Analysis of major depressive disorder with EEG signals using adaptive signal processing and machine learning techniques

Abstract

Background and Objective: Major Depressive Disorder (MDD) is a significant mental health issue that negatively affects individuals' quality of life. In this study, an adaptive signal processing and machine learning-based approach was developed for the intelligent diagnosis of MDD through electroencephalogram (EEG) signals. Methods: The Variational Mode Decomposition (VMD), Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT) methods were applied to EEG data from 26 MDD patients and 29 healthy control subjects using a limited number of EEG channels obtained from the frontal region, thereby decomposing the signals into sub-frequency bands. Frequency and statistical features were extracted from the raw and decomposed EEG signals. The LASSO algorithm was used for feature selection, and classification was performed using four different machine learning models. Results: The results obtained show that adaptive signal processing methods significantly improve performance compared to raw EEG analysis. In particular, the VMD & artificial neural network (ANN) combination emerged as the most successful model. This model demonstrated superior classification performance compared to other models, with 96.66% accuracy, 98.60% AUC, and 96.72% F1 score values. Furthermore, the decision mechanism of this model was examined in detail using SHAP analysis, and potential biomarkers that could guide clinical decision-making processes in MDD diagnosis were identified. Conclusion: In this context, the findings obtained in the proposed study significantly contribute to improving early diagnosis processes and developing automated diagnosis systems, providing a solid foundation for the development of computer-based diagnostic tools that will strengthen clinical decision support mechanisms.

Keywords

• Electroencephalography
• Major depressive disorder
• Variational mode decomposition
• Empirical mode decomposition
• Empirical wavelet transform

Uyarlanabilir sinyal işleme ve makine öğrenmesi teknikleri ile EEG sinyalleri üzerinden majör depresif bozukluk analizi

Abstract

Giriş ve Amaç: Majör Depresif Bozukluk (MDB), bireylerin yaşam kalitesini olumsuz etkileyen önemli bir ruh sağlığı sorunudur. Bu çalışmada, elektroensefalogram (EEG) sinyalleri aracılığıyla MDB’nin akıllı teşhisi için adaptif sinyal işleme ve makine öğrenmesi tabanlı bir yaklaşım geliştirilmiştir. Yöntem: Varyasyonel Mod Ayrıştırma (VMA), Ampirik Mod Ayrıştırma (AMA) ve Ampirik Dalgacık Dönüşümü (ADD) yöntemleri, frontal bölgeden elde edilen sınırlı sayıda EEG kanalına ait veriler kullanılarak 26 majör depresif bozukluk (MDB) hastası ve 29 sağlıklı kontrol deneğine uygulanmıştır. Bu sayede sinyaller alt frekans bantlarına ayrıştırılmıştır. Ham ve ayrıştırılmış EEG sinyallerinden frekans ve istatistiksel özellikler çıkarılmıştır. Özellik seçimi için LASSO algoritması kullanılmış ve dört farklı makine öğrenme modeli ile sınıflandırma işlemi gerçekleştirilmiştir. Bulgular: Elde edilen sonuçlar, adaptif sinyal işleme yöntemlerinin, ham EEG analiziyle karşılaştırıldığında performansı anlamlı ölçüde artırdığını göstermektedir. Özellikle VMA ve yapay sinir ağı (YSA) kombinasyonu en başarılı model olarak öne çıkmıştır. Bu model, diğer modellere kıyasla %96.66 doğruluk, %98.60 AUC ve %96.72 F1 skoru ile üstün sınıflandırma performansı sergilemiştir. Ayrıca, bu modelin karar mekanizması SHAP analizi kullanılarak ayrıntılı biçimde incelenmiş ve MDB tanısında klinik karar verme süreçlerine rehberlik edebilecek potansiyel biyobelirteçler belirlenmiştir. Sonuç: Bu kapsamda, önerilen çalışmada elde edilen bulgular, erken tanı süreçlerinin iyileştirilmesine ve otomatik tanı sistemlerinin geliştirilmesine önemli ölçüde katkı sağlamaktadır. Ayrıca, bu bulgular klinik karar destek mekanizmalarını güçlendirecek bilgisayar tabanlı tanı araçlarının geliştirilmesi için sağlam bir temel oluşturmaktadır.

Keywords

• Elektroensefalografi
• Majör depresif bozukluk
• Varyasyonel mod ayrıştırma
• Ampirik mod ayrıştırma
• Ampirik dalgacık dönüşümü

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