Combination of Multidistance Signal Level Difference and Time Domain Features for Epileptic Seizure Classification

• Published in: JOIV : international journal on informatics visualization Online Vol. 9; no. 2; p. 482
• Main Authors: Amalia, Qoriina Dwi, Beu, Donny Setiawan, Rizal, Achmad, Ziani, Said
• Format: Journal Article
• Language: English
• Published: 31.03.2025
• ISSN: 2549-9610; 2549-9904; 2549-9904
• DOI: 10.62527/joiv.9.2.2692

Epileptic seizures are neurological disorders characterized by abnormal electrical activity in the brain, causing a series of seizures or episodes of temporary loss of consciousness. This research aims to develop a method of detecting and classifying epileptic seizures using one-dimensional EEG signals with the Multidistance Signal Level Difference (MSLD) approach and time domain feature extraction. The goal is to improve accuracy in distinguishing normal, interictal, and ictal conditions in EEG signals. The dataset from Bonn University consists of one-dimensional EEG signals that include normal, interictal, and ictal states. The analysis method includes extracting time domain features from EEG signals, such as Integrated EMG (IEMG), Mean Absolute Value (MAV), and others. The next step is the application of three classification algorithms, namely linear SVM, quadratic SVM, and cubic SVM, to classify the three conditions. Testing is done by measuring the accuracy of the classification results. The results of this study show that by using 14-time domain features and the MSLD approach, the most significant classification accuracy achieved was 98.7%. This result demonstrates the effectiveness of the proposed method in distinguishing normal, interictal, and ictal conditions. This research provides a foundation for further study in developing EEG signal classification analysis models. Future research can expand the scope by considering larger datasets, using more sophisticated feature extraction techniques, and exploring more complex classification algorithms to improve the accuracy and reliability of the model in real-world applications, particularly in the medical field for the diagnosis of epileptic seizures.