorder that affects millions of people globally, and its diagnosis typically involves laborious manual inspection of electroencephalography (EEG) data. Automated detection of epileptic seizures in EEG signals could potentially improve diagnostic accuracy and reduce diagnosis time, but there should be special attention to the number of false alarms to reduce unnecessary treatments and costs. This research presents a study on the use of machine learning techniques for EEG seizure detection with the aim of investigating the effectiveness of different algorithms in terms of high sensitivity and low false alarm rates for feature extraction, selection, pre-processing, classification, and post-processing in designing a medical device for detecting seizure activity in EEG data. The current state-of-the-art methods which are validated clinically using large amounts of data are introduced.

The study focuses on finding potential machine learning methods, considering KNN, SVM, decision trees and, Random forests, and compares their performance on the task of seizure detection using features introduced in the literature. Also using ensemble methods namely, bootstrapping and majority voting techniques we achieved a sensitivity of 0.80 and FAR/h of 2.10, accuracy of 97.1% and specificity of 98.2%. Overall, the findings of this study can be useful for developing more accurate and efficient algorithms for EEG seizure detection medical device, which can contribute to the early diagnosis and treatment of epilepsy in the intensive care unit for critically ill patients.

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Copyright information: © Sanaz Afsari, 2023. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.