A method for the blind separation of sources for use as the first stage of a neonatal seizure detection system

Author

Faul, S. ; Marnane, L. ; Lightbody, G. ; Boylan, G. ; Connolly, S.

Author_Institution

Dept. of Electr. and Electron. Eng., Univ. Coll. Cork, Cork, Ireland

Volume

5

fYear

2005

fDate

23-23 March 2005

Abstract

A method is proposed for automatically choosing independent components (ICs) of interest from neonatal EEG data, with the aim of using them in further analysis to detect seizures. This procedure greatly reduces the amount of information which needs to be processed in the seizure detection system, and reduces the effect of noise and artefacts on its performance. The fast ICA algorithm is used to generate the ICs, and the complexity of each IC is examined to determine those of interest. The singular value fraction (SVF) measure is used to reduce the number of sources containing artefacts chosen. In the best case, the 12 channel EEG used in these tests is reduced to 2 or 3 sources of interest. In every case, at least 3 sources were removed that consisted of noise.

Keywords

blind source separation; electroencephalography; independent component analysis; medical signal processing; paediatrics; patient diagnosis; ICA; SVF; artifact effect reduction; blind source separation; epileptic seizures; neonatal EEG data; neonatal seizure detection system; noise effect reduction; singular value fraction measure; Data engineering; Educational institutions; Electroencephalography; Hospitals; Independent component analysis; Integrated circuit noise; Noise reduction; Pediatrics; Principal component analysis; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on

Conference_Location

Philadelphia, PA

ISSN

1520-6149

Print_ISBN

0-7803-8874-7

Type

conf

DOI

10.1109/ICASSP.2005.1416327

Filename

1416327