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ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION

Citation Author(s):
Danyang Wang, Yuchun Fang, Yifan Li
Submitted by:
Yuchun Fang
Last updated:
16 February 2020 - 7:54pm
Document Type:
Poster
Document Year:
2020
Event:
Presenters Name:
Yifan Li

Abstract 

Abstract: 

In the treatment of epilepsy with intracranial electroencephalogram(iEEG), the recognition accuracy is low, and it is
difficult to find the correlation between channels because of the large amount of channel numbers and time series data. In
order to solve these problems, we propose a novel EEG feature prepresentation method for seizure detection based on the
Log Mel-Filterbank energy feature. We propose to adapt the Mel-Filterbank energy to EEG features with logrithm transform
in the frequency domain. Meanwhile, we also propose the sequential forward channel selection(SFCS) algorithm to
incorporate channel correlation and balance the computing consumption. Experiments show that our proposed method
have significant contributions in channel selection and feature representation to the problem of EEG signal analysis. The
average results of experiments judged by the mean area under the ROC curve (AUC) of the probability reach 99.13%.

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Poster for ICASSP2020 Paper4161

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