DOĞRUSAL ÖNGÖRÜMLEME KODLAMA YÖNTEMİ İLE EEG SİNYALLERİNDEN OTOMATİK EPİLEPTİK NÖBET TESPİTİ

Nuri İkizler

doi:10.17780/ksujes.1669078

TR EN

DOĞRUSAL ÖNGÖRÜMLEME KODLAMA YÖNTEMİ İLE EEG SİNYALLERİNDEN OTOMATİK EPİLEPTİK NÖBET TESPİTİ

Öz

EEG kayıtlarından otomatik epileptik nöbet tespiti, epilepsi tanı ve tedavisinde kritik bir rol oynamaktadır. Geleneksel olarak gerçekleştirilen nöbet tespitinde, uzmanların uzun süreli EEG kayıtlarını incelemesini gerekir, bu zaman alıcı ve insan hatasına açık bir süreçtir. Bu nedenle, EEG verilerinden nöbetleri otomatik olarak algılayan sistemler, daha hızlı, daha doğru ve kesintisiz izleme imkânı sunar. Ayrıca, uzun süreli EEG kayıtlarının depolanması da ayrı bir zorluk teşkil etmektedir. Bu çalışmada, her iki soruna da çözüm sunan tek bir yaklaşım ile EEG verileri doğrusal öngörümleme kodlama yöntemiyle kodlanarak ve doğrusal öngörümleme katsayıları ile bunların istatistiksel özelliklerini öznitelik vektörleri olarak kullanılarak, çok katmanlı algılayıcı, k-en yakın komşu, rastgele orman ve lojistik model ağacı sınıflandırıcılarının sınıflandırma doğrulukları incelenmiştir. Elde edilen sonuçlar ile orijinal EEG sinyali yerine yalnızca doğrusal öngörümleme katsayılarını kullanarak, çeşitli sınıflandırma görevlerinde çok yüksek doğrulukla otomatik nöbet tespiti yapılabileceği gösterilmiştir.

Anahtar Kelimeler

AUTOMATIC EPILEPTIC SEIZURE DETECTION FROM EEG SIGNALS USING THE LINEAR PREDICTIVE CODING METHOD

Abstract

Automatic epileptic seizure detection from EEG recordings plays a critical role in the diagnosis and treatment of epilepsy. Traditionally, seizure detection requires experts to review long-term EEG recordings, a time-consuming process prone to human error. Therefore, systems that automatically detect seizures from EEG data provide faster, more accurate, and continuous monitoring. Additionally, the storage of long-term EEG recordings presents a separate challenge. In this study, a single approach addressing both issues is proposed, where EEG data is encoded using the Linear Predictive Coding method, and the Linear Predictive Coding coefficients and their statistical features are used as feature vectors. The classification accuracy of Multi-Layer Perceptron, k-Nearest Neighbors, Random Forest, and Logistic Model Tree classifiers are examined. The results show that by using only Linear Predictive Coding coefficients instead of the original EEG signal, automatic seizure detection can be performed with very high accuracy in various classification tasks.

Keywords

Kaynakça

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Ayrıntılar

Birincil Dil

Türkçe

Konular

İnsan Merkezli Bilgi İşleme (Diğer) , Makine Öğrenme (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Nuri İkizler ^*
0000-0002-7632-1973
Türkiye

Yayımlanma Tarihi

3 Eylül 2025

Gönderilme Tarihi

2 Nisan 2025

Kabul Tarihi

16 Haziran 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 3

DOI

https://doi.org/10.17780/ksujes.1669078

IZ

https://izlik.org/JA27UR93EH

Kaynak Göster

RIS / Bibtex

APA

İkizler, N. (2025). DOĞRUSAL ÖNGÖRÜMLEME KODLAMA YÖNTEMİ İLE EEG SİNYALLERİNDEN OTOMATİK EPİLEPTİK NÖBET TESPİTİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(3), 1345-1361. https://doi.org/10.17780/ksujes.1669078