Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features
Journal of Medical Systems
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
Epileptic seizure detection in EEGs using time-frequency analysis
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection
Computers in Biology and Medicine
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Epileptic EEG detection using the linear prediction error energy
Expert Systems with Applications: An International Journal
Principles of Signal Detection and Parameter Estimation
Principles of Signal Detection and Parameter Estimation
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in theory and methods for nonstationary signal analysis
A new approach to detect and study spatial-temporal intracranial EEG frames
Digital Signal Processing
Time-frequency-based detection using discrete-time discrete-frequency Wigner distributions
IEEE Transactions on Signal Processing
A method for time-frequency analysis
IEEE Transactions on Signal Processing
Linear frequency-modulated signal detection using Radon-ambiguitytransform
IEEE Transactions on Signal Processing
Kernel design for time-frequency signal analysis using the Radontransform
IEEE Transactions on Signal Processing
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
A Generalized Multivariate Autoregressive (GmAR)-Based Approach for EEG Source Connectivity Analysis
IEEE Transactions on Signal Processing
Hi-index | 0.00 |
This paper presents a novel design of a time-frequency (t-f) matched filter as a solution to the problem of detecting a non-stationary signal in the presence of additive noise, for application to the detection of newborn seizure using multichannel EEG signals. The solution reduces to two possible t-f approaches that use a general formulation of t-f matched filters (TFMFs) based on the Wigner-Ville and cross Wigner-Ville distributions, and a third new approach based on the signal ambiguity domain representation; referred to as Radon-ambiguity detector. This contribution defines a general design formulation and then implements it for newborn seizure detection using multichannel EEG signals. Finally, the performance of different TFMFs is evaluated for different t-f kernels in terms of classification accuracy using real newborn EEG signals. Experimental results show that the detection method which uses TFMFs based on the cross Wigner-Ville distribution outperforms other approaches including the existing TFMF-based ones. The results also show that TFMFs which use high-resolution kernels such as the modified B-distribution, achieve higher detection accuracies compared to the ones which use other reduced-interference t-f kernels.