Browsing by Subject "EEG"
Now showing items 1-19 of 19
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A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction
( Institute of Electrical and Electronics Engineers Inc. , 2021 , Article)Objective: Scarcity of good quality electroencephalography (EEG) data is one of the roadblocks for accurate seizure prediction. This work proposes a deep convolutional generative adversarial network (DCGAN) to generate ... -
Classification for Imperfect EEG Epileptic Seizure in IoT applications: A Comparative Study
( Institute of Electrical and Electronics Engineers Inc. , 2018 , Conference Paper)Epileptic seizure detection could be detected through investigating the electroencephalography (EEG), which is deemed to be very important for IoT wearable sensor-based health systems. EEG-based classification is crucial ... -
Classification of EEG signals for detection of epileptic seizure activities based on LBP descriptor of time-frequency images
( IEEE Computer Society , 2015 , Conference Paper)This paper presents novel time-frequency (t-f) feature extraction approach for the classification of EEG signals for Epileptic seizure activities detection. The proposed features are based on Local Binary Patterns (LBP) ... -
Deep learning approach for EEG compression in mHealth system
( Institute of Electrical and Electronics Engineers Inc. , 2017 , Conference Paper)The emergence of mobile health (mHealth) systems has risen the challenges and concerns due to the sensitivity of the data involved in such systems. It is essential to ensure that these data are well delivered to the health ... -
Detection of neonatal seizure using multiple filters
( IEEE , 2010 , Conference Paper)It is often impossible to accurately differentiate between seizure and non-seizure related activities in irifants based on clinical manifestations alone. The electroencephalogram (EEG) is therefore the best tool available ... -
Detection of partial seizure: An application of fuzzy rule system for wearable ambulatory systems
( IEEE , 2014 , Conference Paper)Electroencephalography (EEG) plays an intelligent role, especially EEG based health diagnosis of brain disorder, as well as brain-computer interface (BCI) applications. One such research field is related to epilepsy. The ... -
Detection of seizure signals in newborns
( IEEE , 1999 , Conference Paper)This paper considers a system design for processing a multidimensional biomedical signal formed by EEG, ECG, EOG and motion recorded from a newborn, for the purpose of detection of epileptic seizures in newborns as an ... -
EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: A structured review
( Elsevier Ireland Ltd , 2016 , Article)Objectives Hypoxic ischaemic encephalopathy is a significant cause of mortality and morbidity in the term infant. Electroencephalography (EEG) is a useful tool in the assessment of newborns with HIE. This systematic review ... -
Effective implementation of time–frequency matched filter with adapted pre and postprocessing for data-dependent detection of newborn seizures
( Elsevier , 2013 , Article)Neonatal EEG seizures often manifest as nonstationary and multicomponent signals, necessitating analysis in the time–frequency (TF) domain. This paper presents a novel neonatal seizure detector based on effective implementation ... -
Electroencephalographic evidence of gray matter lesions among multiple sclerosis patients: A case-control study.
( Lippincott, Williams & Wilkins , 2021 , Article)This study aimed to investigate evidence of gray matter brain lesions in multiple sclerosis (MS) patients by evaluating the resting state alpha rhythm of brain electrical activity.The study included 50 patients diagnosed ... -
Embedded wearable EEG seizure detection in ambulatory state
( UK Simulation Society , 2014 , Article)This paper describes a classification method is presented using a Fuzzy System to detect the occurrences of Partial Seizures from Epilepsy data, which can be implemented in any embedded system as a wearable detection system. ... -
FPGA implementation of DWT EEG data compression for wireless body sensor networks
( Institute of Electrical and Electronics Engineers Inc. , 2017 , Conference Paper)Wireless body sensor networks (WBSN) provide an appreciable aid to patients who require continuous care and monitoring. One key application of WBSN is mobile health (mHealth) for continuous patient monitoring, acquiring ... -
Haralick feature extraction from time-frequency images for epileptic seizure detection and classification of EEG data
( IEEE , 2014 , Conference Paper)This paper presents novel time-frequency (t-f) features based on t-f image descriptors for the automatic detection and classification of epileptic seizure activities in EEG data. Most previous methods were based only on ... -
Joint sparsity recovery for compressive sensing based EEG system
( Institute of Electrical and Electronics Engineers Inc. , 2018 , Conference Paper)The last decade has witnessed tremendous efforts to shape the internet of thing (IoT) platforms to be well suited for healthcare applications. These applications involve the deployment of remote monitoring platforms to ... -
Measuring time-varying information flow in scalp EEG signals: Orthogonalized partial directed coherence
( IEEE , 2014 , Article)This study aimed to develop a time-frequency method for measuring directional interactions over time and frequency from scalp-recorded electroencephalographic (EEG) signals in a way that is less affected by volume conduction ... -
A Nonlinear Model of Newborn EEG with Nonstationary Inputs
( Springer US , 2010 , Article)Newborn EEG is a complex multiple channel signal that displays nonstationary and nonlinear characteristics. Recent studies have focussed on characterizing the manifestation of seizure on the EEG for the purpose of automated ... -
Novel techniques for improving NNetEn entropy calculation for short and noisy time series
( Springer Science and Business Media B.V. , 2023 , Article)Entropy is a fundamental concept in the field of information theory. During measurement, conventional entropy measures are susceptible to length and amplitude changes in time series. A new entropy metric, neural network ... -
Parametric modeling of EEG signals with real patient data for simulating seizures and pre-seizures
( IEEE , 2013 , Conference Paper)Numerous theories and models have been developed to associate various findings or in relating EEG patterns to develop a software simulators. In this paper, a Dynamic model for simulating the EEG signal has been developed ... -
Walsh transform with moving average filtering for data compression in wireless sensor networks
( Institute of Electrical and Electronics Engineers Inc. , 2017 , Conference Paper)Due to the peculiarity of wireless sensor networks (WSNs), where a group of sensors continuously transmit data to other sensors or to the fusion center, it is crucial to compress the transmitted data in order to save the ...