Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review

Abstract

Complexity science has provided new perspectives and opportunities for understanding a variety of complex natural or social phenomena, including brain dysfunctions like epilepsy. By delving into the complexity in electrophysiological signals and neuroimaging, new insights have emerged. These discoveries have revealed that complexity is a fundamental aspect of physiological processes. The inherent nonlinearity and non-stationarity of physiological processes limits the methods based on simpler underlying assumptions to point out the pathway to a more comprehensive understanding of their behavior and relation with certain diseases. The perspective of complexity may benefit both the research and clinical practice through providing novel data analytics tools devoted for the understanding of and the intervention about epilepsies. This review aims to provide a sketchy overview of the methods derived from different disciplines lucubrating to the complexity of bio-signals in the field of epilepsy monitoring. Although the complexity of bio-signals is still not fully understood, bundles of new insights have been already obtained. Despite the promising results about epileptic seizure detection and prediction through offline analysis, we are still lacking robust, tried-and-true real-time applications. Multidisciplinary collaborations and more high-quality data accessible to the whole community are needed for reproducible research and the development of such applications.

Applicable features of electroencephalogram for ADHD diagnosis

“…For example, Mohammadi et al recorded brain signals, while subjects performed an attention stimulating task, and Ahmadlou et al used an innovative approach combining wavelets, nonlinear analysis, and neural networks. Furthermore, nonlinear EEG analysis has been successfully applied to diagnose other diseases and disorders including autism (Hadoush et al 2019;Bhat et al 2014;Grossi et al 2019), epilepsy (da Silva 2008Mei et al 2018), bipolar disorder, and schizophrenia (Khaleghi et al 2015;Cutler and Neufeld 2019;Sadatnezhad et al 2011). The efficacy of these nonlinear approaches is not limited to EEG analysis, and the positive results of their applications can also be observed in the analysis of neuroimaging data such as functional magnetic resonance imaging (fMRI) (Kam et al 2017;Sokunbi et al 2014;Sidhu 2019).…”

Identification of Epileptic Seizures by Characterizing Instantaneous Energy Behavior of EEG

“…An alternative representation also used takes the time-frequency transformation of that same signal segment, then assumes the magnitude squares of the frequency samples in the transform domain as representing the energy in the frequency components. On the one hand, the energy in their studies is not time-sensitive for rapid changes in EEG, which are known to be nonlinear and nonstationary [20]. Thus, those energy-based features could not reflect the nonlinear and nonstationary character of energy in seizures effectively.…”

Low-Intensity Pulsed Ultrasound Stimulation Modulates the Nonlinear Dynamics of Local Field Potentials in Temporal Lobe Epilepsy

“…The complexity of EEG signals and functional status in the brain during epileptic seizures can be quantitatively evaluated by complexity (Mei et al, 2018). Previous studies have demonstrated that the complexity of EEG signals during epileptic seizures is lower than that in healthy humans (Wen-Chin et al, 2015; Noel et al, 2017).…”