Abstract
In the paper, convolutional neural network models were proposed that classify the patient’s EEG into one of the three groups of parkinsonism, i.e., no symptoms (PD-N), mild cognitive impairment(PD-MCI), and Parkinson’s Disease Dementia (PD-PDD). Three different architectures of Deep Convolution Neural Networks were proposed. As the input of the CNN, two approaches were employed: the raw EEG signal and its transformation to power spectral density (PSD). The classification process was performed as a three-class task (PD-N vs PD-MCI vs PDD) and a two-class problem (PD-N vs PD-MCI, PD-N vs PD-PDD, and PD-MCI vs PD-PDD). The obtained accuracy for three classes exceeded 50%, and for the two-class, it was mostly between 60 and 70%.
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Acknowledgement
The study was realized within the project “TeleBrain – Artificially Intelligent EEG Analysis in the Cloud” (grant no. WPN3/9/TeleBrain-/2018).
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Bugdol, M., Ledwoń, D., Bugdol, M.N., Zawiślak-Fornagiel, K., Danch-Wierzchowska, M., Mitas, A.W. (2022). EEG Signal and Deep Learning Approach in Evaluation of Cognitive Declines in Parkinson’s Disease. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2022. Advances in Intelligent Systems and Computing, vol 1429. Springer, Cham. https://doi.org/10.1007/978-3-031-09135-3_4
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