IEEE J Biomed Health Inform - Extracting and Selecting Distinctive EEG Features for Efficient Epileptic Seizure Prediction.

Tópicos

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Resumo

This paper presents compact yet comprehensive feature representations for the electroencephalogram (EEG) signal to achieve efficient epileptic seizure prediction performance. The initial EEG feature vectors are formed by acquiring the dominant amplitude and frequency components on an epoch-by-epoch basis from the EEG signals. These extracted parameters can reveal the intrinsic EEG signal changes as well as the underlying stage transitions. To improve the efficacy of feature extraction, an elimination-based feature selection method has been applied on the initial feature vectors. This diminishes redundant and noisy points, providing each patient with a lower dimensional and independent final feature form. In this context, our study is distinguished from that of others currently prevailing. Usually, these latter approaches adopted feature extraction processes, which employed time-consuming high-dimensional parameter sets. Machine learning approaches that are considered as state of the art have been employed to build patient-specific binary classifiers that can divide the extracted feature parameters into preictal and interictal groups. Through out-of-sample evaluation on the intracranial EEG recordings provided by the publicly available Freiburg dataset, promising prediction performance has been attained. Specifically, we have achieved 98.8% sensitivity results on the 19 patients included in our experiment, where only one of 83 seizures across all patients was not predicted. To make this investigation more comprehensive, we have conducted extensive comparative studies with other recently published competing approaches, in which the advantages of our method are highlighted.

Resumo Limpo

paper present compact yet comprehens featur represent electroencephalogram eeg signal achiev effici epilept seizur predict perform initi eeg featur vector form acquir domin amplitud frequenc compon epochbyepoch basi eeg signal extract paramet can reveal intrins eeg signal chang well under stage transit improv efficaci featur extract eliminationbas featur select method appli initi featur vector diminish redund noisi point provid patient lower dimension independ final featur form context studi distinguish other current prevail usual latter approach adopt featur extract process employ timeconsum highdimension paramet set machin learn approach consid state art employ build patientspecif binari classifi can divid extract featur paramet preictal interict group outofsampl evalu intracrani eeg record provid public avail freiburg dataset promis predict perform attain specif achiev sensit result patient includ experi one seizur across patient predict make investig comprehens conduct extens compar studi recent publish compet approach advantag method highlight

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