Multi-channel EEG epileptic spike detection by a new method of tensor decomposition

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 wh...

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Bibliographic Details
Published in:Journal of neural engineering 2020-01, Vol.17 (1), p.016023-016023
Main Authors: Thanh, Le Trung, Dao, Nguyen Thi Anh, Dung, Nguyen Viet, Trung, Nguyen Linh, Abed-Meraim, Karim
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Adolescent
Adult
Aged
Brain - physiopathology
Child
Child, Preschool
electroencephalography (EEG)
Electroencephalography - methods
Engineering Sciences
Epilepsy - diagnosis
Epilepsy - physiopathology
epileptic spikes
feature extraction
feature selection
Female
Humans
Male
multilinear low-rank approximation
nonegative Tucker decomposition
Signal and Image processing
Signal Processing, Computer-Assisted
tensor decomposition
Young Adult
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Summary: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.
ISSN:1741-2560
1741-2552
1741-2552
DOI:10.1088/1741-2552/ab5247