Browsing by Subject "Electroencephalography"
Now showing items 1-20 of 24
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Adaptive energy-aware encoding for DWT-based wireless EEG tele-monitoring system
( IEEE Computer Society , 2013 , Conference Paper)Recent technological advances in wireless body sensor networks (WBSN) have made it possible for the development of innovative medical applications to improve health care and the quality of life. Electroencephalography ... -
An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System
( Hindawi Limited , 2017 , Article)The last decade has witnessed tremendous efforts to shape the Internet of things (IoT) platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor ... -
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 ... -
Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information Advances in Nonstationary Electrophysiological Signal Analysis and Processing
(2012 , Article)This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to ... -
Bayesian network based heuristic for energy aware EEG signal classification
( SpringerLink , 2013 , Conference Paper)A major challenge in the current research of wireless electroencephalograph (EEG) sensor-based medical or Brain Computer Interface applications is how to classify EEG signals as accurately and energy efficient as possible. ... -
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, ... -
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 ... -
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 ... -
Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees
( Elsevier Ltd , 2023 , Article)The analysis of Electroencephalography (EEG) signals has been an effective way of eye state identification. Its significance is highlighted by studies that examined the classification of eye states using machine learning ... -
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. ... -
Generalised phase synchrony within multivariate signals: An emerging concept in time-frequency analysis
(2012 , Conference Paper)This paper introduces the notion of the instantaneous frequency (IF) based generalized phase synchrony in time-frequency analysis based on the concept of cointegration. This phase synchrony is then quantified by investigating ... -
Instantaneous frequency based newborn EEG seizure characterisation
(2012 , Article)The electroencephalogram (EEG), used to noninvasively monitor brain activity, remains the most reliable tool in the diagnosis of neonatal seizures. Due to their nonstationary and multi-component nature, newborn EEG seizures ... -
Long-term epileptic EEG classification via 2D mapping and textural features
( Elsevier Ltd , 2015 , Article)Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic ... -
Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms
( Institute of Electrical and Electronics Engineers Inc. , 2021 , Article)Electroencephalography (EEG) based biometric systems are gaining attention for their anti-spoofing capability but lack accuracy due to signal variability at different psychological and physiological conditions. On the other ... -
Patient-Specific Seizure Detection Using Nonlinear Dynamics and Nullclines
( Institute of Electrical and Electronics Engineers Inc. , 2020 , Article)Nonlinear dynamics has recently been extensively used to study epilepsy due to the complex nature of the neuronal systems. This study presents a novel method that characterizes the dynamic behavior of pediatric seizure ... -
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 ... -
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 ... -
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 ... -
Robust biometric system using session invariant multimodal EEG and keystroke dynamics by the ensemble of self-ONNs
( Elsevier Ltd , 2022 , Article)Harnessing the inherent anti-spoofing quality from electroencephalogram (EEG) signals has become a potential field of research in recent years. Although several studies have been conducted, still there are some vital ...