Uses of regression in real time processing of neurophysiological signals

Author

Krieger, Don ; Onodipe, Seun ; Sclabassi, Robert J.

Author_Institution

Dept. of Neurological Surg., Childrens Hospital, Pittsburgh, PA, USA

Volume

4

fYear

1996

fDate

31 Oct-3 Nov 1996

Firstpage

1758

Abstract

A set of least squares regression techniques are described for use in real time processing of neural signals in the operating room. These techniques may be understood as equivalent to application of unrealizable ideal filters. The obtained effects include high pass filtering, notch filtering, and identification and removal of noise identified from a reference signal

Keywords

bioelectric potentials; high-pass filters; least mean squares methods; medical signal processing; neurophysiology; notch filters; surgery; clinical electrophysiology; electrophysiological recordings; high pass filtering; intraoperative monitoring; neural signals; noise identification; noise removal; notch filtering; operating room; real time processing; reference signal; unrealizable ideal filters; Biomedical monitoring; Digital filters; Displays; Filtering; Frequency estimation; Hospitals; Least squares methods; Low pass filters; Polynomials; Signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE

Conference_Location

Amsterdam

Print_ISBN

0-7803-3811-1

Type

conf

DOI

10.1109/IEMBS.1996.647648

Filename

647648