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Epilepsy is a common neural disorder disease; about 1.7% of the global population has epilepsy. Most patients take antiepileptic drugs to reduce their seizures. Among them, nearly one-third of the patients are drug-resistant epilepsy. The alternative treatment is the resection surgery of removing the epileptogenic zone. However, all above patients will still have some seizures, which will influence...
This paper outlines the feasibility of detecting epilepsy though low-cost and low-energy dedicated hardware with bit-serial processing. The concept of a novel bit-serial data processing unit (DPU) is presented which implements the functionality of a complete neuron. The proposed approach has been tested using various network configurations and compared with related work. The proposed DPU uses only...
Sudden Unexpected Death in Epilepsy (SUDEP) is the leading mode of epilepsy-related death and is most common in patients with intractable, frequent, and continuing seizures. Magnetic Resonance Imaging (MRI) and other neuroimaging techniques create an important data source for investigators to collaborate and share a larger cohort of potential SUDEP patient data. To address the challenges of sharing...
In this paper, we report on a proof-of-concept wearable prototype, called iSeiz, that can detect specific seizure activity, namely generalized tonic-clonic, in epilepsy patients. We first describe the high-level architecture of iSeiz, and then elaborate on its hardware and software features, including its robust and low-computational intensive real-time seizure detection algorithm (SDA), as well as...
Epilepsy is characterized by temporary and unexpected electrical deterioration in brain. EEG is preferred in diagnosis. There are many studies in the literature on EEG signals to differentiate between groups in epileptic and non-epileptic individuals. In this study, EEG signals were examined for predicting seizures for the three pre-, during, and post-seizures. The EEG was filtered by Singular Spectrum...
This paper presented the design of a wearable EEG-based control network and emergency medical assistance system which works and is now under test by volunteers. The system can detect the user's blink control signal, heart rates and brain signal, etc. A new scheme to control the device for disabilities was proposed in the paper. A DSP was used to calculate and process these data which can also send...
Recognition of epileptic seizures is an important issue and in certain circumstances it is desirable to have portable equipment implementing the algorithm in order to better monitor the patients. This work considers a widely used EEG database from University of Bonn as reference for comparing our recognition method with other previously reported. In order to perform epileptic seizures we combine a...
Accurate localization of brain regions responsible for language and cognitive functions in Epilepsy patients should be carefully determined prior to surgery. Electrocorticography (ECoG)-based Real Time Functional Mapping (RTFM) has been shown to be a safer alternative to the electrical cortical stimulation mapping (ESM), which is currently the clinical/gold standard. Conventional methods for analyzing...
This paper presents a novel wearable biomedical Network on Chip (NoC) concept development to monitor and predict irregular brain waves as advanced sensitive portable for an electroencephalogram (EEG) analysis device. The proposed device will monitor brain’s spontaneous electrical activity in normal and abnormal situations for specific patients suffering from different types of epilepsy...
Epileptologists use interictal epileptic discharge (lED) as a marker for epilepsy. The present conventional method to distinguish normal and I ED by an epileptologist's visual screening is tedious and operator dependent. The focus of this paper is to distinguish normal and IED in clinically recorded electroencephalogram (EEG) using discrete wavelet transform. Wavelet multiresolution analysis has been...
To minimize functional morbidity associated with brain surgery, new preventive approaches (also referred to as "prehabilitation") by using motor-imagery-based computer interfaces (MI-BCIs) can be utilized. To achieve successful MI-BCI performance for prehabilitation purposes, the characteristics of an electrocorticographic (ECoG) signal that is associated with overt motor function ("real...
Electrooculography (EOG) artifacts, generated by winking or other eye's movements, should be eliminated because they are the cause of the wrong decision in analysis the Electroencephalography (EEG) data, especially in the diagnosis of epilepsy. One of the efficient methods for signal separation is the Second order blind identification (SOBI), a blind source separation technique. In most cases, the...
Brain machine interfacing (BMI) needs continuous analyses of ongoing brain activity. For a successful interaction, related brain activities and events should be reliably detected; using various approaches including machine learning techniques. To this end, a variety of characteristic signal features as well as different types of classifiers can be used. One possible application of such an interaction...
Pattern classification in electroencephalography (EEG) signals is an important problem in biomedical engineering since it enables the detection of brain activity, in particular the early detection of epileptic seizures. In this paper we propose a k-nearest neighbors classification for epileptic EEG signals based on an t-location-scale statistical representation to detect spike-and-waves. The proposed...
Unilateral medial temporal lobe epilepsy (mTLE) have similar symptom in clinic. Previous studies indicate that it's difficult to find significant differences when comparing the structure of left and right mTLE directly. As is known to all, hippocampus sclerosis often causes mTLE. Therefore, it may be essential to detect the internal structure of hippocampus (HC) to confirm the lateralization of mTLE...
In this study, it was aimed to classify the epileptic and normal EEG data by using the Ensemble Empirical Mode Decomposition (EEMD) method. For this purpose, we studied with 3 data groups and 30 data from each group were examined. Firstly, data were decomposed into intrinsic mode functions (IMFs) using EEMD. Decomposer features were calculated from the 1st IMF of the EEMD expansion of EEG signals...
Visibility graph analysis of time series became widely used as a time series analysis in the recent years. State transfer network is a network of mapping mono/multivariate time series into a network of local states based on visibility graph, it was used to study the evolutionary behavior of time series and in this study, we applied this principle to the detection of epileptic seizures. Two sets of...
With regard to electrical interactions in the brain, Electroencephalographic (EEG) records are strongly considered to be one of the most applicable methods in diagnosis of neurologic diseases. One of the neurologic diseases is called Epilepsy which has impacted uponnearly 1% of the population worldwide. Today, employing computerized systems, to use in quick diagnosis of illnesses that has been a paramount...
Besides the evident brain state alterations present in electroencephalogram (EEG), epileptic seizures are also associated with changes in the cardiovascular status. In particular, heart rate (HR) has become an important autonomic biomarker in seizure prediction. Based on that, a preliminary study is here proposed in order to inspect the behaviour of electrocardiogram (ECG) derived features in the...
The present study proposes new method for epileptic seizure prediction based on heart rate variability (HRV) analysis and one-class support vector machines (SVM) technique. Methods: Excessive neural activity in preictal period affects not only brain activity, but also autonomic nervous system, that affects HRV. The proposed method distinguishes eight features, analyzes matrix of eigenvalues and eigenvectors,...
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