Intelligent online catastrophe assessment and preventive control via a stacked denoising autoencoder

Liu, Yangyang; Zhai, Mingyu; Jin, Jiahui; Song, Aibo; Lin, Jing; Wu, Zhang; Zhao, Yixin

doi:10.1016/j.neucom.2019.10.090

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…As shown in Fig. 3, the SDAE network is a neural network formed by stacking multiple denoising autoencoders (DAEs) [35]. Each DAE consists of an input-to-hidden-layer encoder (within the red dashed box) and a hidden-to-output-layer decoder (within the green dashed box).…”

Section: B Feature Extraction By the Sdae Network In The Timefrequency Domainmentioning

confidence: 99%

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HFO Detection in Epilepsy: A Stacked Denoising Autoencoder and Sample Weight Adjusting Factors-Based Method

Wu¹,

Qin

Wan

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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High-frequency oscillations (HFOs) recorded by the intracranial electroencephalography (iEEG) are the promising biomarkers of epileptogenic zones. Accurate detection of HFOs is the key to pre-operative assessment for epilepsy. Due to the subjective bias caused by manual features and the class imbalance between HFOs and false HFOs, it is difficult to obtain satisfactory detection performance by the existing methods. To solve these problems, we put forward a novel method to accurately detect HFOs based on the stacked denoising autoencoder (SDAE) and the ensemble classifier with sample weight adjusting factors. First, the adjustable threshold of Hilbert envelopes is proposed to isolate the events of interest (EoIs) from background activities. Then, the SDAE network is utilized to automatically extract features of EoIs in the time-frequency domain. Finally, the AdaBoost-based support vector machine ensemble classifier with sample weight adjusting factors is devised to separate HFOs from EoIs by using the extracted features. These adjusting factors are used to solve the class imbalance problem by adjusting sample weights when learning the base classifiers. Our HFO detection method is evaluated by using clinical iEEG data recorded from 20 patients with medically refractory epilepsy. The experimental results show that our detection method outperforms some existing methods in terms of sensitivity and false discovery rate. In addition, the HFOs detected by our method are effective for localizing seizure onset zones.

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Section: B Feature Extraction By the Sdae Network In The Timefrequency Domainmentioning

confidence: 99%

“…aims to describe the degree of proximity between training samples and their reconstructed representations, the L 2 regularization term λΩ w aims to prevent overfitting, and the sparsity regularization term βΩ s aims to obtain the sparse representations of input vectors [35].…”

Section: B Feature Extraction By the Sdae Network In The Timefrequency Domainmentioning

confidence: 99%

HFO Detection in Epilepsy: A Stacked Denoising Autoencoder and Sample Weight Adjusting Factors-Based Method

Wu¹,

Qin

Wan

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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show abstract

“…Recent years have witnessed the rapid development of the autoencoder-based methods and their wide applications in several domains, including cancer prediction [16], control engineering [17,18], fault diagnosis [19], building energy management [20], and many more. In the work of Ref.…”

Section: Basic Autoencoder Method Deep Learning Techniquesmentioning

confidence: 99%

Sparse-Coding-Based Autoencoder and Its Application for Cancer Survivability Prediction

Huang

Wang

Zhang

2022

Mathematical Problems in Engineering

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Cancer-survivability prediction is one of the popular research topics, that attracted great attention from both the health service providers and academia. However, one remaining question comes from how to make full use of a large number of available factors (or features). This paper, accordingly, presents a novel autoencoder algorithm based on the concept of sparse coding to address this problem. The main contribution is twofold: the utilization of sparsity coding for input feature selection and a subsequent classification using latent information. Precisely, a typical autoencoder architecture is employed for reconstructing the original input. Then the sparse coding technique is applied to optimize the network structure, with the aim of selecting optimal features and enhancing the generalization capability. In addition, the refined latent information is further cast as alternative features for training a sparse classifier. To evaluate the performance of the proposed autoencoder architecture, we present a comprehensive analysis using a publicly available data repository (i.e., Surveillance, Epidemiology, and End Results, SEER). Experimental study shows that the proposed approach has the ability of extracting important features from high-dimensional inputs and achieves competitive performance than other state-of-the-art classification techniques.

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“…The catastrophe theory studies the transformation of a nonlinear system from an unstable state to another stable state in the form of a catastrophe. Under the action of a small accidental perturbation, the stable state can still keep the original state, whereas the unstable state can leave the original state catastrophically once disturbed, and the stable and unstable states are interlaced with each other [3][4][5]. Wright and Deacon used catastrophe theory to predict the behavior of robots driven by fences [6].…”

Section: Introductionmentioning

confidence: 99%

Alternating Positive and Negative Feedback Control Model Based on Catastrophe Theories

et al. 2021

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In automatic control systems, negative feedback control has the advantage of maintaining a steady state, while positive feedback control can enhance some activities of the control system. How to design a controller with both control modes is an interesting and challenging problem. Motivated by it, on the basis idea of catastrophe theories, taking positive feedback and negative feedback as two different states of the system, an adaptive alternating positive and negative feedback (APNF) control model with the advantages of two states is proposed. By adaptively adjusting the relevant parameters of the constructed symmetric catastrophe function and the learning rule based on error and forward weight, the two states can be switched in the form of catastrophe. Through the Lyapunov stability theory, the convergence of the proposed adaptive APNF control model is proven, which indicates that system convergence can be guaranteed by selecting appropriate parameters. Moreover, we present theoretical proof that the negative feedback system with negative parameters can be equivalent to the positive feedback system with positive parameters. Finally, the results of the simulation example show that APNF control has satisfactory performance in response speed and overshoot.

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Intelligent online catastrophe assessment and preventive control via a stacked denoising autoencoder

Cited by 10 publications

References 37 publications

HFO Detection in Epilepsy: A Stacked Denoising Autoencoder and Sample Weight Adjusting Factors-Based Method

HFO Detection in Epilepsy: A Stacked Denoising Autoencoder and Sample Weight Adjusting Factors-Based Method

Sparse-Coding-Based Autoencoder and Its Application for Cancer Survivability Prediction

Alternating Positive and Negative Feedback Control Model Based on Catastrophe Theories

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