Structural Reliability Assessment by Local Approximation of Limit State Functions Using Adaptive Markov Chain Simulation and Support Vector Regression

Dai, Hongzhe; Zhang, Hao; Wang, Wei; Xue, Guofeng

doi:10.1111/j.1467-8667.2012.00767.x

Cited by 123 publications

(53 citation statements)

References 43 publications

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“…Nonlinear methods such as entropies, fractality dimension [98], synchronization [99] HOS, CD, RQA and detrended fluctuation analysis are applied to the de-noised EEG signals and characteristic features are extracted. The significant features extracted from sleep EEG signals can be classified using learning algorithms such as SVM [100,101], clustering techniques [102,103], classification methods [104,105] and neural networks [106,107,108]. Table 5 summarizes some of the work on sleep stage classification based on R&K standard where classification accuracy was reported.…”

Section: Discussionmentioning

confidence: 99%

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

et al. 2015

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Background: The brain's continuous neural activity during sleep can be monitored by electroencephalogram (EEG) signals. The EEG wave pattern and frequency vary during five stages of sleep. These subtle variations in sleep EEG signals cannot be easily detected through visual inspection. Summary: A range of time, frequency, time-frequency and nonlinear analysis methods can be applied to understand the complex physiological signals and their chaotic behavior. This paper presents a comprehensive comparative review and analysis of 29 nonlinear dynamics measures for EEG-based sleep stage detection. Key Messages: The characteristic ranges of these features are reported for the five different sleep stages. All nonlinear measures produce clinically significant results, that is, they can discriminate the individual sleep stages. Feature ranking based on the statistical F-value, however, shows that the third order cumulant of higher order spectra yields the most discriminative result. The distinct value ranges for each sleep stage and the discriminative power of the features can be used for sleep disorder diagnosis, treatment monitoring, and drug efficacy assessment.

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Section: Discussionmentioning

confidence: 99%

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

et al. 2015

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“…In recent years, Adeli and associates have advanced the idea that judicious combination of signal processing techniques such as wavelet transforms [37,[69][70][71][72][73], nonlinear dynamics, and chaos theory [74][75][76][77][78], and pattern recognition and classification techniques such as neural networks [79][80][81][82][83][84][85], principal component analysis (PCA) [86,87], support vector machine (SVM) [88,89], and recently developed enhanced probabilistic networks (EPNN) [90], is the most effective approach to model the subtle variation in EEG signals for computer-aided diagnosis of various neurological and psychiatric disorders. This also applies to alcoholism and its impact on the human brain [39,91,92].…”

Section: Computer-aided Assessment and Diagnosis Of Alcoholism-relatementioning

confidence: 99%

Computer-aided diagnosis of alcoholism-related EEG signals

Acharya

Sudevan

Bhat

et al. 2014

Epilepsy & Behavior

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“…The conceptual ideas share some similarities with those of the method developed in [12] in which SVM were used in classification. The SVM model used in the present work is based on the -insensitive loss function as explored in the context of reliability assessment in a few other works [9,[15][16][17]. A sequence of adaptive SVR surrogates G s (u), s = 1, .…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment With Adaptive Surrogates Based on Support Vector Machine Regression

Bourinet¹

2015

Proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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Abstract. Reliability assessment in the context of rare failure events still suffers from its computational cost despite some available methods widely accepted by researchers and engineers. Monte Carlo simulation methods even in their most efficient version such as subset simulation often require a large number of samples for an acceptable accuracy on the failure probability of interest. For low to moderately high dimensional problems and under the assumption of a rather smooth limit-state function, surrogate models a.k.a. metamodels or response surfaces represent interesting alternative solutions. This paper presents an adaptive method based on SVM surrogates used in regression. The SVM formulation hinges on the -insensitive loss function and a Gaussian RBF kernel. The key idea of the proposed method is to iteratively construct SVM surrogates which become more accurate as they get closer to the limit-state surface. Subset simulation is applied to the constructed SVM surrogates for assessing probabilities with respect to intermediate threshold values of the LSF and more importantly for generating new training points. The efficiency of the method is tested on several examples featuring various challenges: a parallel system, a highly curved limit-state surface at a single most probable failure point and a smooth high-dimensional limit-state surface with equal curvatures. The paper puts an emphasis on the key role of an optimal selection of SVM surrogate hyperparameters. This is achieved in the present work by minimizing an estimate of the leave-one-out error using the cross-entropy method.652

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Structural Reliability Assessment by Local Approximation of Limit State Functions Using Adaptive Markov Chain Simulation and Support Vector Regression

Cited by 123 publications

References 43 publications

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

Computer-aided diagnosis of alcoholism-related EEG signals

Reliability Assessment With Adaptive Surrogates Based on Support Vector Machine Regression

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