eprintid: 4004
rev_number: 15
eprint_status: archive
userid: 17
dir: disk0/00/00/40/04
datestamp: 2020-07-10 05:36:01
lastmod: 2020-07-10 05:41:17
status_changed: 2020-07-10 05:41:17
type: article
succeeds: 3695
metadata_visibility: show
creators_name: Le, Trung Thanh
creators_name: Nguyen Thi Anh, Dao
creators_name: Nguyen, Viet Dung
creators_name: Nguyen, Linh Trung
creators_name: Karim, Abed-Meraim
creators_id: letrungthanhtbt@gmail.com
creators_id: daonta81@gmail.com
creators_id: nvdung@vnu.edu.vn
creators_id: linhtrung@vnu.edu.vn
creators_id: karim.abed-meraim@univ-orleans.fr
title: Multi-channel EEG epileptic spike detection by a new method of
tensor decomposition
ispublished: pub
subjects: ECE
subjects: IT
subjects: isi
divisions: avitech
divisions: fac_fet
abstract: Objective. Epilepsy is one of the most common brain disorders. For epilepsy diagnosis or treatment, the neurologist needs to observe epileptic spikes from electroencephalography (EEG) data. Since multi-channel EEG records can be naturally represented by multi-way tensors, it is of interest to see whether tensor decomposition is able to analyze EEG epileptic spikes. Approach. In this paper, we first proposed the problem of simultaneous multilinear low-rank approximation of tensors (SMLRAT) and proved that SMLRAT can obtain local optimum solutions by using two well-known tensor decomposition algorithms (HOSVD and Tucker-ALS). Second, we presented a new system for automatic epileptic spike detection based on SMLRAT. Main results. We propose to formulate the problem of feature extraction from a set of EEG segments, represented by tensors, as the SMLRAT problem. Efficient EEG features were obtained, based on estimating the ‘eigenspikes’ derived from nonnegative GSMLRAT. We compared the proposed tensor analysis method with other common tensor methods in analyzing EEG signal and compared the proposed feature extraction method with the state-of-the-art methods. Experimental results indicated that our proposed method is able to detect epileptic spikes with high accuracy. Significance. Our method, for the first time, makes a step forward for automatic detection EEG epileptic spikes based on tensor decomposition. The method can provide a practical solution to distinguish epileptic spikes from artifacts in real-life EEG datasets.
date: 2020-01-01
date_type: published
publisher: IOP
official_url: https://iopscience.iop.org/article/10.1088/1741-2552/ab5247/meta
contact_email: linhtrung@vnu.edu.vn
full_text_status: public
publication: Journal of Neural Engineering
refereed: TRUE
issn: 1741-2552
citation:   Le, Trung Thanh and Nguyen Thi Anh, Dao and Nguyen, Viet Dung and Nguyen, Linh Trung and Karim, Abed-Meraim  (2020) Multi-channel EEG epileptic spike detection by a new method of tensor decomposition.  Journal of Neural Engineering .    ISSN 1741-2552     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4004/6/Multi-channel%20EEG%20epileptic%20spike%20detection%20by%20a%20new%20method%20of%20tensor%20decomposition_2020.pdf