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Detection of anxiety with non-linear EEG dynamics

Year 2024, Volume: 13 Issue: 2, 558 - 567, 15.04.2024

Elif Uğurgöl Turgay Batbat Demet Yesilbas Miray Altınkaynak Ayşegül Güven Esra Demirci Nazan Dolu

https://doi.org/10.28948/ngumuh.1359809

Abstract

Anxiety is a psychiatric disorder characterized by excessive worry frequently encountered within society. Given the prevalence of anxiety and the limitations of current subjective assessment methods, the quantitative determination of this disorder gains significance. In pursuit of this objective, the study employed the 4-point likert-type Beck Anxiety Scale alongside essential clinical evaluations. As a result of the assessment, two participant groups were formed: one consisting of individuals with anxiety disorder and the other serving as the control group. Electroencephalography (EEG) recordings were obtained from the participants during resting states, followed by the computation of entropy and Hjorth (mobility, complexity) parameters from the EEG signals. The computed features were then classified using machine learning algorithms, namely K-Nearest Neighbor (kNN), Multi-Layer Perceptron (MLP), and Random Forest (RF), for classification purposes. The k-Nearest Neighbor (kNN) model, which yielded the most successful outcome among these classifiers, was able to reach an accuracy level of 88.4%. Furthermore, the combined utilization of diverse parameters was observed to lead to an increase in the success rate across all three algorithms.

Keywords

EEG Anxiety Disorder Resting State Machine Learning

Ethical Statement

Valid ethical documents regarding the method and participants used in the study have been uploaded as an additional file.

Supporting Institution

TUBITAK

Project Number

121E502

References

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Doğrusal olmayan EEG dinamikleri ile anksiyete tespiti

Year 2024, Volume: 13 Issue: 2, 558 - 567, 15.04.2024

Elif Uğurgöl Turgay Batbat Demet Yesilbas Miray Altınkaynak Ayşegül Güven Esra Demirci Nazan Dolu

https://doi.org/10.28948/ngumuh.1359809

Abstract

Anksiyete, toplum içerisinde sıklıkla rastlanılan ve aşırı kaygı ile karakterize edilen psikiyatrik bir bozukluktur. Mevcut subjektif yöntemler düşünüldüğünde bu bozukluğun kantitatif yöntemlerle tespiti önem kazanmaktadır. Bu amaçla yapılan çalışmada 4’lü likert tipli Beck Anksiyete Ölçeği kullanılıp gerekli klinik değerlendirmeler yapılmıştır. Değerlendirme sonucunda anksiyete bozukluğu bulunan grup ve kontrol grubu şeklinde iki katılımcı grubu belirlenmiştir. Katılımcılardan dinlenim durumunda Elektroensefalografi (EEG) kayıtları alınmış daha sonra EEG sinyallerinden entropi ve Hjorth (karmaşıklık, hareketlilik) parametreleri hesaplanmıştır. Hesaplanan öznitelikler makine öğrenmesinde K -En Yakın Komşu (K-Nearest Neighbor, kNN), Çok Katmanlı Algılayıcı (Multi-Layer Perceptron, MLP) ve Rastgele Orman (Random Forest, RF) sınıflandırma algoritmalarıyla sınıflandırılmışlardır. Bu sınıflandırıcılardan en başarılı sonuç veren model olan kNN %88.4 değerine kadar ulaşabilmiştir. Ayrıca farklı parametrelerin bir arada kullanımının başarı oranında 3 algoritma için yükselişe sebep olduğu gözlenmiştir. Bu sonuçlar makineli öğrenme tekniklerinin anksiyetenin tanı süreçlerinde kullanımına uygun olduğunu gösteren çalışmaları desteklemektedir.

Keywords

EEG Anksiyete Bozukluğu Dinlenim Durumu Makineli Öğrenme

Ethical Statement

Çalışmada kullanılan yöntem ve katılımcılarla ilgili geçerli etik belgeler ek dosya olarka yüklenmiştir.

Supporting Institution

TÜBİTAK

Project Number

121E502

References

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  • A. Al-Ezzi, N. Kamel, I. Faye, E. Gunaseli, Analysis of default mode network in social anxiety disorder: Eeg resting-state effective connectivity study. Sensors, 21, 1–19, 2021. https://doi.org/10.3390/s21124098.
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  • N. Kannathal, M.L. Choo, U.R. Acharya, P.K. Sadasivan, Entropies for detection of epilepsy in EEG. Computer Methods and Programs in Biomedicine, 80, 187–194, 2005. https://doi.org/10.1016/j.cmpb.2005.06.012.
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  • S.-H. Oh, Y.-R. Lee, H.-N. Kim, A Novel EEG Feature Extraction Method Using Hjorth Parameter. International Journal of Electronics and Electrical Engineering, 2, 106–110, 2014. https://doi. org/10.12720/ijeee.2.2.106-110.
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  • X.T. Li, The distribution of left and right handedness in Chinese people. Acta Psychologica Sinica, 3, 268–276, 1983.
  • M. Altınkaynak, Dikkat Eksikliği Ve Hiperaktivitesi Olan Hastalarda Kognitif Fonksiyonların Uyarılmış Potansiyel Ve Fonksiyonel Yakın Kızıl Ötesi Spektroskopisi Yöntemleriyle İncelenmesi. Doktora Tezi, Erciyes Üniversitesi Fen Bilimleri Enstitüsü, Türkiye, 2021.
  • L. Guo, Y. Wu, L. Zhao, T. Cao, W. Yan, X. Shen, Classification of mental task from EEG signals using immune feature weighted support vector machines. IEEE Transactions on Magnetics, 47, 866–869, 2011. https://doi.org/ 10.1109/TMAG.2010.2072775.
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There are 64 citations in total.

Details

Primary Language Turkish
Subjects Biomedical Diagnosis
Journal Section Research Articles
Authors

Elif Uğurgöl 0000-0002-6071-9020

Turgay Batbat 0000-0002-0128-2076

Demet Yesilbas 0000-0001-9070-4439

Miray Altınkaynak 0000-0002-0258-2804

Ayşegül Güven 0000-0001-8517-3530

Esra Demirci 0000-0002-8424-4947

Nazan Dolu 0000-0002-3104-7587

Project Number 121E502
Early Pub Date February 15, 2024
Publication Date April 15, 2024
Submission Date September 13, 2023
Acceptance Date January 30, 2024
Published in Issue Year 2024 Volume: 13 Issue: 2

Cite

APA Uğurgöl, E., Batbat, T., Yesilbas, D., Altınkaynak, M., et al. (2024). Doğrusal olmayan EEG dinamikleri ile anksiyete tespiti. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 13(2), 558-567. https://doi.org/10.28948/ngumuh.1359809
AMA Uğurgöl E, Batbat T, Yesilbas D, Altınkaynak M, Güven A, Demirci E, Dolu N. Doğrusal olmayan EEG dinamikleri ile anksiyete tespiti. NOHU J. Eng. Sci. April 2024;13(2):558-567. doi:10.28948/ngumuh.1359809
Chicago Uğurgöl, Elif, Turgay Batbat, Demet Yesilbas, Miray Altınkaynak, Ayşegül Güven, Esra Demirci, and Nazan Dolu. “Doğrusal Olmayan EEG Dinamikleri Ile Anksiyete Tespiti”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 13, no. 2 (April 2024): 558-67. https://doi.org/10.28948/ngumuh.1359809.
EndNote Uğurgöl E, Batbat T, Yesilbas D, Altınkaynak M, Güven A, Demirci E, Dolu N (April 1, 2024) Doğrusal olmayan EEG dinamikleri ile anksiyete tespiti. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 13 2 558–567.
IEEE E. Uğurgöl, “Doğrusal olmayan EEG dinamikleri ile anksiyete tespiti”, NOHU J. Eng. Sci., vol. 13, no. 2, pp. 558–567, 2024, doi: 10.28948/ngumuh.1359809.
ISNAD Uğurgöl, Elif et al. “Doğrusal Olmayan EEG Dinamikleri Ile Anksiyete Tespiti”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 13/2 (April 2024), 558-567. https://doi.org/10.28948/ngumuh.1359809.
JAMA Uğurgöl E, Batbat T, Yesilbas D, Altınkaynak M, Güven A, Demirci E, Dolu N. Doğrusal olmayan EEG dinamikleri ile anksiyete tespiti. NOHU J. Eng. Sci. 2024;13:558–567.
MLA Uğurgöl, Elif et al. “Doğrusal Olmayan EEG Dinamikleri Ile Anksiyete Tespiti”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 13, no. 2, 2024, pp. 558-67, doi:10.28948/ngumuh.1359809.
Vancouver Uğurgöl E, Batbat T, Yesilbas D, Altınkaynak M, Güven A, Demirci E, Dolu N. Doğrusal olmayan EEG dinamikleri ile anksiyete tespiti. NOHU J. Eng. Sci. 2024;13(2):558-67.
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