Seizure activity classification based on bimodal Gaussian modeling of the gamma and theta band IMFs of EEG signals. (February 2021)

• Record Type: Journal Article
• Authors: Chowdhury, Tanima Tasmin; Fattah, Shaikh Anowarul; Shahnaz, Celia
• Is Part Of: Biomedical signal processing and control. Volume 64(2021)
• Publication Date: 2021-02
• Subjects: 00-01 -- 99-00; Bimodal Gaussian distribution -- Empirical mode decomposition -- Dominant IMFs -- Epileptic seizure -- Statistical model; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102273
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: In this manuscript, EEG signals of seizure and non-seizure activities have been discussed and classified into five groups on the basis of seizure onset, seizure action and brain signal recording location. EEG signals consisting of gamma-band (40–80 Hz) and theta-band (4–8 Hz) oscillations have been captured for performing empirical mode decomposition (EMD). Dominant intrinsic mode functions (IMFs) have been selected from the consequences of EMD and a statistical model is employed upon the IMFs to summarize the information on those. Bimodal Gaussian statistical model has been found most effective to prepare feature set taking the modeling parameters of its probability density function (PDF). Plotting together bimodal Gaussian PDF and empirical PDF for pictorial scrutiny; cumulative distribution function (CDF) in probability–probability (p–p) plot and goodness of fit K–S test result justified the effectiveness of proposed bimodal Gaussian statistical model. Hence, aforementioned statistical modeling parameters have been sent to numerous classifiers and rationalization of goodness of features has been shown through inter-class separability and intra-class compactness parameters. Extensive varieties of simulations are performed using a well-established dataset. The suggested strategy reveals the capability of making higher values of sensitivity, specificity and accuracy compared to that made by some cutting-edge methods utilizing the same EEG dataset. Highlights: Significant appearances of high frequency oscillations (HFO) at seizure onset Robust connection of electrical actions between high and low-frequency bands at brain Developing statistical model of IMF coefficients to form more functional features set Shape of entire dataset included in feature representing each class more consistently