Browsing by Subject "Electrophysiology"
Now showing items 1-12 of 12
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Adaptive compression and optimization for real-time energy-efficient wireless EEG monitoring systems
( IEEE , 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 ... -
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 ... -
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 ... -
Design and analysis of an adaptive compressive sensing architecture for epileptic seizure detection
( IEEE Computer Society , 2013 , Conference Paper)Epileptic detection techniques rely heavily on the Electroencephalography (EEG) as a representative signal carrying valuable information pertaining to the current brain state. In this work, we investigate the stability of ... -
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, ... -
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 ... -
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 ... -
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 ... -
Scalable real-time energy-efficient EEG compression scheme for wireless body area sensor network
( Elsevier Ltd , 2015 , Article)Recent technological advances in wireless body sensor networks have made it possible for the development of innovative medical applications to improve health care and the quality of life. By using miniaturized wireless ... -
Semi-transparent thermo-electric cells based on bismuth telluride and its composites with CNTs and graphene
( National Institute of Optoelectronics , 2019 , Article)In this paper, semi-transparent thin film thermo-electric cells based on the composite of bismuth telluride (Bi2Te3, p-type and n-type) with graphene and carbon nanotubes (CNTs) have been reported. The voltage, current and ... -
Sleep stage classification using sparse rational decomposition of single channel EEG records
( Institute of Electrical and Electronics Engineers Inc. , 2015 , Conference Paper)A sparse representation of ID signals is proposed based on time-frequency analysis using Generalized Rational Discrete Short Time Fourier Transform (RDSTFT). First, the signal is decomposed into a set of frequency sub-bands ...