Aug 8, 2018 · In this paper, we have proposed an application of sparse-based morphological component analysis (MCA) to address the problem of ...
In this paper, we have proposed an application of sparse-based morphological component analysis (MCA) to address the problem of classification of the epileptic ...
In this paper, we have proposed an application of sparse-based morphological component analysis (MCA) to address the problem of classification of the ...
In this study, we have reinvestigated the chaotic features and sub-band energies of EEG and its ability for aiding neurologists in detecting epileptic seizures.
We proposed a morphological component analysis (MCA) based method for epilepsy classification using the explicit dictionary of independent redundant transforms.
MCA was employed to decompose the EEG signal segments considering its morphology during epileptic events using undecimated wavelet transform (UDWT), ...
This thesis aims to classify the ECoG channel data as epileptic or non-epileptic using an automated machine learning algorithm called support vector machines ( ...
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Quantified EEG spike morphology (BEMS) and IED candidate count can be combined to classify an EEG as epileptiform with high reliability but with lower ...
This method combined with principal component analysis (ACP) is used to identify the different spikes morphologies in EEG signal after their detection.
Nov 30, 2024 · New Epilepsy Findings from Kyushu Institute of Technology Discussed (Epilepsy EEG classification using morphological component analysis) ...