Scattering transform-based features for the automatic seizure detection

Publication date: Available online 23 November 2019Source: Biocybernetics and Biomedical EngineeringAuthor(s): Yun Jiang, Wanzhong Chen, Yang YouAbstractDeveloping the automatic detection system is of great clinical significance for assisting neurologists to detect epilepsy using electroencephalogram (EEG) signals. In this research, we explore the ability of a newly-developed algorithm named scattering transform in seizure detection. The preprocessed signal is initially decomposed into scattering coefficients with various orders and scales employing scattering transform. Fuzzy entropy (FuzzyEn) and Log energy entropy (LogEn) of the sub-band coefficients are obtained to characterize the epileptic seizure signals. Then the joint features are fed into five classifiers including support vector machine (SVM), least squares-support vector machine (LS-SVM), genetic algorithm-support vector machine (GA-SVM), extreme learning machine (ELM) and probabilistic neural network (PNN) for the verification of the effectiveness of the proposed scheme. Finally, we not only compare the classification results and the time efficiency derived from different classifiers, but also explore the discrimination performance of the proposed methodology based on ten different classification tasks with great clinical significance. The prominent classification accuracy (ACC) of 99.87 %, 99.59 %, 99.58 %, 99.56 % and 99.80 % are achieved using the above five classifiers respectively. The average ACC and Matthe...
Source: Biocybernetics and Biomedical Engineering - Category: Biomedical Engineering Source Type: research