Nonlinear prediction of brain electrical activity in epilepsy with a Volterra RLS algorithm

Author

Niederhofer, C. ; Suna, S. ; Tetzlaff, Ronald

Author_Institution

Inst. of Appl. Phys., Univ. of Frankfurt, Germany

Volume

3

fYear

2002

fDate

2002

Abstract

Approximately 0.3% of the world´s population is suffering from a focal epilepsy. In this paper the bioelectrical activity of the human brain in epilepsy will be analyzed with Volterra-systems (VS), whereas the kernels of different orders will be determined by using an RLS (recursive least squares) method of reduced computation complexity. The prediction gain for data segments obtained in presurgical evaluations will be given and criteria for a detection of distinct changes of the prediction coefficients will be proposed.

Keywords

Volterra series; biomedical measurement; computational complexity; electroencephalography; least squares approximations; medical signal processing; patient diagnosis; patient monitoring; recursive estimation; Volterra RLS algorithm; Volterra-system kernel order; computation complexity reduction; data segment prediction gain; focal epilepsy; human brain bioelectrical activity; nonlinear brain electrical activity prediction; prediction coefficient distinct change detection; presurgical evaluations; recursive least squares methods; world population epilepsy sufferer percentage; Bioelectric phenomena; Brain; Electrodes; Epilepsy; Filters; Humans; Kernel; Neural networks; Physics; Resonance light scattering;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2002. ISCAS 2002. IEEE International Symposium on

Print_ISBN

0-7803-7448-7

Type

conf

DOI

10.1109/ISCAS.2002.1010294

Filename

1010294