Nonlinear forecasting measurement of magnetoencephalogram recordings from Alzheimer's disease patients.
The goal of this study was to analyze the magnetoencephalogram (MEG) background activity in patients with Alzheimer's disease (AD) using a nonlinear forecasting measure. It is a nonparametric method to quantify the predictability of time series. Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 15 patients with probable AD and 15 elderly control subjects. Stationary epochs of 5 seconds (848 points, sample frequency of 169.55 Hz) were selected. Our results showed that AD patients' MEGs were more predictable than controls' recordings. Additionally, an accuracy of 76.7% (80.0% sensitivity; 73.3% specificity) was reached using a receiver operating characteristic curve. These preliminary results suggest the usefulness of nonlinear forecasting to gain a better understanding of dynamical processes underlying the MEG recording.
Gomez
Carlos
C
Department of Signal Theory and Communications, University of Valladolid, Campus Miguel Delibes, 47011, Spain. cargom@tel.uva.es
Hornero
Roberto
R
Mediavilla
Angela
A
Fernandez
Alberto
A
Abasolo
Daniel
D
D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D005544:Forecasting; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D012372:ROC Curve