2021
DOI: 10.1016/j.compbiomed.2021.104969
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Classification of alcoholic EEG signals using wavelet scattering transform-based features

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Cited by 55 publications
(23 citation statements)
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“…The composition of k-spaced amino acid pairs (CKSAAP) ( Chen et al, 2010 ; Ahmad et al, 2021 ; Akbar et al, 2021 ; Al-Qazzaz et al, 2021 ; Alar and Fernandez, 2021 ; Alim et al, 2021 ; Buriro et al, 2021 ) method describes the order-related information of the protein sequence, which takes the occurrence frequency of two amino acids separated by k-residues in the sequence as a feature element. The protein contains 20 amino acids; thus, a 400-dimensional feature vector can be obtained for each interval.…”
Section: Methodsmentioning
confidence: 99%
“…The composition of k-spaced amino acid pairs (CKSAAP) ( Chen et al, 2010 ; Ahmad et al, 2021 ; Akbar et al, 2021 ; Al-Qazzaz et al, 2021 ; Alar and Fernandez, 2021 ; Alim et al, 2021 ; Buriro et al, 2021 ) method describes the order-related information of the protein sequence, which takes the occurrence frequency of two amino acids separated by k-residues in the sequence as a feature element. The protein contains 20 amino acids; thus, a 400-dimensional feature vector can be obtained for each interval.…”
Section: Methodsmentioning
confidence: 99%
“…As another type of convolutional networks, the wavelet scattering transform [31][32][33] is gaining increasing attention from the community of signal processing and machine learning with its applications for pattern analysis, such as neural disease classification 34 , authentication of art works 35 , predicting indoor fingerprinting-based localization 36 , classification of ECG beats 37 , classification of alcoholic EEG signals 38 , and classification of magnetohydrodynamic simulations 39 .…”
Section: Introductionmentioning
confidence: 99%
“…In the field of complex signal analysis, wavelet scattering transforms or networks [23], [24], [25] offer the extraction of low-variance coefficients from time series (being robust to translation) and images (being robust to both translation and rotation), which can be useful for machine learning and classification. Particularly for the analysis of EEG signals, wavelet scattering networks have recently been used for extracting differential features of the data for classifying alcohol-affected and healthy subjects [26], recognizing emotional states [27], and detecting different types of heartbeats [28].…”
Section: Introductionmentioning
confidence: 99%