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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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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) ...