Linear and non-linear methods for automatic seizure detection in scalp electro-encephalogram recordings

The electro-encephalogram is a time-varying signal that measures electrical activity in the brain. A conceptually intuitive non-linear technique, multi-dimensional probability evolution (MDPE), is introduced. It is based on the time evolution of the probability density function within a multi-dimens...

Full description

Saved in:
Bibliographic Details
Published in:Medical & biological engineering & computing 2002-07, Vol.40 (4), p.447-461
Main Authors: MCSHARRY, P. E, HE, T, SMITH, L. A, TARASSENKO, L
Format: Article
Language:English
Subjects:
Biological and medical sciences
Electroencephalography
Epilepsy - diagnosis
Humans
Linear Models
Medical sciences
Nonlinear Dynamics
Scalp
Signal Processing, Computer-Assisted
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The electro-encephalogram is a time-varying signal that measures electrical activity in the brain. A conceptually intuitive non-linear technique, multi-dimensional probability evolution (MDPE), is introduced. It is based on the time evolution of the probability density function within a multi-dimensional state space. A synthetic recording is employed to illustrate why MDPE is capable of detecting changes in the underlying dynamics that are invisible to linear statistics. If a non-linear statistic cannot outperform a simple linear statistic such as variance, then there is no reason to advocate its use. Both variance and MDPE were able to detect the seizure in each of the ten scalp EEG recordings investigated. Although MDPE produced fewer false positives, there is no firm evidence to suggest that MDPE, or any other non-linear statistic considered, outperforms variance-based methods at identifying seizures.
ISSN:0140-0118
1741-0444
DOI:10.1007/BF02345078