Analysis of wavelet filtered Tonic-Clonic electroencephalogram recordings.

O. A. ROSSO; A. FIGLIOLA; J. CRESO; E. SERRANO

MEDICAL AND BIOLOGICAL ENGINEERING AND COMPUTING

The Institution of Electrical Engineers (The IEE)

Año: 2004 vol. 42 p. 516 - 523

0140-0118

EEG signals obtained during Tonic-Clonic epileptic seizures can be severely contaminated by muscle and physiological noise. Heavily contaminated EEG signals are hard to analyze quantitatively and also are usually rejected for visual inspection by the physicians resulting in a considerable loss of collected information. In this regard, we present here a method for filtering those frequencies greatly associated with muscle activity using a wavelet transform. One of the advantages of this method over traditional filtering is that wavelet filtering of some frequency bands does not modify the pattern of the remaining ones. In consequence, the dynamics associated with them do not change either. As an example, after generating a ``noise free" signal by removing the muscle artifacts by wavelets, we performed a dynamical analysis using nonlinear dynamics metric tools. The characteristic parameters evaluated (correlation dimension and largest Lyapunov exponent) are compatible with those obtained in previous works.