Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
3 results
Search Results
Now showing 1 - 3 of 3
Article Citation - WoS: 2Citation - Scopus: 2Temporal Electroencephalography Features Unveiled Via Olfactory Stimulus as Biomarkers for Mild Alzheimer's Disease(Elsevier Sci Ltd, 2025) Olcay, Bilal Orkan; Pehlivan, Murat; Karacali, BilgeAim: Our primary aim is to capture and use the timings of the characteristic brain responses to olfactory stimulation for mild Alzheimer's disease diagnosis purposes. Proposed method: Our method identifies the timings of short-lived signal segments where characteristic distances between pre- and post-stimulus relative spectral energies are attained for each EEG channel and frequency band. These timings and timing-derived features were subsequently used in a leave-one-subject-out cross-validation scenario to assess the diagnostic performance of our framework. We evaluated seven distinct statistical distance measures to determine the most effective one for characterizing the neurological conditions of the subjects. Results: The average cross-validation performance shows that our framework achieved 87.50% diagnosis performance. The frequently used features were mainly derived from the delta and alpha activity of the prefrontal region (Fp1) and the beta activity of the parietal region (Pz), which agree with the current findings of olfaction biophysics. Comparison with existing methods: We compared the performance of our method with that of four existing methods in the literature. Our method outperformed these four methods. Moreover, our method elicited the highest accuracy when the clinical olfactory score (UPSIT) was included as a feature. Conclusions: Our analysis framework reveals a significant alteration of the timing organization of the brain that emerged upon olfactory stimulation in Alzheimer's patients. The timings of characteristic response and the features calculated via these timings contribute to Alzheimer's disease diagnosis performance remarkably. The perspective proposed here may facilitate early diagnosis, thereby facilitating the exploration of novel therapeutic and treatment strategies.Conference Object Ortak Bilgi Miktarının Modelden-Baǧımsız ve Hızlı Hesaplanması için Yeni Yöntemler(IEEE, 2018) Cagdas, Serhat; Karacali, BilgeIn this study, two new approaches are proposed to calculate mutual information between two random variables from data. These approaches are constructed in a way to use the properties of the differential entropy under linear transformations and to try to minimize conditional entropy in a model-free manner. In comparisons with a widely used mutual information estimator, the Kraskov method, the methods that we termed as unit vector parametrization and data fitting based estimators, offered an advantage in terms of computation speed.Conference Object Citation - Scopus: 3Entropik Kümeleme Kullanılarak Beyin Aktivitesi Karakterizasyonu(IEEE, 2017) Olcay, Bilal Orkan; Karacali, Bilge; Ozgoren, Murat; Guducu, CagdaIn this study, two novel entropy and mutual information based algorithms have been proposed to characterize the stimulus specific brain activity. In the first method, inter channel mutual information of electroencephalography signals has been calculated and the channels that exhibit synchronized behaivour during stimulus. In the second method, the responsiveness of the individual channels has been characterized in an entropic manner and then, the channels which demonstrates stimulus specific entropic behavior have been obtained. The performance of the proposed methods has been simulated on a real dataset obtained from Dokuz Eylul University Brain Biophysics laboratory. The results demonstrate that different regions of the brain exhibit a coherent activity during stimulus.