Sleep Spindle Detection and Prediction Using a Mixture of Time Series and Chaotic Features

Hekmatmanesh, Amin; Mikaeili, Mohammad; Sadeghniiat-Haghighi, Khosro; Wu, Huapeng; Handroos, Heikki; Martínek, Radek; Nazeran, Homer

doi:10.15598/aeee.v15i3.2174

Cited by 19 publications

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“…TN is related to the false condition that is classified correctly. FN is related to the false condition that is as true condition, and FP is the false condition that is classified as true condition [21]. Table 4 depicts that the highest amount of TP and TN, consequently the highest accuracies belong to the ERD mother wavelet with the DWPT-DFA.…”

Section: Discussionmentioning

confidence: 99%

“…Based on the DWPT-DFA method, the extracted features with different mother wavelets are fed into the SSVM classifier with the GRBF kernel. Regarding the previous studies [8,21,22,30] on the classifiers of NN, K-NN, SVM, RBF and GRBF in the EEG signal processing, we employ a SSVM with a GRBF for classifying the imagin ary movement features. The SSVM and GRBF are the amended algorithms of the traditional SVM and RBF to solve the limitations and add flexibility, such as the curse of dimension in the SVM, and to add flexibility to the RBF for different cases of data distribution [8].…”

Section: Discussionmentioning

confidence: 99%

“…The Generalized RBF is a successful kernel, which is utilized in our previous studies [21,22,30]. The flexibility of the GRBF is summarized in terms of width (w > 0), shape (τ > 0) and center (c) of the Gaussian distribution parameters, which leads the fashion to highly accurate and robust results.…”

Section: Generalized Radial Basis Function (Grbf)mentioning

confidence: 99%

“…These patterns are studied for quantifying learning, memory consolidation and detecting mental disorders [22,30]. In our previous study [21], a combination of Katz, Higuchi, and Sevcick fractal dimensions is utilized to quantify the self-similarity, in order to detect sleep spindle patterns. Katz and Sevcick fractal dimensions are self-similarity quantifier algorithms based on the time space and Higuchi is based on the embedded space in signal processing [3].…”

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confidence: 99%

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Combination of discrete wavelet packet transform with detrended fluctuation analysis using customized mother wavelet with the aim of an imagery-motor control interface for an exoskeleton

Hekmatmanesh

Nasrabadi

et al. 2019

Multimed Tools Appl

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One critical issue in brain computer interface (BCI) studies is to extract imaginary movement patterns from electroencephalograph (EEG). In this study, two different techniques -detrended fluctuation analysis (DFA) and discrete wavelet packet transform-are combined (DWPT-DFA) for feature extraction. Both approaches are known as self-similarity quantifier techniques. In wavelet technique, mother wavelets play an important role. Herein, A customized mother wavelet utilizing event related desynchronization (ERD) potential patterns are extracted and updated automatically for individual subjects. Also, three predefined mother wavelets are used, and the results are compared with the customized mother wavelet. The predefined mother wavelets are db4, db8 and coiflet 4. The soft margin support vector machine with the generalized radial basis function (SSVM-GRBF) is employed to classify the DWPT-DFA features. For the efficiency of the method, nine subjects have participated to record EEG based on the imaginary hand movements. The ERDs and features are extracted from FC1 and CP6 channels. Results show that the combination of the DWPT and DFA with the personalize ERD mother wavelet gives the best accuracy of 85.33% with p < 0.001. Based on the results, we conclude that the DWPT-DFA method using the ERD mother wavelets improves significantly the efficiency of the SSVM-GRBF classifier.Keywords Brain computer interface (BCI) · Detrended fluctuation analysis (DFA) · Discrete wavelet packet transform (DWPT) · Event related desynchronization (ERD) · Soft margin support vector machine with generalized radial basis function (SSVM-GRBF)

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Generalized Radial Basis Function (Grbf)mentioning

confidence: 99%

mentioning

confidence: 99%

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Combination of discrete wavelet packet transform with detrended fluctuation analysis using customized mother wavelet with the aim of an imagery-motor control interface for an exoskeleton

Hekmatmanesh

Nasrabadi

et al. 2019

Multimed Tools Appl

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“…The DSLVQ method computes and assigns weights to the features to indicate their importance. Three improvements are employed to improve the generalization of the LVQ: I-weights are calculated by an iterative training algorithm for each dimension based on distances; II-a vector of weights is produced for each feature in (9); and III-the iterative training is used to optimize and select one scalar weight for each feature in (10). The selected optimal weights estimate distances for the iterative learning (t) procedure [26]:…”

Section: Discriminative Sensitive Learning Vector Quantizationmentioning

confidence: 99%

A combination of CSP-based method with soft margin SVM classifier and generalized RBF kernel for imagery-based brain computer interface applications

Hekmatmanesh

Jamaloo

et al. 2020

Multimed Tools Appl

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Several methods utilizing common spatial pattern (CSP) algorithm have been presented for improving the identification of imagery movement patterns for brain computer interface applications. The present study focuses on improving a CSP-based algorithm for detecting the motor imagery movement patterns. A discriminative filter bank of CSP method using a discriminative sensitive learning vector quantization (DFBCSP-DSLVQ) system is implemented. Four algorithms are then combined to form three methods for improving the efficiency of the DFBCSP-DSLVQ method, namely the kernel linear discriminant analysis (KLDA), the kernel principal component analysis (KPCA), the soft margin support vector machine (SSVM) classifier and the generalized radial bases functions (GRBF) kernel. The GRBF is used as a kernel for the KLDA, the KPCA feature selection algorithms and the SSVM classifier. In addition, three types of classifiers, namely K-nearest neighbor (K-NN), neural network (NN) and traditional support vector machine (SVM), are employed to evaluate the efficiency of the classifiers. Results show that the best algorithm is the combination of the DFBCSP-DSLVQ method using the SSVM classifier with GRBF kernel (SSVM-GRBF), in which the best average accuracy, attained are 92.70% and 83.21%, respectively. Results of the Repeated Measures ANOVA shows the statistically significant dominance of this method at p < 0.05. The presented algorithms are then compared with the base algorithm of this study i.e. the DFBCSP-DSLVQ with the SVM-RBF classifier. It is concluded that the algorithms, which are based on the SSVM-GRBF classifier and the KLDA with the SSVM-GRBF classifiers give sufficient accuracy and reliable results.

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Electrophysiological processing of happiness during conscious and sub-conscious awareness in depression

Soukhtanlou

Rostami

Salehinejad

et al. 2019

Neurology, Psychiatry and Brain Research

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Sleep Spindle Detection and Prediction Using a Mixture of Time Series and Chaotic Features

Cited by 19 publications

References 0 publications

Combination of discrete wavelet packet transform with detrended fluctuation analysis using customized mother wavelet with the aim of an imagery-motor control interface for an exoskeleton

Combination of discrete wavelet packet transform with detrended fluctuation analysis using customized mother wavelet with the aim of an imagery-motor control interface for an exoskeleton

A combination of CSP-based method with soft margin SVM classifier and generalized RBF kernel for imagery-based brain computer interface applications

Electrophysiological processing of happiness during conscious and sub-conscious awareness in depression

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