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Methods for alignment of multi-class signal sets

Wahlberg, Patrik LU and Salomonsson, G (2003) In Signal Processing 83(5). p.983-1000
Abstract
The paper treats jitter estimation for alignment of a set of signals which contains several unknown classes of waveforms. The motivating application is epileptic EEG spikes. where alignment prior to clustering and averaging is desired. The assumption that the signal waveforms are unknown precludes the use of classical techniques, notably matched filtering. Instead we treat two other classes of methods. In the first class the jitter of each signal is estimated with aid of the whole data set, using the Rayleigh quotient of the sample correlation matrix. The main idea of the paper is the suggestion of two such methods, consisting respectively of mean value computation and maximization of the Rayleigh quotient as a function of translation of a... (More)
The paper treats jitter estimation for alignment of a set of signals which contains several unknown classes of waveforms. The motivating application is epileptic EEG spikes. where alignment prior to clustering and averaging is desired. The assumption that the signal waveforms are unknown precludes the use of classical techniques, notably matched filtering. Instead we treat two other classes of methods. In the first class the jitter of each signal is estimated with aid of the whole data set, using the Rayleigh quotient of the sample correlation matrix. The main idea of the paper is the suggestion of two such methods, consisting respectively of mean value computation and maximization of the Rayleigh quotient as a function of translation of a given signal. In the second class of methods each signal is processed individually, and one such method is estimation of the jitter of a signal by its centre of gravity. By means of deduction, simulations and evaluation on real life epileptic EEG signals, we show that the first class of methods is preferable to the second. Simulations also show that the method of maximization of the Rayleigh quotient seems to be a generally good method, which gives small estimation error and is applicable in a wide range of circumstances. For seven investigated sets of real life EEG data, the maximization algorithm turned out to give the best results, and improved alignment in the majority of signal clusters. (C) 2003 Elsevier Science B.V. All rights reserved. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
multi-class signal sets, clustering, alignment, Rayleigh, epileptic EEG spikes, quotient, time jitter
in
Signal Processing
volume
83
issue
5
pages
983 - 1000
publisher
Elsevier
external identifiers
  • wos:000182104200008
  • scopus:0037401287
ISSN
0165-1684
DOI
10.1016/S0165-1684(02)00501-7
language
English
LU publication?
yes
id
709c0551-b7d6-4202-8e5c-4c628f9a6742 (old id 907677)
date added to LUP
2008-01-16 11:27:52
date last changed
2017-01-01 07:17:55
@article{709c0551-b7d6-4202-8e5c-4c628f9a6742,
  abstract     = {The paper treats jitter estimation for alignment of a set of signals which contains several unknown classes of waveforms. The motivating application is epileptic EEG spikes. where alignment prior to clustering and averaging is desired. The assumption that the signal waveforms are unknown precludes the use of classical techniques, notably matched filtering. Instead we treat two other classes of methods. In the first class the jitter of each signal is estimated with aid of the whole data set, using the Rayleigh quotient of the sample correlation matrix. The main idea of the paper is the suggestion of two such methods, consisting respectively of mean value computation and maximization of the Rayleigh quotient as a function of translation of a given signal. In the second class of methods each signal is processed individually, and one such method is estimation of the jitter of a signal by its centre of gravity. By means of deduction, simulations and evaluation on real life epileptic EEG signals, we show that the first class of methods is preferable to the second. Simulations also show that the method of maximization of the Rayleigh quotient seems to be a generally good method, which gives small estimation error and is applicable in a wide range of circumstances. For seven investigated sets of real life EEG data, the maximization algorithm turned out to give the best results, and improved alignment in the majority of signal clusters. (C) 2003 Elsevier Science B.V. All rights reserved.},
  author       = {Wahlberg, Patrik and Salomonsson, G},
  issn         = {0165-1684},
  keyword      = {multi-class signal sets,clustering,alignment,Rayleigh,epileptic EEG spikes,quotient,time jitter},
  language     = {eng},
  number       = {5},
  pages        = {983--1000},
  publisher    = {Elsevier},
  series       = {Signal Processing},
  title        = {Methods for alignment of multi-class signal sets},
  url          = {http://dx.doi.org/10.1016/S0165-1684(02)00501-7},
  volume       = {83},
  year         = {2003},
}