Motivated by the dramatic rise of neurological disorders, we propose a SNN technique to model electroen-cephalography (EEG) data collected from people affected by Alzheimer's Disease (AD) and people diagnosed with mild cognitive impairment (MCI). An evolving spatio-temporal data machine (eSTDM), named the NeuCube architecture, is used to analyse changes of neural activity across different brain regions. The model developed allows for studying AD progression and for predicting whether a patient diagnosed with MCI is more likely to develop AD. © 2016 IEEE.
Longitudinal Study of Alzheimer's Disease Degeneration through EEG Data Analysis with a NeuCube Spiking Neural Network Model / Capecci, E.; Doborjeh, Z. G.; Mammone, N.; La Foresta, F.; Morabito, C.; Kasabov, N.. - 2016:(2016), pp. 7727356.1360-7727356.1366. (Intervento presentato al convegno International Joint Conference on Neural Networks, IJCNN 2016 tenutosi a Vancouver, Canada nel 24 July 2016 through 29 July 2016) [10.1109/IJCNN.2016.7727356].
Longitudinal Study of Alzheimer's Disease Degeneration through EEG Data Analysis with a NeuCube Spiking Neural Network Model
N. Mammone;F. La Foresta;C. Morabito;
2016-01-01
Abstract
Motivated by the dramatic rise of neurological disorders, we propose a SNN technique to model electroen-cephalography (EEG) data collected from people affected by Alzheimer's Disease (AD) and people diagnosed with mild cognitive impairment (MCI). An evolving spatio-temporal data machine (eSTDM), named the NeuCube architecture, is used to analyse changes of neural activity across different brain regions. The model developed allows for studying AD progression and for predicting whether a patient diagnosed with MCI is more likely to develop AD. © 2016 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
