ADHD ve Sağlıklı Bireylerin Tanısında Boyut Azaltan Zamansal Karakteristik Özellik Çıkarma Yaklaşımı ve 1D-CNN

Year 2023, Volume: 5 Issue: 2, 349 - 359, 27.10.2023

Kutlucan Görür

https://doi.org/10.46387/bjesr.1336892

Abstract

EEG sinyalleri, bir çocukluk nörogelişimsel bozukluğu olan ADHD/ Attention Deficit Hyperactivity Disorder (Dikkat Eksikliği Hiperaktivite Bozukluğu) ile ilgili kritik bilgileri ayıklamak için güvenilir bir şekilde kullanılabilir. ADHD'nin erken tespiti, bu bozukluğun gelişimini azaltmak ve uzun vadeli etkisini azaltmak için önemlidir. Bu çalışmanın amacı, katılımcıların ekran üzerindeki rakamları takip etmeleri istenirken toplanan Elektroensefalografi (EEG) sinyallerinden, t-SNE tekniği ile zaman alanında özellik çıkarıldıktan sonra, RNN (Recurrent Neural Network) derin öğrenme modelleri ile ADHD ve sağlıklı bireyleri ayıran yüksek bir tahmin başarısına sahip bir çalışma-çerçevesi tanımlamaktır. Çalışmaya 15 ADHD hastası ve 15 sağlıklı kontrol bireyi dahil edilmiştir. 15’er kişiden oluşan veri setleri (ACC: ≤100% ve AUC: 1), 10’ar kişiden oluşan veri setlerinden (ACC: ≥94.23% ve AUC: 1) daha başarılı sonuçlar ürettiğini göstermiştir. t-SNE, yüksek boyutlu özellik görselleştirme veri gösterim tekniği olarak kullanıldığında da her iki grubun da önemli ölçüde ayırt edilebildiğini ortaya koymuştur. Bulgular, ADHD'nin erken teşhisinde ve objektif tanısında yardımcı olacağı düşünülmektedir.

Keywords

ADHD, t-SNE Özellik Çıkarma, Derin Öğrenme, Hastalık Teşhisi

Low Dimensionality Temporal Characteristic Feature Extraction Approach and 1D-CNN for Diagnosing ADHD and Healthy Individuals

Abstract

EEGs (Electroencephalography) can be reliably used to extract critical information about ADHD/Attention Deficit Hyperactivity Disorder, a childhood neurodevelopmental disorder. Early detection of ADHD is important to reduce the development of this disorder and lessen its long-term impact. This study aims to achieve a high prediction success framework that distinguishes ADHD and healthy individuals with RNN (Recurrent Neural Network) models, after extracting the features with the t-SNE technique from the EEGs. It is to define a high-success framework that has 15 ADHD patients and 15 healthy controls included in the study. Datasets comprising 15 people (ACC: ≤100% and AUC: 1) have shown more successful results than datasets comprising 10 people (ACC: ≥94.23% and AUC: 1). Both groups were significantly distinguishable when t-SNE was used as a high-dimensional feature visualization data display technique. The findings are thought to be helpful in the early and objective diagnosis of ADHD.

Keywords

ADHD, t-SNE Feature Extraction, Deep Learning, Diagnostic

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