Journal
JOURNAL OF BIOMEDICAL RESEARCH
Volume 34, Issue 3, Pages 180-190Publisher
NANJING MEDICAL UNIV
DOI: 10.7555/JBR.33.20190009
Keywords
epileptic seizure; electroencephalograms; empirical mode decomposition; detrended fluctuation analysis; energy distribution; fractal dimension
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Funding
- Enabling TechnologiesNTNU under the project David versus Goliath: single-channel EEG unravels its power through adaptive signal analysis-FlexEEG
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We are here to present a new method for the classification of epileptic seizures from electroencephalogram (EEG) signals. It consists of applying empirical mode decomposition (EMD) to extract the most relevant intrinsic mode functions (IMFs) and subsequent computation of the Teager and instantaneous energy, Higuchi and Petrosian fractal dimension, and detrended fluctuation analysis (DFA) for each IMF. We validated the method using a public dataset of 24 subjects with EEG signals from 22 channels and showed that it is possible to classify the epileptic seizures, even with segments of six seconds and a smaller number of channels (e.g., an accuracy of 0.93 using five channels). We were able to create a general machine-learning-based model to detect epileptic seizures of new subjects using epileptic-seizure data from various subjects, after reducing the number of instances, based on the k-means algorithm.
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