Abstract
One of the most prevalent neurological disorders is epilepsy. Epileptic seizures can occur
repeatedly in people with the condition for no recognisable reason. The diagnostic methods
dependent on EEG are promising. EEG has uncovered the dynamic functioning of all brain areas
throughout time. Its low cost, non-invasiveness, and simply make it crucial for clinical
evaluations of brain function. Integrating multiple EEG biomarkers as a compound biomarker
could provide a high performance that may accelerate the diagnosis speeds. Artificial intelligence
techniques such as machine learning and deep learning provide a significant result in healthcare
applications. Logistic Regression (LR), Naive Bayes (NB), and Neural Network (NN) were
evaluated using a compound biomarker containing eleven EEG features that were extracted from
the Bonn EEG dataset. The aim of this study is to evaluate the feasibility of integrating multiple
EEG biomarkers as compound biomarkers for identifying epileptic people. The outcomes showed
the performance of all LR, NB, and NN detection models provide a high performance with
sensitivity and specificity of greater than 90%.
repeatedly in people with the condition for no recognisable reason. The diagnostic methods
dependent on EEG are promising. EEG has uncovered the dynamic functioning of all brain areas
throughout time. Its low cost, non-invasiveness, and simply make it crucial for clinical
evaluations of brain function. Integrating multiple EEG biomarkers as a compound biomarker
could provide a high performance that may accelerate the diagnosis speeds. Artificial intelligence
techniques such as machine learning and deep learning provide a significant result in healthcare
applications. Logistic Regression (LR), Naive Bayes (NB), and Neural Network (NN) were
evaluated using a compound biomarker containing eleven EEG features that were extracted from
the Bonn EEG dataset. The aim of this study is to evaluate the feasibility of integrating multiple
EEG biomarkers as compound biomarkers for identifying epileptic people. The outcomes showed
the performance of all LR, NB, and NN detection models provide a high performance with
sensitivity and specificity of greater than 90%.
| Original language | English |
|---|---|
| Pages (from-to) | 21-33 |
| Journal | Journal of Information Systems Engineering and Management |
| Volume | 10 |
| Issue number | 39 |
| DOIs | |
| Publication status | Published - Apr 2025 |
Keywords
- Epileptic seizures
- Healthcare Applications
- EEG Biomarkers
- Machine Learning
- Compound Biomarkers