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Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy.
Metin, Sinem Zeynep; Uyulan, Çaglar; Farhad, Shams; Ergüzel, Türker Tekin; Türk, Ömer; Metin, Baris; Çerezci, Önder; Tarhan, Nevzat.
Afiliación
  • Metin SZ; Department of Psychiatry, Uskudar University, Istanbul, Turkey.
  • Uyulan Ç; Department of Mechanical Engineering, Katip Çelebi University, Izmir, Turkey.
  • Farhad S; Department of Neuroscience, Uskudar University, Istanbul, Turkey.
  • Ergüzel TT; Department of Software Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey.
  • Türk Ö; Department of Computer Technologies, Artuklu University, Mardin, Turkey.
  • Metin B; Neurology Department, Medical Faculty, Uskudar University, Istanbul, Turkey.
  • Çerezci Ö; Department of Physioterapy and Rehabilitation, Faculty of Health SciencesUskudar University, Istanbul, Turkey.
  • Tarhan N; Department of Psychiatry, Uskudar University, Istanbul, Turkey.
Clin EEG Neurosci ; : 15500594241273181, 2024 Sep 09.
Article en En | MEDLINE | ID: mdl-39251228
ABSTRACT

Background:

Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistant depression (TRD), which is characterized by an inadequate response to antidepressant treatment. Identifying the TRD population is crucial in terms of saving time and resources in depression treatment. Recently several studies employed various methods on EEG datasets for automatic depression detection or treatment outcome prediction. However, no previous study has used the deep learning (DL) approach and EEG signals for detecting treatment resistance.

Method:

77 patients with TRD, 43 patients with non-TRD, and 40 healthy controls were compared using GoogleNet convolutional neural network and DL on EEG data. Additionally, Class Activation Maps (CAMs) acquired from the TRD and non-TRD groups were used to obtain distinctive regions for classification.

Results:

GoogleNet classified the healthy controls and non-TRD group with 88.43%, the healthy controls and TRD subjects with 89.73%, and the TRD and non-TRD group with 90.05% accuracy. The external validation accuracy for the TRD-non-TRD classification was 73.33%. Finally, the CAM analysis revealed that the TRD group contained dominant features in class detection of deep learning architecture in almost all electrodes.

Limitations:

Our study is limited by the moderate sample size of clinical groups and the retrospective nature of the study.

Conclusion:

These findings suggest that EEG-based deep learning can be used to classify treatment resistance in depression and may in the future prove to be a useful tool in psychiatry practice to identify patients who need more vigorous intervention.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin EEG Neurosci Asunto de la revista: CEREBRO / NEUROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Turquía Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin EEG Neurosci Asunto de la revista: CEREBRO / NEUROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Turquía Pais de publicación: Estados Unidos