Segmentation of quasiperiodic patterns in EEG recordings for analysis of post-traumatic paroxysmal activity in rat brains

• Published in: Pattern recognition and image analysis Vol. 27; no. 4; pp. 789 - 803
• Main Authors: Antsiperov, V. E., Obukhov, Yu. V., Komol’tsev, I. G., Gulyaeva, N. V.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.10.2017; Springer Nature B.V
• Subjects: Applied Problems; Brain damage; Cellular communication; Computer Science; Dynamic characteristics; Epilepsy; Fragmentation; Fragments; Head injuries; Image Processing and Computer Vision; Injury analysis; Neural networks; Pattern Recognition; Periodic variations; Plastic foam; Rats; Restoration; Segmentation; Seizures; Structural damage; Trauma; Traumatic brain injury; EEG rhythms; post-traumatic epilepsy; estimation of a local period; analysis of nonstationary signals; detection of quasiperiodic fragments; rat model of the traumatic brain injury
• ISSN: 1054-6618; 1555-6212
• DOI: 10.1134/S1054661817040022

The study of the consequences of traumatic brain injury represents an important problem in modern medicine. Brain trauma leads to the manifestation of neurological and psychiatric disturbances in most patients; severe traumatic brain injuries are accompanied by paroxysms and neurologic impairment, which in the long term lead to post-traumatic epilepsy. The mechanisms of post-traumatic epilepsy, in view of cellular and molecular plasticity, include rearrangement of neuronal networks. These processes induce spontaneous and synchronous discharges of numerous neurons, clinically manifested in the form of paroxysms. The posttraumatic period before the first unprovoked seizures is known as the latent phase of post-traumatic epileptogenesis, which can last for years in humans. Unfortunately, approaches to its detection and to the prediction of post-traumatic epilepsy have not been sufficiently developed yet; though they are of exceptional importance for clinical practice. Moreover, it is still poorly understood how the damage of the brain tissue leads to the brain structural rearrangement connected with epileptogenesis. In other words, the question is why and when the epileptogenesis occurs. The current paper considers the first results of the approach that we have suggested to quantitative assessment of the epileptogenesis development on the basis of EEG analysis as an alternative to the regular activity of the brain. This approach is based on the analysis of highly nonstationary signals that contain quasiperiodic fragments. In particular, the pipeline of procedures for the analysis includes detection of local quasiperiodic (rhythmic) behavior with further assessment of its dynamic characteristics and segmentation of fragments with “rhythmic” behavior. The first results of this approach application are connected with the analysis of experimental data obtained in the modeling of traumatic brain injury in rats. The characteristics of the EEG rhythm and their dynamics are analyzed in the background (before traumatic brain injury) and one, two, three and six days after the injury on the basis of ten-hour EEG recordings. The results demonstrate a significant difference in the degree of periodicity of the characteristics (total duration of the corresponding EEG fragments) before and after traumatic brain injury (a week after the trauma some restoration of the periodicity is observed). At the end of the paper we offer some interpretation of these results and express a hope that such studies could provide new ways for development and assessment of methods of the paroxysmal activity in the post-traumatic period.