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Journals / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2022 / Cilt: 12 Sayı: 4

Detection of Schizophrenia from EEG Signals by Permutation Entropy-Based Complexity Analysis

Permütasyon Entropi Tabanlı Karmaşıklık Analizi ile EEG İşaretlerinden Şizofreni Tespiti

Hasan Polat(BİNGÖL ÜNİVERSİTESİ, BİNGÖL TEKNİK BİLİMLER MESLEK YÜKSEKOKULU)0000-0001-5535-4832
Pages
2085–2096
Publication Date
2022-12-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results demonstrate that SZ can be detected efficiently from the P4 and T6 electrode locations.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Early diagnosis of schizophrenia (SZ) can improve the quality of life by allowing patients to receive more effective treatment.
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Key Findings & Data:PE analyzes were performed on EEG recordings from 45 adolescents with symptoms of SZ and 39 healthy participants.
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Conclusion & Impact:The results demonstrate that SZ can be detected efficiently from the P4 and T6 electrode locations.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Şizofreni · permütasyon entropi · karmaşıklık

Abstract

Early diagnosis of schizophrenia (SZ) can improve the quality of life by allowing patients to receive more effective treatment. The complexity and heterogeneity of symptoms related to the disease hinder early clinical diagnosis. In this context, electroencephalogram (EEG) is used as an alternative diagnostic tool for the probable SZ. Due to the high temporal resolution of the EEG technique, the reflections of cognitive and behavioral processes on cortical activities can be successfully investigated. In this study, it is aimed to classify and evaluate SZ anomalies by permutation entropy (PE) based complexity analysis of EEG signals. PE analyzes were performed on EEG recordings from 45 adolescents with symptoms of SZ and 39 healthy participants. For feature extraction, PE values are computed for all sub-bands in the EEG as delta, theta, alpha, beta, and gamma waves. A multilayer perceptron neural network (MLPNN) is used as a classifier model. The classification process was performed separately for each scalp electrode. Thus, comprehensive statistical analyzes of the PE distributions related to the efficient channels were performed. The results demonstrate that SZ can be detected efficiently from the P4 and T6 electrode locations. The classification accuracy of 87.2% and 86.8% is obtained for P4 and T6 channels, respectively. Moreover, statistical results of PE distributions showed that complex neurobehavioral features significantly decrease in the case of SZ patients for gamma activities.

Özet

Şizofreninin (SZ) erken tanısı hastaların daha etkili tedavi görmelerine olanak sağlayarak, yaşam kalitelerini artırır. Ancak, hastalığın karmaşık ve heterojen bulguları erken klinik tanıları sekteye uğratmaktadır. Bu bağlamda elektroansefalogram (EEG), olası SZ için alternatif bir tanı aracı olarak kullanılmaktadır. EEG tekniğinin yüksek temporal çözünürlük sunmasından dolayı, bilişsel ve davranışsal süreçlerin kortikal aktivitelere yansımaları başarılı bir şekilde irdelenebilir. Bu çalışmada, EEG işaretlerinin permütasyon entropi (PE) tabanlı karmaşıklık analizi ile SZ anomalilerin sınıflandırılması ve değerlendirilmesi amaçlanmıştır. PE analizleri, SZ semptomları sergileyen 45 adölesan birey ile 39 sağlıklı katılımcıdan alınan EEG kayıtları üzerinde uygulanmıştır. Özellik çıkarımı için delta, teta, alfa, beta ve gama dalgaları olmak üzere tüm alt bantların PE değerleri hesaplanmıştır. Sınıflandırıcı model olarak ise çok katmanlı perseptron sinir ağları (MLPNN) kullanılmıştır. Sınıflandırma işlemi her bir elektrot için ayrı bir şekilde yürütülmüştür. Böylelikle, SZ tespitinde etkin kanallar belirlenmiş ve bu kanallara ilişkin kapsamlı istatistiksel analizler uygulanmıştır. Bulgular, SZ tespitinin P4 ve T6 elektrot konumlarından etkin bir şekilde yapılabileceğini göstermiştir. Sınıflandırma doğrulukları P4 ve T6 kanalları için sırasıyla %87.2 ve %86.8 olarak elde edilmiştir. Ayrıca, PE dağılımlarının istatistiksel sonuçları, gama aktiviteleri için SZ hastalarında karmaşık nörodavranışsal özelliklerin önemli ölçüde azaldığını göstermiştir.

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Cite This Article

Polat, H. (2022). Detection of Schizophrenia from EEG Signals by Permutation Entropy-Based Complexity Analysis. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 12 Sayı: 4, 2085–2096. https://doi.org/10.21597/jist.1122315

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