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Analysis of the time-varying cortical neural connectivity in the newborn EEG: A time-frequency approach
(
IEEE
, 2011 , Conference Paper)
Relationships between cortical neural recordings as a representation of functional connectivity between cortical brain regions were quantified using different time-frequency criteria. Among these, Partial Directed Coherence ...
Time frequency signal analysis and processing toolbox update 6.2: An enhanced research platform with new advanced high-resolution TFDs
(
IEEE
, 2013 , Conference Paper)
This paper describes the advancements, updates and improvements made in the new Time Frequency Signal Analysis TFSAP toolbox as compared with the previous TFSA toolbox version. The updates and improvements done in TFSA ...
Prediction of infarction volume and infarction growth rate in acute ischemic stroke
(
Nature Publishing Group
, 2017 , Article)
The prediction of infarction volume after stroke onset depends on the shape of the growth dynamics of the infarction. To understand growth patterns that predict lesion volume changes, we studied currently available models ...
Evidence theory-based approach for epileptic seizure detection using EEG signals
(
IEEE
, 2012 , Conference Paper)
Electroencephalogram (EEG) is one of the potential physiological signals used for detecting epileptic seizure. Discriminant features, representing different brain conditions, are often extracted for diagnosis purposes. ...
Effective seizure detection through the fusion of single-feature enhanced-k-NN classifiers of EEG signals
(
IEEE
, 2013 , Conference Paper)
Electroencephalogram (EEG) physiological signals are widely used for detecting epileptic seizure. To reduce complexity stemming from the dimensionality problem, EEG signals are often reduced into a smaller set of discriminant ...
Deep Reinforcement Learning Algorithm for Smart Data Compression under NOMA-Uplink Protocol
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
One of the highly promising radio access strategies for enhancing performance in the next generation cellular communications is non-orthogonal multiple access (NOMA). NOMA offers a number of advantages including better ...
EEG feature extraction and selection techniques for epileptic detection: A comparative study
(
IEEE Computer Society
, 2013 , Conference Paper)
Epileptic detection techniques rely heavily on the Electroencephalography (EEG) as representative signal carrying valuable information pertaining to the current brain state. For these techniques to be efficient and reliable, ...
Performance evaluation for compression-accuracy trade-off using compressive sensing for EEG-based epileptic seizure detection in wireless tele-monitoring
(
IEEE
, 2013 , Conference Paper)
Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities. EEG signals capture important information pertinent to different ...
EEG-based Analysis Study for Patients Receiving Intravenous Antibiotic Medication
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
In this paper, we conduct a biological data collection and analysis study for patients undergoing routine planned intravenous antibiotic treatment. The acquired data (i.e., Electroencephalogram (EEG), temperature and blood ...
Performance Comparison of classification algorithms for EEG-based remote epileptic seizure detection in Wireless Sensor Networks
(
IEEE Computer Society
, 2014 , Conference Paper)
Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems. Classification is the most important technique for wide-ranging ...