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Epilepsy is characterized by the sudden and recurrent neuronal firing in the brain. It can be detected by analyzing Electroencephalogram (EEG) of the subject. In this paper, a method of classification of EEG signals into normal and seizure classes is presented. Features based on the statistical distributions were calculated for each frame of EEG signals. After ranking the features using Fisher's discriminant...
The gold standard for the localization of epileptic activities in the cerebral cortex is intracranial electrocorticography (ECoG) electrodes placed directly on the brain surface. However, it has limitations in being able to localize deep brain epileptic sources. As a means to improve the localization of epileptic activities from these subdural electrical recordings, we developed a simple source monitoring...
In this paper, the support vector machines (SVMs) is adopted for distinguishing between normal and epileptic EEG time series. The embedding dimension of electroencephalogram (EEG) time series is used as the input feature for detecting epileptic seizure automatically. Cao's method is applied for computing the embedding dimension of normal and epileptic EEG time series. In the last work, probabilistic...
Epilepsy is a serious neurological disorder characterized by recurrent unprovoked seizures due to abnormal or excessive neuronal activity in the brain. An estimated 50 million people around the world suffer from this condition, and it is classified as the second most serious neurological disease known to humanity, after stroke. With early and accurate detection of seizures, doctors can gain valuable...
Neuromodulation of the brain is an emerging therapy to control the epileptic seizure. The therapy can be improved with a closed-loop mechanism in which the electrical stimuli is activated in accordance with the seizure onset. In this paper, a correlation integral (CI) processor in a form of application specific integrated circuit is designed to estimate the brain complexity, chaoticity, after the...
In this paper, we propose a new scheme which combines two algorithms to detect epileptic seizure in the grouped multi-channel EEG signals. For the proposed scheme, a recent technique, Independent Component Analysis (ICA), is first adapted to separate blind sources and extract feature from grouped EEG signals. Then, Wavelet transform is followed for multi resolution and multi-level analysis on those...
Epileptic seizures are generated by an abnormal synchronization of neurons. Since epileptic seizures are unforeseeable for the patients, epileptic seizures detection is an interesting issue in epileptology, that novel approaches to understand the mechanism of epileptic seizures. In this study we analyzed invasive electroencephalogram (EEG) recordings in patients suffering from medically intractable...
Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important tool for the diagnosis of epilepsy. In this study, an epileptic seizure classification method based on features of the Empirical Mode Decomposition (EMD) of EEG records is proposed. The Intrinsic Mode Functions (IMFs) of EEG records are first computed, and then several time and...
An automatic Uni- or Multi-modal Intelligent Seizure Acquisition (UISA/MISA) system is highly applicable for onset detection of epileptic seizures based on motion data. The modalities used are surface electromyography (sEMG), acceleration (ACC) and angular velocity (ANG). The new proposed automatic algorithm on motion data is extracting features as “log-sum” measures of discrete wavelet components...
The worldwide prevalence of epilepsy is approximately 1%, and 25% of epilepsy patients cannot be treated sufficiently by available therapies. Brain stimulation with closed-loop seizure control has recently been proposed as an innovative and effective alternative. In this paper, a portable closed-loop brain computer interface for seizure control was developed and shown with several aspects of advantages,...
Compression of biosignals is an important means of conserving power in wireless body area networks and ambulatory monitoring systems. In contrast to lossless compression techniques, lossy compression algorithms can achieve higher compression ratios and hence, higher power savings, at the expense of some degradation of the reconstructed signal. In this paper, a variant of the lossy JPEG2000 algorithm...
The purpose of this study was to analyze morphological characteristics of electroencephalogram (EEG) signals in order to define a representation of epileptiform events that can distinguish them from other events occurring in the signal. There are several studies on parameterization of EEG signals, particularly for automatic detection of paroxysms related to epilepsy. Considering that during the automatic...
We present a method for the analysis of electroencephalogram (EEG) signals which has the potential to distinguish between ictal and seizure-free intracranial EEG recordings. This is achieved by analyzing common frequency components in multichannel EEG recordings, using the multivariate empirical mode decomposition (MEMD) algorithm. The mean frequency of the signal is calculated by applying the Hilbert-Huang...
Several different algorithms have been proposed for automatic detection of epileptic seizure based on both scalp and intracranial electroencephalography (sEEG and iEEG). Which modality that renders the best result is hard to assess though. From 16 patients with focal epilepsy, at least 24 hours of ictal and non-ictal iEEG were obtained. Characteristics of the seizures are represented by use of wavelet...
An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose. The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before,...
In this paper, we present the design of an epilepticseizure detector. This circuit is part of an implantable device used to continuously record intracerebral electroencephalographic signals through subdural and depth electrodes. The implemented seizure detector is based on a detection algorithm validated in Matlab tools and the circuits were implemented using CMOS 0.18-μm process. The proposed system...
Most of the automatic seizure detection schemes reported in the literature are complex for detecting seizures that are of (a) short duration, (b) minimal amplitude evolution, or (c) non-rhythmic mixed frequency epileptic activity. We present a novel morphology-based classifier to detect epileptic seizures for intracranial EEG recording. The method characterizes epileptic seizure by detecting continual...
In this paper, we present a seizure detector that is part of an implantable CMOS integrated device intended to identify seizure onsets and trigger focal treatment to disrupt seizure progression. The detector consists of a preamplifier, voltage level detectors, digital demodulators and a high-frequency detector. Variable gain amplification, adjustable threshold voltage identification and tunable recognition...
Implantable neurostimulators for the treatment of epilepsy that are capable of sensing seizures can enable novel therapeutic applications. However, detecting seizures is challenging due to significant intracranial EEG signal variability across patients. In this paper, we illustrate how a machine-learning based, patient-specific seizure detector provides better performance and lower power consumption...
Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates...
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