Independent component analysis and high-order statistics for automatic artifact rejection

Nadia Mammone, Francesco Carlo Morabito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One of the aims of biomedical signal processing is to extract some features from the data in order to make diagnosis and to understand the biological phenomena but, often, a preprocessing step is essential because some unwelcome signals, the artifacts, are superimposed to the useful signals we want to analyse. Automatic artifact detection is a key topic, because we aim to automatically analyse and extract features from the data. In literature, Independent Component Analysis (ICA) has been exploited for artifact isolation and the joint use of some high order statistics, kurtosis and Shannon's entropy has been exploited to automatically detect the artifacts. In this paper we propose the joint use of kurtosis and Renyi's entropy as a new tool for automatic detection and we show that it outperforms the other tool thanks to the features of the Renyi's entropy.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages2447-2452
Number of pages6
Volume4
DOIs
Publication statusPublished - 2005
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: Jul 31 2005Aug 4 2005

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2005
CountryCanada
CityMontreal, QC
Period7/31/058/4/05

ASJC Scopus subject areas

  • Software

Fingerprint Dive into the research topics of 'Independent component analysis and high-order statistics for automatic artifact rejection'. Together they form a unique fingerprint.

  • Cite this

    Mammone, N., & Morabito, F. C. (2005). Independent component analysis and high-order statistics for automatic artifact rejection. In Proceedings of the International Joint Conference on Neural Networks (Vol. 4, pp. 2447-2452). [1556286] https://doi.org/10.1109/IJCNN.2005.1556286