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Dergiler / Firat University Journal of Experimental and Computational Engineering / 2026 / Cilt: 5 Sayı: 3

LBP-Moment: Otomatik DEHB Tespiti için Hafif ve Yorumlanabilir Bir EEG Modeli

LBP-Moment: A Lightweight and Interpretable EEG Framework for Automated ADHD Detection

İrem Taşcı(FIRAT UNIVERSITY)0000-0001-7069-769X
Ömer Faruk Göktaş(ANKARA YILDIRIM BEYAZIT ÜNİVERSİTESİ)0000-0002-2021-4052
Kübra Yıldırım(FIRAT UNIVERSITY)0000-0002-4738-2777
Abdul Hafeez Baig(University of Southern Queensland)0000-0003-3848-8008
Mehmet Bayğın(ERZURUM TEKNİK ÜNİVERSİTESİ)0000-0001-6449-8950
Burak Tasci(FIRAT ÜNİVERSİTESİ)0000-0002-4490-0946
Sengul Dogan(FIRAT UNIVERSITY)0000-0001-9677-5684
Türker Tuncer(FIRAT UNIVERSITY)0000-0002-5126-6445
Sayfa
827–847
Yayın Tarihi
2026-10-25
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bulgular, sınırlı donanımda çalışabilen saydam bir EEG özellik mühendisliği yaklaşımını desteklemektedir.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Dikkat eksikliği hiperaktivite bozukluğu (DEHB), yaygın bir nörogelişimsel bozukluktur.
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Temel Bulgular & Veri:Her 5 saniyelik segmentten 256 “ yerel ikili örüntü (LBP) histogramı ile 40 boyutlu IST40 istatistiksel özellik vektörü çıkarılmış ve 296 boyutlu bir temsil elde edilmiştir.
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Sonuç & Katkı:Bulgular, sınırlı donanımda çalışabilen saydam bir EEG özellik mühendisliği yaklaşımını desteklemektedir.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:DEHB tespiti · EEG sınıflandırması · Yerel ikili örüntü

Özet

Dikkat eksikliği hiperaktivite bozukluğu (DEHB), yaygın bir nörogelişimsel bozukluktur. Bu çalışmada, elektroensefalografi (EEG) sinyallerinden DEHB'nin otomatik tespiti için hafif ve yorumlanabilir bir makine öğrenmesi çerçevesi sunulmuştur. Her 5 saniyelik segmentten 256 “ yerel ikili örüntü (LBP) histogramı ile 40 boyutlu IST40 istatistiksel özellik vektörü çıkarılmış ve 296 boyutlu bir temsil elde edilmiştir. Yinelemeli Ki-kare seçimi bu uzayı 132 özelliğe indirmiştir. Manhattan uzaklığına dayalı k-en yakın komşu sınıflandırıcısı (k=1), segment düzeyinde 10 katlı çapraz doğrulama ile değerlendirilmiştir. Model %97,65 doğruluk, %97,65 F1 puanı, 0,9527 Cohen kappa ve 0,9762 AUC üretmiştir. Ayrıştırma analizi, LBP ve IST40 özelliklerinin tamamlayıcı katkısını ve özellik seçiminin boyutu %55,4 azaltırken doğruluğu 0,35 yüzde puanı artırdığını göstermiştir. Bulgular, sınırlı donanımda çalışabilen saydam bir EEG özellik mühendisliği yaklaşımını desteklemektedir. Bununla birlikte, katılımcı düzeyinde ve bağımsız veri setlerinde doğrulama klinik yorumdan önce gereklidir.

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Here we present LBP-Moment, a lightweight and interpretable framework for automated ADHD classification from electroencephalography (EEG). Each 5-s segment was represented by a 256-dimensional local binary pattern histogram and 40 statistical features (IST40). Iterative Chi-square selection reduced the resulting 296-dimensional space to 132 features. A k-nearest-neighbour classifier (k=1; Manhattan distance) was evaluated by segment-level 10-fold cross-validation. The model achieved 97.65% accuracy, a 97.65% F1-score, a Cohen's kappa of 0.9527 and an area under the receiver operating characteristic curve of 0.9762. Ablation analysis showed complementary contributions from LBP and IST40; feature selection removed 55.4% of the features and increased accuracy by 0.35 percentage points. These findings support a transparent, CPU-based feature-engineering approach, but participant-wise and external validation are required before clinical interpretation.

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Bu Makaleye Atıf Yap

Taşcı, İ., Göktaş, Ö. F., Yıldırım, K., Baig, A. H., Bayğın, M., Tasci, B., Dogan, S., Tuncer, T. (2026). LBP-Moment: Otomatik DEHB Tespiti için Hafif ve Yorumlanabilir Bir EEG Modeli. Firat University Journal of Experimental and Computational Engineering, Cilt: 5 Sayı: 3, 827–847. https://doi.org/10.62520/fujece.1950290

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