Abstract
In this paper, we propose a new method based on the dynamic mode decomposition (DMD) to find a distinctive contrast between the ictal and interictal patterns in epileptic electroencephalography (EEG) data. The features extracted from the method of DMD clearly capture the phase transition of a specific frequency among the channels corresponding to the ictal state and the channel corresponding to the interictal state, such as direct current shift (DC-shift or ictal slow shifts) and high-frequency oscillation (HFO). By performing classification tests with Electrocorticography (ECoG) recordings of one patient measured at different timings, it is shown that the captured phenomenon is the unique pattern that occurs in the ictal onset zone of the patient. We eventually explain how advantageously the DMD captures some specific characteristics to distinguish the ictal state and the interictal state. The method presented in this study allows simultaneous interpretation of changes in the channel correlation and particular information for activity related to an epileptic seizure so that it can be applied to identification and prediction of the ictal state and analysis of the mechanism on its dynamics.
Highlights
Epilepsy is a neurological condition in which patients suffer spontaneous seizures
We introduce a method of classification between the ictal and interictal states in dynamic mode decomposition on the signals
ECoG is an invasive method of measuring the electrical signals with electrodes that are implanted directly on the exposed surface of the brain while the normal EEG is a non-invasive method with electrodes that are placed on the surface of the scalp
Summary
Epilepsy is a neurological condition in which patients suffer spontaneous seizures. The seizure is caused by disturbances in the electrical activity of the brain. As proposed in [1], an epileptic seizure is a transient occurrence of signs and/or symptoms due to abnormal, excessive, and synchronous neuronal activity in the brain. Identifying the presence of epileptic activity, characterizing the spatio-temporal patterns of the corresponding brain activity, and predicting the occurrence of seizures are major challenges, and achieving this could significantly improve the quality of life for patients with epilepsy. Electroencephalogram (EEG) including electrocorticogram (ECoG) is the prime signal that has been widely used for the diagnosis of epilepsy (see, for example, [2]). It is an accurate tool for the Electroencephalogram (EEG)
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