Key result
Multivariate empirical mode decomposition of multichannel EEG recordings successfully distinguished between ictal and seizure-free signals based on mean frequency estimates.
The MEMD algorithm combined with the Hilbert-Huang transform can effectively distinguish between ictal and seizure-free intracranial EEG recordings.
May support automated ictal EEG analysis in research; leaves open clinical diagnostic utility pending validation.
We present a method for the analysis of electroencephalogram (EEG) signals which has the potential to distinguish between ictal and seizure-free intracranial EEG recordings. This is achieved by analyzing common frequency components in multichannel EEG recordings, using the multivariate empirical mode decomposition (MEMD) algorithm. The mean frequency of the signal is calculated by applying the Hilbert-Huang transform on the resulting intrinsic mode functions (IMFs). It has been shown that the mean frequency estimates for the ictal and seizure-free EEG recordings are statistically different, and hence, can serve as a test statistic to distinguish between the two classes of signals. Simulation results on real world EEG signals support the analysis and demonstrate the potential of the proposed scheme.
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Rehman et al. (2010) studied Seizures. Multivariate empirical mode decomposition (MEMD) algorithm was evaluated on Distinguishing between ictal and seizure-free intracranial EEG recordings. Multivariate empirical mode decomposition of multichannel EEG recordings successfully distinguished between ictal and seizure-free signals based on mean frequency estimates.
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