Search
Now showing items 1-10 of 11
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 ...
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 ...
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 ...
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 ...
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) ...
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 ...
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 ...
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 ...
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 ...