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    Evaluation of alexithymia, anger and anxiety depression levels in smokers
    (Taylor & Francis Inc, 2024) Alagoz, Yasemin; Cihan, Fatma Goksin; Kutlu, Ruhusen; Alagoz, Celal; Eren, I'brahim; Ecirli, Samil
    ObjectiveSmoking is a major mental health concern due to its addictive nature and its status as the leading preventable cause of premature death worldwide. This study aimed to examine the levels of alexithymia, anger, anxiety, and depression in smokers compared to nonsmokers.Materials and MethodsThis case-control study involved 176 smokers from a Smoking Cessation Clinic and 175 age- and gender-matched nonsmokers. Participants completed assessments using the Toronto Alexithymia Scale (TAS-20), State-Trait Anger Expression Inventory (STAXI), and Hospital Anxiety and Depression Scale (HADS). Nicotine dependence in smokers was measured using the Fagerstrom Nicotine Dependency Test. Statistical analysis was performed using SPSS 22.0.FindingsSignificant differences were observed between smokers and nonsmokers in TAS, TAS-1, TAS-2, HADS-A, and HADS-D scores. Smokers exhibited higher levels of alexithymia, anger expression, and anxiety, while nonsmokers demonstrated better anger control. Additionally, addiction levels in smokers were associated with variations in TAS, TAS-1, TAS-2, TAS-3, STAXI scores (SA, AC, AI, AO), and HADS-A, highlighting a complex interplay between addiction, psychological factors, and smoking habits.ResultThis study establishes a link between smoking status, addiction levels, and elevated alexithymia, anger, anxiety, and depression. The findings underscore the psychological impact of smoking, contributing valuable insights for mental health interventions in individuals with smoking habits.
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    Robust and Efficient Atrial Fibrillation Detection from Intracardiac Electrograms Using Minirocket
    (Kırıkkale Üniversitesi, 2024) Alagoz, Celal
    Atrial Fibrillation (AF) detection from intracardiac Electrogram (EGM) signals is a critical aspect of cardiovascular health monitoring. This study explores the application of Minirocket, a time series classification (TSC) algorithm, for robust and efficient AF detection. A comparative analysis is conducted against a deep learning approach using a subset of the dataset from Rodrigo et al. (2022). The study investigates the robustness of Minirocket in the face of shorter EGM sequences and varying training sizes, essential for real-world applications such as wearable and implanted devices. Empirical runtime analysis further assesses the efficiency of Minirocket in comparison to conventional machine learning (ML) algorithms. The results showcase Minirocket's notable performance, especially in scenarios with shorter signals and varying training sizes, making it a promising candidate for streamlined AF detection in emerging cardiovascular monitoring technologies. This research contributes to the optimization of AF detection algorithms for increased efficiency and adaptability to dynamic clinical scenarios.