Automatic detection of ictal activity in EEG using synchronization and chaos-based attributes

Abstract

Automatic seizure onset detectors (SODs) have been proposed to alert epileptic patients when a seizure is about to happen and in turn improve their quality of life. Yet, the detectors proposed in literature are complex and difficult to implement in real-time as they utilize large feature sets with redundant and irrelevant features. Hence, the aim of this work is to propose a simple and lightweight SOD that exploits two characteristics that reflect the neuronal behavior during a seizure. Namely, the synchronization between EEG channels and the chaoticity of the EEG; synchronization was measured by the condition number while the recurrence period density entropy estimated the chaoticity of an EEG signal. A support vector machine was trained and tested on 10 patients from a scalp EEG dataset and was able to detect the considered seizures with a sensitivity of 100% and a false positives rate of 0.5 per hour. The results indicate that synchronization and chaos attributes can reflect the manifestation of seizures in EEG data and can be used to develop SODs. This work emphasizes that even a single relevant feature can produce an SOD with comparable performance to SODs that use many features. Graphical Abstract

Approaching Electroencephalographic Pathological Spikes in Terms of Solitons

“…Using brain network analysis, data-driven approaches and machine learning [25], various types of nonlinear dynamic behavior have been found in the wave series extracted from EEG data sets [23]. Nonlinear dynamics in the central nervous system consist of phase transitions, branching processes, metastability, limit cycles, self-oscillations, non-stationarity, neuronal avalanches, chaotic behavior and collective phenomena with the emergence of order [26,27]. Increasing evidence suggests that cortical neuronal networks operate near a critical state where balanced activity patterns support optimal information processing, scale-free correlations and the emergence of adaptive collective behavior [26].…”