• Türkçe
    • English
  • English 
    • Türkçe
    • English
  • Login
View Item 
  •   [email protected]
  • Araştırma Çıktıları | TR-Dizin | WoS | Scopus | PubMed
  • Scopus İndeksli Yayınlar Koleksiyonu
  • View Item
  •   [email protected]
  • Araştırma Çıktıları | TR-Dizin | WoS | Scopus | PubMed
  • Scopus İndeksli Yayınlar Koleksiyonu
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

The use of quantitative eeg for differentiating frontotemporal dementia from late-onset bipolar disorder

Thumbnail

View/Open

Tam Metin / Full Text (240.9Kb)

Access

info:eu-repo/semantics/openAccess

Date

2018

Author

Metin, Sinem Zeynep
Ergüzel, Türker Tekin
Ertan, Gülhan
Şalçini, Celal
Koçarslan, Betül
Çebi, Merve
Metin, Barış
Tanrıdağ, Oğuz
Tarhan, Nevzat

Metadata

Show full item record

Citation

Metin, S. Z., Ergüzel, T., Ertan, G., Şalçini, C., Koçarslan, B., Çebi, M. ... Tarhan, N. (2018). The use of quantitative eeg for differentiating frontotemporal dementia from late-onset bipolar disorder. Clinical Eeg and Neuroscience, 49(3), 171-176. https://dx.doi.org/10.1177/1550059417750914

Abstract

The behavioral variant frontotemporal dementia (bvFTD) usually emerges with behavioral changes similar to changes in late-life bipolar disorder (BD) especially in the early stages. According to the literature, a substantial number of bvFTD cases have been misdiagnosed as BD. Since the literature lacks studies comparing differential diagnosis ability of electrophysiological and neuroimaging findings in BD and bvFTD, we aimed to show their classification power using an artificial neural network and genetic algorithm based approach. Eighteen patients with the diagnosis of bvFTD and 20 patients with the diagnosis of late-life BD are included in the study. All patients' clinical magnetic resonance imaging (MRI) scan and electroencephalography recordings were assessed by a double-blind method to make diagnosis from MRI data. Classification of bvFTD and BD from total 38 participants was performed using feature selection and a neural network based on general algorithm. The artificial neural network method classified BD from bvFTD with 76% overall accuracy only by using on EEG power values. The radiological diagnosis classified BD from bvFTD with 79% overall accuracy. When the radiological diagnosis was added to the EEG analysis, the total classification performance raised to 87% overall accuracy. These results suggest that EEG and MRI combination has more powerful classification ability as compared with EEG and MRI alone. The findings may support the utility of neurophysiological and structural neuroimaging assessments for discriminating the 2 pathologies.

WoS Q Kategorisi

Q4

Source

Clinical Eeg and Neuroscience

Volume

49

Issue

3

URI

https://dx.doi.org/10.1177/1550059417750914
https://hdl.handle.net/20.500.12511/2183

Collections

  • Makale Koleksiyonu [3079]
  • PubMed İndeksli Yayınlar Koleksiyonu [3361]
  • Scopus İndeksli Yayınlar Koleksiyonu [5122]
  • WoS İndeksli Yayınlar Koleksiyonu [5378]



DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV
 

 




| Guide | Contact |

[email protected]

by OpenAIRE
Advanced Search

sherpa/romeo

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsInstitution AuthorORCIDTitlesSubjectsTypeLanguageDepartmentCategoryWoS Q ValueScopus Q ValuePublisherAccess TypeThis CollectionBy Issue DateAuthorsInstitution AuthorORCIDTitlesSubjectsTypeLanguageDepartmentCategoryWoS Q ValueScopus Q ValuePublisherAccess Type

My Account

LoginRegister

Statistics

View Google Analytics Statistics

DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV
 

 


|| Guide || Library || İstanbul Medipol University || OAI-PMH ||

Kütüphane ve Dokümantasyon Daire Başkanlığı, İstabul, Turkey
If you find any errors in content, please contact: [email protected]

Creative Commons License
[email protected] by İstanbul Medipol University Institutional Repository is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License..

[email protected]:


DSpace 6.2

tarafından İdeal DSpace hizmetleri çerçevesinde özelleştirilerek kurulmuştur.