The Impact of Feature Selection on Epilepsy Diagnosis from EEG Signals: A Comparison of Dynamic Time Warping and Itakura-Saito Distance

Epileptic seizure detection is crucial for accurate diagnosis and effective treatment of epilepsy. Automated systems based on electroencephalogram signals play an important role in reducing the time, effort, and subjectivity involved in manual analysis performed by neurologists. However, many previo...

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Vydáno v:Advances in Electrical and Computer Engineering Ročník 25; číslo 3; s. 45 - 58
Hlavní autoři: EKIM, G., IKIZLER, N.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Stefan cel Mare University of Suceava 01.08.2025
Témata:
Algorithms
Analysis
biomedical signal processing
Care and treatment
Comparative analysis
decision support systems
Detectors
Diagnosis
Electroencephalography
Epilepsy
feature extraction
Machine learning
Methods
Neural networks
Physiological aspects
Seizures (Medicine)
Signal processing
Turkey
ISSN:1582-7445, 1844-7600
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Shrnutí:Epileptic seizure detection is crucial for accurate diagnosis and effective treatment of epilepsy. Automated systems based on electroencephalogram signals play an important role in reducing the time, effort, and subjectivity involved in manual analysis performed by neurologists. However, many previous studies mainly focused on timedomain similarity measures, fuzzy logic models, or neural network-based classifiers and did not fully explore the potential of spectral similarity features for seizure detection. This study introduces a new approach by applying the Dynamic Time Warping method directly to power spectral density data obtained using the Welch method. This enables a spectral similarity analysis that is independent of time shifts and better reflects the non-stationary nature of brain signals. In addition, the Itakura-Saito distance is investigated for the first time for epileptic seizure detection and compared with the Dynamic Time Warping method. A twelve-feature vector was created using reference-based similarity measurements combined with energy and spectral features, and multiple classification methods were evaluated. Experimental results on the Bonn electroencephalogram dataset demonstrate that Dynamic Time Warping achieved the highest classification performance, with the Random Forest algorithm reaching 100% accuracy in binary tasks. These findings highlight the importance of spectral similarity features in improving automatic seizure detection systems and demonstrate the clinical value of the proposed approach. Index Terms--biomedical signal processing, decision support systems, epilepsy, feature extraction, machine learning.
ISSN:1582-7445
1844-7600
DOI:10.4316/AECE.2025.03006