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.2019 Aug 29;34(3):180-190.
doi: 10.7555/JBR.33.20190009.

Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD

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Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD

Luis Alfredo Moctezuma et al. J Biomed Res..

Abstract

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.

Keywords: detrended fluctuation analysis; electroencephalograms; empirical mode decomposition; energy distribution; epileptic seizure; fractal dimension.

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Figures

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1. An illustrative example using the method for feature extraction from the monopolar channel AF4.
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2. Average accuracy of epileptic seizure classification for 24 subjects by SVM using the complete signal and six-second segments (with a sample rate of 256 Hz and 128 Hz).
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3. Average accuracy of epileptic seizure classification for 24 subjects using SVM during channel reduction.
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4. Classification of epileptic seizures of subject 1 using the ML-based model of subject 21, and the epileptic seizures of subject 21 using the ML-based model of subject 1.
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5. Accuracy obtained with data from subject 1 (S1), subject 21 (S21), and data from both subjects using SVM, during channel reduction.
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6. Accuracy obtained in the classification of epileptic seizures and seizure-free periods of subject 1 and after adding 1, 2, and 3 instances of subject 21 using the SVM classifier.
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7. Illustration of the instance-reduction process, based on removing the non-dominant instances in k-means clusters: those indicated by the red line will be removed (S means epileptic seizure and S-F means seizure-free).
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8. Selection of the optimal k for the k-means algorithm using the elbow method.
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9. ​Accuracy obtained using all the subjects without and with instance reduction.
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10. Accuracy of epileptic seizure classification using SVM with the model created for ​N-i ​subjects and evaluation of the model with subject ​i.
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