Use of multiscale entropy to facilitate artifact detection in electroencephalographic signals

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Mariani, S (Author) Other; Borges, AFT (Author) Other; Henriques, T (Author) Other; Goldberger, AL (Author) Other; Costa, MD (Author) Other

Conference proceedings International

Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Pages: 7869-7872

37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015

25 August 2015 through 29 August 2015

Indexing

Scopus - 4 Citations

Other information

Authenticus ID: P-00N-9Z6

DOI: 10.1109/embc.2015.7320216

Abstract (EN): Electroencephalographic (EEG) signals present a myriad of challenges to analysis, beginning with the detection of artifacts. Prior approaches to noise detection have utilized multiple techniques, including visual methods, independent component analysis and wavelets. However, no single method is broadly accepted, inviting alternative ways to address this problem. Here, we introduce a novel approach based on a statistical physics method, multiscale entropy (MSE) analysis, which quantifies the complexity of a signal. We postulate that noise corrupted EEG signals have lower information content, and, therefore, reduced complexity compared with their noise free counterparts. We test the new method on an open-access database of EEG signals with and without added artifacts due to electrode motion. © 2015 IEEE.

Language: English

Type (Professor's evaluation): Scientific