Latifoğlu F., Orhanbulucu F., Penekli S., Gültekin M., Baydemir R., Chowdhury M. E. H.
CURRENT NEUROVASCULAR RESEARCH, cilt.23, ss.1-10, 2026 (SCI-Expanded, Scopus)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
23
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Basım Tarihi:
2026
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Doi Numarası:
10.2174/0115672026485254260821045826
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Dergi Adı:
CURRENT NEUROVASCULAR RESEARCH
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Derginin Tarandığı İndeksler:
Scopus, Science Citation Index Expanded (SCI-EXPANDED), BIOSIS, EMBASE, MEDLINE
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Sayfa Sayıları:
ss.1-10
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Erciyes Üniversitesi Adresli:
Evet
Özet
Introduction/Objective::
Introduction/Objective: Parkinson's disease (PD) is a neurodegenerative disease that
can cause motor or non-motor symptoms as a result of damage to nerve cells in the brain. Recently,
studies on the diagnosis and prediction of this type of disease continue with the application
of machine learning (ML) and deep learning (DL) methods to electroencephalogram (EEG)
signals. This study proposes a novel diagnostic framework for classifying PD across both ON
and OFF medication states, leveraging EEG-based signal analysis and DL architectures.
Methods::
In the proposed approach, EEG signals were obtained from 15 patients with PD (ON
and OFF medication), with a mean age of 62.60, and 16 healthy controls (HC), with a mean age
of 63,50. Data augmentation was achieved by decomposing EEG signals into subbands using
Variational Mode Decomposition (VMD) as a signal processing method. Spectrogram images
were obtained by applying the Wavelet Coherence (WC) method to these subbands. The obtained
images were classified using a 2D convolutional neural network (2D-CNN).
Results::
Following the classification process, accuracy, sensitivity, specificity, and F1-Score
values were obtained and interpreted. In this study, which examined subband performance, the
best results were achieved with 99.80% accuracy, 99.79% sensitivity, 99.80% specificity, and
99.79% F1-Score.
Discussion::
The findings of this study confirm that the proposed approach outperforms similar
approaches in the literature. Given the clinical need for reliable, non-invasive methods for diagnosing
Parkinson’s disease, the success of the developed model represents a significant contribution
to the existing literature.
Conclusion::
This study has contributed a high-performance, innovative approach to the existing
literature on Parkinson’s disease diagnosis. The successful results obtained clearly demonstrate
that the model developed can serve as a robust and reliable framework not only for the diagnosis
of Parkinson’s disease, but also for similar biomedical signal analyses and the investigation of
various neurological disorders.