Visualization and Sonification of Long-Term Epilepsy Electroencephalogram Monitoring

Journal of Medical and Biological Engineering(2018)

Cited 10|Views16
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Long-term electroencephalogram (EEG) monitoring is effective for epilepsy diagnosis. However, it also takes a lot of time for clinicians to correctly interpret the long-time recordings. Real-time computer-based EEG monitoring and classification systems have attracted recently the attention of researchers to help clinicians locate online possible epileptic-form EEG signals. In this paper, we first present an accurate and fast EEG classification algorithm that can recognize three types of EEG signals: normal, spike, and seizure. 16-channel bipolar EEG recordings of epilepsy patients are preprocessed, segmented, and ensemble empirical mode decomposed (EEMD) into intrinsic mode functions (IMFs). Features are extracted and linear discriminant analysis (LDA) is applied to train two classifiers: one is for seizure and non-seizure discrimination, and the other is for normal and spike discrimination. In order to furthermore help the clinicians, the results of LDA are visualized and sonified. The changes of the discriminant in the LDA on continuous EEG segments are backtracked to each feature, and thus to each EEG channel. Accordingly, contours of the changes in EEG channels are depicted. At the same time, sinusoidal waves in 440 or 880 Hz are played when EEG segments are classified into spike or seizure respectively. In the experiment, EEG recordings of six subjects (two normal and four seizure patients) are examined. The experiment result shows that the accuracy of the proposed epileptic EEG classification algorithm is relatively high. In addition, the visualization and sonification algorithms of epileptic-form EEG may greatly help clinicians localize the focus of seizure and nurses take care of seizure patients, immediately.
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Key words
Electroencephalogram (EEG),Multi-channel,Epilepsy,Visualization,Sonification,Ensemble empirical mode decomposition (EEMD),Linear discriminant analysis (LDA)
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