Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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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.Article Citation - WoS: 6Citation - Scopus: 10Using Chemosensory-Induced Eeg Signals To Identify Patients With <i>de Novo</I> Parkinson's Disease(Elsevier Sci Ltd, 2024) Olcay, Orkan; Onay, Fatih; Ozturk, Guliz Akin; Oniz, Adile; Ozgoren, Murat; Hummel, Thomas; Guducu, CagdasObjective: Parkinson's disease (PD) patients generally exhibit an olfactory loss. Hence, psychophysical or electrophysiological tests are used for diagnosis. However, these tests are susceptible to the subjects' behavioral response bias and require advanced techniques for an accurate analysis. Proposed Approach: Using well-known feature extraction methods, we characterized chemosensory-induced EEG responses of the participants to classify whether they have PD. The classification was performed for different time intervals after chemosensory stimulation to see which temporal segment better separates healthy controls and subjects with de novo PD. Results: The performances show that entropy and connectivity features discriminate effectively PD and HC participants when olfactory-induced EEG signals were used. For these methods, discrimination is over 80% for segments 100-700 and 200-800 milliseconds after stimulus onset. Comparison with Existing Methods: We compared the performance of our framework with linear predictive coding, bispectrum, wavelet entropy-based methods, and TDI score-based classification. While the entropy- and connectivity-based methods elicited the highest classification performances for olfactory stimuli, the linear predictive coding-based method elicited slightly higher performance than our framework when the trigeminal stimuli were used. Conclusion: This is one of the first studies that use chemosensory-induced EEG signals along with different feature extraction methods to classify healthy subjects and subjects with de novo PD. Our results show that entropy and functional connectivity methods unravel the chemosensory-induced neural dynamics encapsulating critical information about the subjects' olfactory performance. Furthermore, time- and frequency-resolved feature analysis is beneficial for capturing disease-affected neural patterns.