Using chemosensory-induced EEG signals to identify patients with de novo Parkinson's disease

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dc.contributor.authorOlcay, Bilal Orkan
dc.contributor.authorOnay, Fatih
dc.contributor.authorAkın Öztürk, Güliz
dc.contributor.authorÖniz, Adile
dc.contributor.authorÖzgören, Murat
dc.contributor.authorHummel, Thomas
dc.contributor.authorGüdücü, Ça?daş
dc.date.accessioned2026-02-08T15:11:07Z
dc.date.available2026-02-08T15:11:07Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractObjective: 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. © 2023 Elsevier Ltd
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK; Dokuz Eylül Üniversitesi, DEÜ, (121E122, KB.SAG.083); Dokuz Eylül Üniversitesi, DEÜ
dc.identifier.doi10.1016/j.bspc.2023.105438
dc.identifier.issn1746-8094
dc.identifier.scopus2-s2.0-85171865684
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2023.105438
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5255
dc.identifier.volume87
dc.identifier.wosWOS:001082128200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofBiomedical Signal Processing and Control
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzScopus_KA_20260207
dc.subjectClassification
dc.subjectEntropy
dc.subjectFeature extraction
dc.subjectFunctional connectivity
dc.subjectOlfaction
dc.subjectParkinson's disease
dc.titleUsing chemosensory-induced EEG signals to identify patients with de novo Parkinson's disease
dc.typeArticle

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