Epileptic-Seizure onset detection using PARAFAC model with Cross-Wavelet Transformation on Multi-Channel EEG

Background: Finding components from multi-channel EEG signal for localizing and detection of onset of seizure is a new approach in biomedical signal analysis. Tensor-based approaches are utilized to fit the components into multi-dimensional array in recent works. Method: We initially decompose EEG signals into Beta band using Discrete Wavelet Transform. We compare patient templates with normal template for cross-wavelet analysis to obtain Wavelet cross spectrum and Wavelet cross coherence coefficients. Next we apply PARAFAC (Parallel Factorization) modeling, a three-way tensor-based representation in channel, frequency and time-points dimensions on features. Finally, we utilize ensemble classifier for detecting seizure-free, onset and seizure classes. Results: The clinical dataset for this work comprises of 5 normal subjects and 6 epileptiform patients. The classification performances of Wavelet cross spectrum features on PARAFAC model for Seizure detection using Ensemble Bagged-Trees classifier obtains highest 82.21% accuracy, while for Wavelet Coherence features it provides 84.76% accuracy. The results have been compared with well-known Fine Gaussian SVM, Weighted KNN and Ensemble Subspace KNN classifiers. Conclusions: The aim is to analyze data over three dimensions i.e., time, frequency and space (channels). This EEG based analysis is effective as an automatic method for detection of seizure before its actual manifestation.

Data-Driven and Low-Sparsity False Data Injection Attacks in Smart Grid

Recent researches on data-driven and low-sparsity data injection attacks have been presented, respectively. To combine the two main goals (data-driven and low-sparsity) of research, this paper presents a data-driven and low-sparsity false data injection attack strategy. The proposed attacking strategy (EID: Eliminate-Infer-Determine) is divided into three stages. In the first step, the intercepted data is preprocessed by sparse optimization techniques to eliminate the outliers. The recovered data is then exploited to learn about the system matrix based on the parallel factorization algorithm in the second step. In the third step, the approximated system matrix is applied for the design of sparse attack vector based on the convex optimization. The simulation results show that the EID attack strategy achieves a better performance than the improved ICA-based attack strategy in constructing perfect sparse attack vectors. What is more, data-driven implementation of the proposed strategy is also presented which ensures attack performance even without the prior information of the system.