Notice of Violation of IEEE Publication Principles: Online Epilepsy Diagnosis Based on Analysis of EEG Signals by Hybrid Adaptive Filtering and Higher-order Crossings

Abstract

Notice of Violation of IEEE Publication Principles<br><br>"Online Epilepsy Diagnosis Based on Analysis of EEG Signals by Hybrid Adaptive Filtering and Higher-order Crossings"<br>by Saadat Nasehi, Hossein Pourghassem, and Afshine Etesami<br>in the 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.<br><br>This paper contains portions of text from the paper(s) cited below. A reference is included, but due to the absence of quotation marks or offset text, copied material is not clearly credited or specifically identified.<br><br>"Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis"<br>by Panagiotis C. Petrantonakis and Leontios J. Hadjileontiadis<br> in IEEE Transactions on Affective Computing, Vol 1, No 2, July-December 2010, pp. 81-97<br><br> <br/> This paper presents a novel epilepsy diagnosis algorithm based on analysis of EEG signals by hybrid adaptive filtering (HAF) and higher-order crossings (HOC). In this algorithm, HAF is developed to isolate the seizure and non-seizure EEG characteristics and facilitating the task of the feature vector extraction. Furthermore, HOC analysis is employed to select the effective feature from the HAF-filtered signals. The extracted features by HAF-HOC scheme can create maximum distinction between two classes. Finally, Quadratic Discriminant Analysis (QDA) and Mahalanobis Distance (MD) is used for classification and recognition of seizures through EEG signals. The proposed algorithm is implemented on CHB dataset and its performance has been evaluated for three measures. The results indicate that the algorithm can recognize the seizure with smaller delay and higher good detection rate that are important factors from a clinical viewpoint.