A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model

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.

Identification of Brain Electrical Activity Related to Head Yaw Rotations

Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.

Features of brain electrical activity in adult patients with POLG-related disorders

Introduction. Epilepsy is a common feature of mitochondrial disorders, including those associated with mutations in the POLG gene. Nevertheless, brain electrical activity features of POLG-related disorders in adult patients have not been adequately studied. Objective. To study the features and characteristics of the electroencephalography (EEG) pattern in adult patients with POLG-related disorders. Material and methods. Eight patients were examined: 7 with SANDO (Sensory Ataxic Neuropathy, Dysarthria, Ophthalmoparesis) syndrome, and 1 with MEMSA (Myoclonic Epilepsy Myopathy Sensory Ataxia) syndrome; median age was 32.5 years. All patients underwent routine EEG monitoring using a 19-channel electroencephalograph according to the generally accepted method. Results. Epileptic seizures were found in 3 patients, for 2 of them – as the first manifestation of the disease. In 6 patients, theta waves predominated in the occipital regions. Of those 6 patients, in 5 bilateral synchronous bursts of theta and delta wave groups were identified being more prominent in the frontocentral regions; 4 patients had transient non-lateralized delta activity in the occipital and parieto-occipital brain regions. In all patients, opening eyes led to the depression of rhythms and burst suppression. After photostimulation, in 2 cases bilateral synchronous bursts of delta and theta wave groups were recorded predominantly in frontal lobes. In 3 patients during hyperventilation an increase in delta activity in the occipital lobes and bilateral synchronous bursts of delta wave groups were observed. Epileptiform activity was recorded in 2 cases. Conclusion. In adult patients with POLG-related disorders, regardless of the clinical manifestation, typical EEG features include generalized background slowing, theta and delta bursts in occipital lobes with their suppression by opening eyes.

Predication and Analysis of Epileptic Seizure Neurological Disorder using Intracranial Electroencephalography (iEEG)

Epileptic seizure is one of the neurological brain disorder approximately 50 million of world’s population is affected. Diagnosis of seizure is done using medical test Electroencephalography. Electroencephalography is a technique to record brain signal by placing electrodes on scalp. Electroencephalography suffers from disadvantage such as low spatial resolution and presence of artifact. Intracranial Electroencephalography is used to record brain electrical activity by mounting strip, grid and depth electrodes on surface of brain by surgery. Online standard Intracranial Electroencephalography data is analyzed by our system for predication and analysis of Epileptic seizure. The pre-processing of Intracranial Electroencephalography signal is done and is further analyzed in wavelet domain by implementation of Daubechies Discrete Wavelet Transform. Features were extracted to classify as preictal and ictal state. Analysis of preictal state was carried out for predication of seizure. Intracranial Electroencephalography signals provide better result and accuracy in seizure detection and predication. Earlier warning can also be issued to control seizure with anti- epileptic drugs