Title :
Prediction of brain electrical activity in epilepsy using a higher-dimensional prediction algorithm for discrete time cellular neural networks (DTCNN)
Author :
Gollas, F. ; Niederhöfer, C. ; Tetzlaff, R.
Author_Institution :
Inst. of Appl. Phys., Johann Wolfgang Goethe Univ., Frankfurt, Germany
Abstract :
Several investigations have shown that a higher-dimensional nonlinear signal analysis can contribute to the problem of detecting precursors for impending epileptic seizures in electroencephalographic recordings. In previous work we analyzed brain electrical activity using Volterra systems as stated in M. Schetzen (1980) and CNN in L. O. Chua (1998). The outline of this paper is to propose a higher-dimensional DTCNN prediction algorithm. First results are given for the long term recording of brain electrical activity.
Keywords :
discrete time systems; electroencephalography; prediction theory; signal detection; Volterra systems; brain electrical activity; discrete time cellular neural networks; electroencephalographic recordings; epilepsy; epileptic seizures; higher-dimensional prediction; nonlinear signal analysis; precursors detection; Brain; Cellular neural networks; Disk recording; Electrodes; Epilepsy; Intelligent networks; Nervous system; Physics; Prediction algorithms; Signal analysis;
Conference_Titel :
Circuits and Systems, 2004. ISCAS '04. Proceedings of the 2004 International Symposium on
Print_ISBN :
0-7803-8251-X
DOI :
10.1109/ISCAS.2004.1329909