TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14365/4

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  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Myocardial Infarction in Young Adults: Diagnosis Begins Through Inspection
    (Kare Publ, 2024) Özpelit, Mehmet Emre; Kumral, Zeynep; Özpelit, Ebru; Çolak, Ayse
    Spontan koroner arter diseksiyonu (SKAD), genellikle orta yaşlı kadınları etkileyen nadir bir akut koroner sendrom formudur. Genetik vaskülopatiler de dahil olmak üzere bağ dokusu hastalıkları SCAD’ye yol açan önemli predispozan durumlardan biridir. Bu yazıda, anterior miyokard enfarktüsü geçiren ve vasküler tip Ehler-Danlos sendromu tanısı alan 36 yaşında bir erkek hasta sunulmuş ve literatür gözden geçirilmiştir.
  • Article
    Citation - WoS: 1
    Detection of Alzheimer's Dementia by Using Deep Time-Frequency Feature Extraction
    (AVES, 2024-01-30) Karabiber Cura, Özlem; Türe, H. Sabiha; Akan, Aydin; Cura, Ozlem Karabiber
    Alzheimer's disease (AD), a neurological condition connected with aging, causes cognitive deterioration and has a substantial influence on a patient's daily activities. One of the most widely used clinical methods for examining how AD affects the brain is the electroencephalogram (EEG). Handcraft calculating descriptive features for machine learning algorithms requires time and frequently increases computational complexity. Deep networks provide a practical solution to feature extraction compared to handcraft feature extraction. The proposed work employs a time-frequency (TF) representation and a deep feature extraction-based approach to detect EEG segments in control subjects (CS) and AD patients. To create EEG segments'TF representations, high-resolution synchrosqueezing transform (SST) and traditional short-time Fourier transform (STFT) approaches are utilized. For deep feature extraction, SST and STFT magnitudes are used. The collected features are classified using a variety of classifiers to determine the EEG segments of CS and AD patients. In comparison to the SST method, the STFT-based deep feature extraction strategy produced improved classification accuracy between 79.56% and 92.96%.