Open AccessA Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model
Author(s) -
Saleh Lashkari,
Ali Moghimi,
Hamid Reza Kobravi,
Mohamad Amin Younessi Heravi
Publication year - 2021
Publication title -
international clinical neuroscience journal
Language(s) - English
Resource type - Journals
eISSN - 2383-2096
pISSN - 2383-1871
DOI - 10.34172/icnj.2021.36
Subject(s) - spike (software development) , curvature , electroencephalography , epilepsy , thresholding , nonlinear system , computer science , neuroscience , brain activity and meditation , pattern recognition (psychology) , artificial intelligence , physics , mathematics , psychology , geometry , software engineering , quantum mechanics , image (mathematics)
Background: Animal models of absence epilepsy are widely used in childhood absence epilepsy studies. Absence seizures appear in the brain’s electrical activity as a specific spike wave discharge (SWD) pattern. Reviewing long-term brain electrical activity is time-consuming and automatic methods are necessary. On the other hand, nonlinear techniques such as phase space are effective in brain electrical activity analysis. In this study, we present a novel SWD-detection framework based on the geometrical characteristics of the phase space. Methods: The method consists of the following steps: (1) Rat stereotaxic surgery and cortical electrode implantation, (2) Long-term brain electrical activity recording, (3) Phase space reconstruction, (4) Extracting geometrical features such as volume, occupied space, and curvature of brain signal trajectories, and (5) Detecting SDWs based on the thresholding method. We evaluated the approach with the accuracy of the SWDs detection method. Results: It has been demonstrated that the features change significantly in transition from a normal state to epileptic seizures. The proposed approach detected SWDs with 98% accuracy. Conclusion: The result supports that nonlinear approaches can identify the dynamics of brain electrical activity signals.