An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting

Background: Epileptic seizure detection is traditionally performed by visual observation of Electroencephalogram (EEG) signals. Owing to its onerous and time-consuming nature, seizure detection based on visual inspection hinders epilepsy diagnosis, monitoring, and large-scale data analysis in epilepsy research. So, there is a dire need of an automatic seizure detection scheme. Method: An automated scheme for epileptic seizure identification is developed in this study. Here we utilize a signal processing technique, namely-complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for epileptic seizure identification. First, we decompose segments of EEG signals into intrinsic mode functions by CEEMDAN. The mode functions are then modeled by normal inverse Gaussian (NIG) pdf parameters. In this work, NIG modeling is employed in conjunction with CEEMDAN for epileptic seizure detection for the first time. The efficacy of the NIG parameters in the CEEMDAN domain is demonstrated by intuitive, graphical, and statistical analyses. Adaptive Boosting, an eminent ensemble learning based classification model, is implemented to perform classification. Results: Experimental outcomes suggest that the algorithmic performance of the proposed scheme is promising in all the cases of clinical significance. Comparative evaluation of algorithmic performance with the state-of-the-art schemes manifest that the seizure detection scheme proposed herein outperforms competing algorithms in terms of accuracy, sensitivity, specificity, and Cohen’s Kappa coefficient. Conclusions: Upon its implementation in clinical practice, the proposed seizure detection scheme will eliminate the onus of medical professionals and expedite epilepsy research and diagnosis.

Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise

Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting

In this paper, a comprehensive method using symmetric normal inverse Gaussian (NIG) parameters of the sub-bands of EEG signals calculated in the dual-tree complex wavelet transformation domain is proposed for classifying EEG data. The suitability of the NIG probability distribution function is illustrated using statistical measures. A support vector machine is employed as the classifier of the EEG signals, wherein the NIG parameters are used as features. The performance of the proposed method is studied using a publicly available benchmark EEG database for various classification cases that include healthy, inter-ictal (seizure-free interval) and ictal (seizure), non-seizure and seizure, healthy and seizure, and inter-ictal and ictal, and compared with that of several recent methods. It is shown that in almost all the cases, the proposed method can provide 100 % accuracy with 100 % sensitivity and 100 % specificity while being faster as compared to the time–frequency analysis-based and EMD techniques.

Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection

In recent years, many works on digital image watermarking have been proposed all aiming at protection of the copyright of an image document or authentication of data. This paper proposes a novel watermark decoder in the contourlet domain . It is known that the contourlet coefficients of an image are highly non-Gaussian and a proper distribution to model the statistics of the contourlet coefficients is a heavy-tailed PDF. It has been shown in the literature that the normal inverse Gaussian (NIG) distribution can suitably fit the empirical distribution. In view of this, statistical methods for watermark extraction are proposed by exploiting the NIG as a prior for the contourlet coefficients of images. The proposed watermark extraction approach is developed using the maximum likelihood method based on the NIG distribution. Closed-form expressions are obtained for extracting the watermark bits in both clean and noisy environments. Experiments are performed to verify the robustness of the proposed decoder. The results show that the proposed decoder is superior to other decoders in terms of providing a lower bit error rate. It is also shown that the proposed decoder is highly robust against various kinds of attacks such as noise, rotation, cropping, filtering, and compression.

Multiplicative Watermark Decoder in Contourlet Domain Using the Normal Inverse Gaussian Distribution

In this study, an adaptive image denoising method is proposed based on the symmetric normal inverse Gaussian (SNIG) model and the non-subsampled contourlet transform (NSCT). In the framework of Bayesian maximum a posteriori estimation, the problem of denoising is reduced to a procedure of thresholding. A novel strategy is then proposed to determine the threshold that is not only adaptive to different directions and scales, but also able to take into considerations the scale-to-scale difference in the contribution of the NSCT coefficients to the noise. The experimental results in different kinds of sample images show that the authors' method can not only result in higher peak-signal-to-noise ratio values, but also have better visual effects in reduced processing artefacts and preserved edges.

J. Wang

Papers

Image denoising based on the symmetric normal inverse Gaussian model and non-subsampled contourlet transform