In Journal of neural engineering ; h5-index 52.0
In the last decades, machine learning (ML) approaches have been widely used to distinguish Parkinson's disease (PD) and many other neuropsychiatric diseases. They also speed up the clinicians and facilitate decision-making for several conditions with similar clinical symptoms. The current study attempts to detect PD with dementia (PDD) by Event-related Oscillations (EROs) during cognitive processing in two modalities, i.e. auditory and visual.
The study was conducted to discriminate PDD from healthy controls (HC) using event-related phase-locking factors in slow frequency ranges (delta and theta) during visual and auditory cognitive tasks. 17 PDD and 19 HC were included in the study, and Linear Discriminant Analysis (LDA) was used as a classifier. During classification analysis, multiple settings were implemented by using different sets of channels (overall, fronto-central and temporo-parieto-occipital region), frequency bands (delta-theta combined, delta, theta, and low theta), and time of interests (0.1- 0.7 s, 0.1 - 0.5 s and 0.1 - 0.3 s for delta, delta-theta combined; 0.1- 0.4 s for theta and low theta) for spatial-spectral-temporal searchlight procedure.
The classification performance results of the current study revealed that if visual stimuli are applied to PDD, the delta and theta phase-locking factor over fronto-central region have a remarkable contribution to detecting the disease, whereas if auditory stimuli are applied, the phase-locking factor in low theta over temporo-parieto-occipital and in a wider range of frequency (1-7 Hz) over the fronto-central region classify HC and PDD with better performances.
These findings show that the delta and theta phase-locking factor of EROs during visual and auditory stimuli has valuable contributions to detecting PDD.
Tülay Emine Elif, Yıldırım Ebru, Aktürk Tuba, Güntekin Bahar
Classification, Delta, Theta, Inter-trial phase coherence, Linear Discriminant Analysis, “Parkinsons Disease with dementia”