introduction: in this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. epilepsy is considered as a dynamical change in nonlinear and complex brain system. the ability of the proposed measure for characterizing the normal and epileptic eeg signals when the signal is short or is...

Introduction: Epileptic seizure detection is a key step for both researchers and epilepsy specialists for epilepsy assessment due to the non-stationariness and chaos in the electroencephalogram (EEG) signals. Current research is directed toward the development of an efficient method for epilepsy or seizure detection based the limited penetrable visibility graph (LPVG) algorith...

the monitoring of epileptic seizures is mainly done by means of eeg (electroencephalogram) monitoring. although this method is accurate, it is not comfortable for the patient as the eeg-electrodes have to be attached to the scalp which hampers the patient’s movement. this makes long term home monitoring not feasible. in this paper the aim is to propose a seizure detection system based on accele...

background: imaging studies performed in research centers suggest a high prevalence of brain structural abnormalities in patients with epilepsy. however, the results of research studies may not be directly applicable to general clinic, because they may perform on patients with high probability of cerebral abnormalities. on the other hand, more advanced modalities are likely to be used in resear...

Epilepsy is a common neurological disease with various seizure types, complicated etiologies, and unclear mechanisms. Its diagnosis mainly relies on clinical history, but an electroencephalogram also crucial auxiliary examination. Recently, brain imaging technology has gained increasing attention in the of epilepsy, conventional magnetic resonance can detect epileptic foci some patients epileps...

Epilepsy is the most common form of neurological disease. Patients with epilepsy may experience seizures a certain duration or without provocation. analysis can be done an electroencephalogram (EEG) examination. Observation qualitative EEG signals generates high cost and often confuses due to nature non-linear signal noise. In this study, we proposed processing system for seizure detection. The...

Abstract Since the manual diagnosis of electroencephalograph (EEG) recordings requires a lot labor and material costs for clinical seizure detection, annotation data is great challenge detection. To tackle issue small samples epilepsy-labeled data, we propose semi-supervised generative adversarial network-based detection method. begin with, Butterworth filter used to preprocess EEG, filtered EE...

Abstract Epilepsy is a nervous system disease, which caused by abnormal discharge of brain neurons. The clinical manifestations are generalized seizures, clonus, loss consciousness, and shock. An electroencephalogram (EEG) can accurately capture the changes in EEG activities. Therefore, signals used to detect seizures. In this paper, an epilepsy detection model based on time-gated feature netwo...

Epilepsy is a neurological disorder characterized by recurrent seizures due to spontaneous changes of chemical synaptic coupling within the central nervous system. Numerous studies have been done in order increase level cognition epilepsy. Electroencephalography (EEG) as non-invasive technique with ability presenting potentials on head surface neural activity widely used epilepsy studies. The s...

anxiety and depression are very common in population and occur in a wide range of clinical states and is very common in epileptic patients. many recent epidemiological studies have found a high prevalence of depression and anxiety in epileptic patients. these studies found that epileptic patients suffered from depression and anxiety than those without epilepsy. the prevalence of depression or a...