2018
DOI: 10.1186/s13634-018-0568-2
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Epilepsy EEG classification using morphological component analysis

Abstract: In this paper, we have proposed an application of sparse-based morphological component analysis (MCA) to address the problem of classification of the epileptic seizure using time series electroencephalogram (EEG). MCA was employed to decompose the EEG signal segments considering its morphology during epileptic events using undecimated wavelet transform (UDWT), local discrete cosine transform (LDCT), and Dirac bases forming the over-complete dictionary. Frequency-modulated time frequency features were extracted… Show more

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Cited by 8 publications
(3 citation statements)
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“…The vertical projection of the processed signal is non-orthogonal and the number of iterations increases Generalized morphological component analysis [59,60] Capable of adapting to different input signal types, improving calculation speed and signal separation accuracy…”
Section: Improved Compressionbased Algorithmmentioning
confidence: 99%
“…The vertical projection of the processed signal is non-orthogonal and the number of iterations increases Generalized morphological component analysis [59,60] Capable of adapting to different input signal types, improving calculation speed and signal separation accuracy…”
Section: Improved Compressionbased Algorithmmentioning
confidence: 99%
“…A local mean decomposition (LMD)-based feature analysis with Support Vector Machine (SVM) was utilized by Zhang and Chen where the classification accuracy reached an accuracy of 98.10% [11] . In the year 2018, for automated classification of epilepsy from EEG signals, deep learning approaches was proposed in [12] , [13] and a morphological component analysis based SVM classification was proposed in [14] , and these three approaches produced a high classification accuracy of more than 95% as per the consideration of their problem requirement. A scalogram based convolution network from EEG signals was proposed in [15] , a matrix determinant-based approach was utilized in [16] , cross-bispectrum analysis for seizure detection in [17] , a novel random forest model with grid search optimization in [18] are some of the famous works in 2019 and almost all the works have achieved a classification accuracy of 90% to 100% depending on type of case study.…”
Section: Introductionmentioning
confidence: 99%
“…Sairamya et al [13] proposed to employ the improved local pattern transformation methods (LPT) to extract the EEG features for epileptic diagnosis. For the SEEG classification, Mahapatra et al [14] proposed an application of sparse-based morphological component analysis (MCA) for the seizure classification. Subasi et al [15] proposed a method by combining with the multi-scale principal component analysis (MSPCA) denoising and random forest (RF) classifier for the classification of focal and non-focal EEG signals.…”
mentioning
confidence: 99%