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

Öz

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.

Anahtar Kelimeler

• DEHB tespiti
• EEG sınıflandırması
• Yerel ikili örüntü
• Ki-kare özellik seçimi
• kNN

Etik Beyan

Hazırlanan makalede herhangi bir kişi veya kurumla çıkar çatışması bulunmamaktadır.

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

Öz

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.

Anahtar Kelimeler

• ADHD detection
• EEG classification
• Local binary pattern
• Chi-square feature selection
• kNN.

Etik Beyan

There is no conflict of interest with any person/institution in the prepared article.

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