Online Epileptic Seizure Prediction Using Wavelet-Based Bi-Phase Correlation of Electrical Signals Tomography

Abstract

Considerable efforts have been made in order to predict seizures. Among these methods, the ones that quantify synchronization between brain areas, are the most important methods. However, to date, a practically acceptable result has not been reported. In this paper, we use a synchronization measurement method that is derived according to the ability of bi-spectrum in determining the nonlinear properties of a system. In this method, first, temporal variation of the bi-spectrum of different channels of electro cardiography (ECoG) signals are obtained via an extended wavelet-based time-frequency analysis method; then, to compare different channels, the bi-phase correlation measure is introduced. Since, in this way, the temporal variation of the amount of nonlinear coupling between brain regions, which have not been considered yet, are taken into account, results are more reliable than the conventional phase-synchronization measures. It is shown that, for 21 patients of FSPEEG database, bi-phase correlation can discriminate the pre-ictal and ictal states, with very low false positive rates (FPRs) (average: 0.078/h) and high sensitivity (100%). However, the proposed seizure predictor still cannot significantly overcome the random predictor for all patients.

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

“…As such, MRI can be utilized to study and diagnose the diseases which affect the WM and GM differentially (Saba, 2015). An alternative to MRI imaging is computed tomography (CT) which uses X-rays with the aid of a computer to generate cross-sectional views and three-dimensional images of the brain (Vahabi et al, 2015). Although CT is a structural image and can detect the structural changes as MRI does, it cannot detect the details of the soft tissue.…”

Volumetric measurement of synchronization of the dust acoustic wave with an external modulation

“…[7] The adjustment of the selfexcited dust acoustic wave mode to match the externally applied modulation is a nonlinear process known as (phase) synchronization; a process that is ubiquitous in a variety of physical systems and is believed to play an important role in many self-organized phenomena. [8][9][10][11][12][13] In this paper, we present a measurement showing the volumetric nature of this synchronization process by applying a time-resolved Hilbert Transform [14,15] to high-speed imaging of the dust acoustic wave in an rf glow discharge plasma.…”

A General Approach for Quantifying Nonlinear Connectivity in the Nervous System Based on Phase Coupling

“…highorder spectra methods, 11,[30][31][32][33][34] are often used to study the nervous system in both animals 35,36 and humans. [37][38][39][40][41] Bicoherence has been proposed to quantify the second-order phase coupling (also called quadratic phase coupling, QPC) among three frequencies, i.e. a pair of frequencies {f 1 , f 2 } is coupled with a third frequency f 3 , which is the sum of these two frequencies (i.e.…”