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

• 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
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/ab5247

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.