Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

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.

Review of EEG-Based Biometrics in 5G-IoT: Current Trends and Future Prospects

“…EEG is a valuable tool used in diagnosing a range of diseases. For instance, seizures and epilepsy can be identified by analyzing brain activity patterns in EEG recordings [17]. Other neurological diseases, particularly those leading to dementia, can also be detected through EEGs.…”

“…Technology can also function as an instrument for identifying individuals with certain neurological or psychological conditions. For example, EEG, a component of BCI, can be employed in the diagnosis of epilepsy and other neurological disorders, including Alzheimer's disease and autism spectrum disorder [14][15][16][17]. Additionally, it can aid in the diagnosis of mood disorders like depression and anxiety [18].…”

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

“…Seo et al [ 255 ] proposed a new method based on the dynamic mode decomposition (DMD) to find a significant contrast between the ictal and the interictal patterns in the epileptic EEG data. The DMD-extracted features clearly capture the phase transition of a specific frequency between the channels corresponding with the ictal state and the channel corresponding with the interictal state, such as direct current shift and high-frequency oscillation (HFO).…”

A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns

“…Yuan and Zhou developed an approach to detect seizures employing log-Euclidean Gaussian kernel-based sparse representation (SR) in long-term EEG recordings [ 15 ]. Seo and Tsuda proposed a new method that was based on the dynamic mode decomposition (DMD) in order to find a distinctive contrast between the ictal and inter-ictal patterns [ 16 ]. When compared with previous time-domain analysis, frequency-domain analysis, and time-frequency analysis [ 17 , 18 ], the entropy based nonlinear analysis method has been applied to characterize brain activities to research the pathophysiological mechanisms underlying the neurological conditions [ 19 , 20 ].…”