Choosing Strategies to Deal with Artifactual EEG Data in Children with Cognitive Impairment
Por:
Tost A, Migliorelli C, Bachiller A, Medina-Rivera IF, Romero-Lafuente S, Garcia-Cazorla A and Mañanas MA
Publicada:
1 ago 2021
Ahead of Print:
11 ago 2021
Resumen:
Rett syndrome is a disease that involves acute cognitive impairment and, consequently, a complex and varied symptomatology. This study evaluates the EEG signals of twenty-nine patients and classify them according to the level of movement artifact. The main goal is to achieve an artifact rejection strategy that performs well in all signals, regardless of the artifact level. Two different methods have been studied: one based on the data distribution and the other based on the energy function, with entropy as its main component. The method based on the data distribution shows poor performance with signals containing high amplitude outliers. On the contrary, the method based on the energy function is more robust to outliers. As it does not depend on the data distribution, it is not affected by artifactual events. A double rejection strategy has been chosen, first on a motion signal (accelerometer or EEG low-pass filtered between 1 and 10 Hz) and then on the EEG signal. The results showed a higher performance when working combining both artifact rejection methods. The energy-based method, to isolate motion artifacts, and the data-distribution-based method, to eliminate the remaining lower amplitude artifacts were used. In conclusion, a new method that proves to be robust for all types of signals is designed.
Filiaciones:
Tost A:
Biomedical Engineering Research Centre (CREB), Department of Automatic Control (ESAII), Universitat Politècnica de Catalunya (UPC), 08028 Barcelona, Spain
Migliorelli C:
Biomedical Engineering Research Centre (CREB), Department of Automatic Control (ESAII), Universitat Politècnica de Catalunya (UPC), 08028 Barcelona, Spain
CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain
Bachiller A:
Biomedical Engineering Research Centre (CREB), Department of Automatic Control (ESAII), Universitat Politècnica de Catalunya (UPC), 08028 Barcelona, Spain
CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain
Medina-Rivera IF:
Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain
Romero-Lafuente S:
Biomedical Engineering Research Centre (CREB), Department of Automatic Control (ESAII), Universitat Politècnica de Catalunya (UPC), 08028 Barcelona, Spain
CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain
Garcia-Cazorla A:
Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain
Neurometabolic Unit and Synaptic Metabolism Lab, Neurology Department, Institut Pediàtric de Recerca, Hospital Sant Joan de Déu, metabERN and CIBERER-ISCIII, 08950 Barcelona, Spain
Mañanas MA:
Biomedical Engineering Research Centre (CREB), Department of Automatic Control (ESAII), Universitat Politècnica de Catalunya (UPC), 08028 Barcelona, Spain
CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain
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