P 55 Functional connectivity and convolutional neural networks for automatic classification of EEG data. (May 2022)

• Record Type: Journal Article
• Authors: Alves, C.; Wissel, L.; Capetian, P.; Thielemann, C.
• Is Part Of: Clinical neurophysiology. Volume 137(2022)
• Page Start: e47
• Publication Date: 2022-05
• Subjects: Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2022.01.086
• Languages: English
• ISSNs: 1388-2457

Abstract

Abstract : It is well known that machine learning algorithms can support the classification of EEG data by using deep learning networks, especially convolutional neural networks [1-3]. In this paper, we propose a new method for automatic classification of movement disorders based on an open-access EEG dataset published by Anjun et al. in 2020 [4]. The dataset includes a total of 41 Parkinson's disease (PD) patients and 41 control subjects. Control participants were demographically matched for age and sex with PD patients and did not differ in any measurements of education or premorbid intelligence. Additionally, EEG recordings from OFF medication sessions for 27 PD patients were recorded in the practically defined OFF levodopa period (12 h after the last dose of dopaminergic medication). Resting state EEG recordings were gathered under both eyes-open and eyes-closed at a sampling rate of 500 Hz on a 64-channel system and eye blinks were removed [5]. In the first step of our machine learning process, a connectivity matrix is created using three different approaches based on Granger causality, Pearson correlation and Spearman correlation. In a second step, these matrices are fed into a convolutional neural network that is tuned using random search, hyperband and Bayesian optimization. We will show that this approach can provide very accurate classification results for the given EEG data sets. Comparison with the traditional method considering raw EEG data shows that our method is more accurate, highlighting the importance of network topology in describing brain data [6]. References: [1] J. C. Vasquez-Correa, T. Arias-Vergara, C. D. Rios-Urrego, M. Schuster, J. Rusz, J. R. Orozco-Arroyave, and E. N ̈oth, Convolutional neural networks and a transfer learning strategy to classify parkinson's disease from speech in three different languages, in Iberoamerican Congress on Pattern Recognition(Springer, 2019) pp. 697–706. [2] T. D. Pham, A comprehensive study on classification of covid-19 on computed tomography with pretrained convolutional neural networks, Scientific reports10, 1 (2020). [3] O. Akbilgic, R. Kamaleswaran, A. Mohammed, G. W.Ross, K. Masaki, H. Petrovitch, C. M. Tanner, R. L. Davis, and S. M. Goldman, Electrocardiographic changes predate parkinson's disease onset, Scientific reports 10, 1 (2020). [4] Narayanan lab, Data set https://narayanan.lab.uiowa.edu/article/datasets [5] M.F. Anjum, S. Dasgupta, R. Mudumbai, A. Singh, J. F. Cavanagh, N. S. Narayanan, Linear predictive coding distinguishes spectral EEG features of Parkinson's disease, Parkinsonism & Related Disorders, 79, 2020, pp. 79-85. [6] C. L. Alves, A. M. Pineda, K. Roster, C. Thielemann, and F. A. Rodrigues, EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia, arXiv preprintarXiv:2110.06140 (2021).