Convmixer ve SDD Kullanılarak DEHB Hastalığının EEG Sinyalleri ile Otomatik Olarak Tespit Edilmesi

Year 2024, Volume: 13 Issue: 1, 19 - 25, 26.03.2024

Buğra Karakaş , Salih Taha Alperen Özçelik , Hakan Uyanık , Hüseyin Üzen , Abdülkadir Şengür

https://doi.org/10.46810/tdfd.1388893

Abstract

DEHB, çocuklarda dikkat eksikliği, davranış problemleri, eğitimle ilgili sorunlar ve düşük özgüven gibi problemler oluşturabilir. Bu çalışma, Dikkat Eksikliği Hiperaktivite Bozukluğu (DEHB) teşhisini elektroensefalografi (EEG) sinyalleriyle değerlendirmeyi hedefleyen bir araştırmayı özetlemektedir. Araştırma, 30 DEHB tanısı almış çocuk ve 30 sağlıklı kontrol grubunun EEG verilerini kullanmıştır. EEG verileri öncelikle gürültü azaltma amacıyla işlenmiş ve ardından ConvMixer, ResNet50 ve ResNet18 gibi derin öğrenme modelleri kullanılarak sınıflandırılmıştır. Bulgular, ConvMixer'in düşük hesaplama kaynaklarına ihtiyaç duyarak yüksek sınıflandırma başarısı elde ettiğini göstermektedir. Ayrıca, EEG sinyallerinin DEHB teşhisinde kullanılabilirliği konusunda farklı kanalların etkileri incelenmiş ve T8 kanalının özellikle etkili olduğu tespit edilmiştir. Bu çalışma, EEG tabanlı DEHB teşhisi için daha hafif modellerin kullanılabilirliğini ve EEG kanallarının önemini vurgulamaktadır.

Keywords

EEG, Derin Öğrenme, DEHB, Sürekli dalgacık dönüşümü

Advanced EEG-Based Analysis for ADHD Identification Utilizing Convmixer and Continuous Wavelet Transform

Abstract

Children with ADHD may experience challenges such as attention deficits, behavioral problems, educational problems, and low self-confidence. This study summarizes research aiming to evaluate the diagnosis of attention deficit hyperactivity disorder (ADHD) with electroencephalography (EEG) signals. The research used EEG data from 30 children diagnosed with ADHD and 30 healthy control groups. EEG data was first processed for noise reduction purposes and then classified using deep learning models such as ConvMixer, ResNet50, and ResNet18. The findings show that ConvMixer demonstrates high accuracy in classification. while requiring low computational resources. Additionally, the effects of different channels on the usability of EEG signals in the diagnosis of ADHD were examined, and the T8 channel was found to be particularly effective. In conclusion, the study emphasizes the effectiveness of lightweight models and underscores the significance of specific EEG channels in diagnosing ADHD using EEG signals.

Keywords

EEG, Deep learning, ADHD, Continious wavelet transform

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