Autoregressive Modeling Based Empirical Mode Decomposition (EMD) for Epileptic Seizures Detection Using EEG Signals
نویسندگان
چکیده
منابع مشابه
Epileptic Seizures Detection Based on Empirical Mode Decomposition of EEG Signals
Epilepsy is a chronic neurological disorder that affects more than 50 million people world wide, characterized by recurrent seizures (World Health Organization [WHO], 2006). An epileptic seizure is a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain (Fisher et al., 2005 & Berg et al., 2010). This electrical hyperactivity can ha...
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Epilepsy is a global disease with considerable incidence due to recurrent unprovoked seizures. These seizures can be noninvasively diagnosed using electroencephalogram (EEG), a measure of neuronal electrical activity in brain recorded along scalp. EEG is highly nonlinear, nonstationary and non-Gaussian in nature. Nonlinear adaptive models such as empirical mode decomposition (EMD) provide intui...
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Epilepsy is the most common brain diseases that cause many problems in the daily life of the patient. In most attempts to automatic detection, the attack used an EEG. In this paper, The complete data set consists of five sets recorded from normal and epileptic patients. Each set containing 100 single-channel EEG segments. Here we used first and last sets (A and E). Set A consisted of segments r...
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Electroencephalogram (EEG) is the concentrated expression of physiological activity of the brain. Effective EEG feature extraction methods are key to improving different EEG recognition rates, which is a significant issue in EEG studies. A new feature extraction method is proposed in this paper based on Empirical Mode Decomposition (EMD), which can decompose nonstationary EEG into a series of I...
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Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities, containing information relating to the different physiological states of the brain. It is a very effective tool for understanding the complex dynamical b...
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ژورنال
عنوان ژورنال: Traitement du Signal
سال: 2019
ISSN: 0765-0019,1958-5608
DOI: 10.18280/ts.360311