Arrhythmia classification algorithm based on multi-head self-attention mechanism

Wang, Yue; Yang, Guanci; Li, Shaobo; Li, Yang; He, Ling; Liú, Dan

doi:10.1016/j.bspc.2022.104206

Cited by 65 publications

(20 citation statements)

References 38 publications

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“…For more general case, that is, identification of EIV systems with mixed delays and irregular output sampling 82,83 will be studied in our future work. The proposed parameter estimation approaches in this paper can be combined with other estimation methods [84][85][86][87] to explore new parameter estimation algorithms of other linear and nonlinear stochastic systems with colored noises, and can be applied to other control and schedule areas such as the information processing systems [88][89][90][91][92] and transportation communication systems and so on.…”

Section: Discussionmentioning

confidence: 99%

Identification of dual‐rate sampled errors‐in‐variables systems with time delays

Xie

Huang

Tao

et al. 2023

Optim Control Appl Methods

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For identification of dual‐rate sampled errors‐in‐variables (EIV) systems with time delays, this paper utilizes the polynomial transformation technique and the redundant rule to derive the augmented dual‐rate model, and proposes a bias compensation‐based least squares (BC‐LS) algorithm. The basic idea is to obtain a biased parameter estimate by using the LS algorithm, then compensate the noise‐induced bias by the estimated noise variances, and finally estimate the time delay according to the unbiased parameter estimate and the given threshold. Considering that proper threshold selection has great influence on the performance of the BC‐LS identification algorithm for high noise level, the generalized orthogonal matching pursuit (GOMP) algorithm is introduced to simultaneously estimate the biased parameters and the time delay, and the bias compensation‐based GOMP (BC‐GOMP) algorithm is further proposed to identify the dual‐rate sampled EIV systems with time delays. The simulation examples demonstrate the effectiveness of the proposed two algorithms, and the BC‐GOMP algorithm performs better especially when the modeling dataset is small.

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Section: Discussionmentioning

confidence: 99%

Identification of dual‐rate sampled errors‐in‐variables systems with time delays

Xie

Huang

Tao

et al. 2023

Optim Control Appl Methods

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“…The proposed F-AM-RGEG algorithm in this paper can combine some statistical tools and optimal strategies [74][75][76][77][78][79][80] to study the parameter estimation algorithms of various stochastic systems with disturbances [81][82][83][84][85] and can be applied to literatures [86][87][88][89][90] such as paper-making systems and information processing systems and so on. The flowchart of computing the parameter estimation vector θ(t) by using the F-AM-RGEG algorithm in ( 73)-( 87) is shown in Figure 1 and the procedures are as follows.…”

Section: Filtered Auxiliary Model Recursive Generalized Extended Grad...mentioning

confidence: 99%

Filtered auxiliary model recursive generalized extended parameter estimation methods for Box–Jenkins systems by means of the filtering identification idea

Ding

Xiao

et al. 2023

Intl J Robust & Nonlinear

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For equation-error autoregressive moving average systems, that is, Box-Jenkins systems, this paper presents a filtered auxiliary model generalized extended stochastic gradient identification method, a filtered auxiliary model multi-innovation generalized extended stochastic gradient identification method, a filtered auxiliary model recursive generalized extended gradient identification method, a filtered auxiliary model multi-innovation recursive generalized extended gradient identification method, a filtered auxiliary model recursive generalized extended least squares identification method, and a filtered auxiliary model multi-innovation recursive generalized extended least squares identification method by using the filtering identification idea and the auxiliary model identification idea. The proposed filtered auxiliary model recursive generalized extended identification methods can be generalized to other linear and nonlinear multivariable stochastic systems with colored noises.

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“…The methods proposed in this paper can combine some statistical tools and optimal strategies [76][77][78][79][80][81][82][83] to study the parameter estimation algorithms of linear and nonlinear systems [84][85][86][87] and can be applied to other fields [88][89][90][91][92] such as paper-making systems, information processing, engineering systems and so on. The steps of computing the parameter estimation vector θl (t) by the AM-HMIGI algorithm in ( 77)-( 95) are listed as follows.…”

Section: The Am-hmigi Algorithmmentioning

confidence: 99%

Hierarchical iterative identification algorithms for a nonlinear system with dead‐zone and saturation nonlinearity based on the auxiliary model

Sun

Wang

Ding

2023

Adaptive Control & Signal

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Summary This paper investigates the identification problem of an output‐error nonlinear system with saturation and dead‐zone nonlinearity. Introducing a switching function and by means of the auxiliary model identification idea, an auxiliary model hierarchical least squares‐based iterative algorithm is proposed for estimating the parameters of the nonlinear system. Based on the hierarchical identification model, an auxiliary model hierarchical gradient‐based iterative algorithm is presented for the nonlinear system by utilizing the gradient search. In order to take full advantage of the system data, an auxiliary model hierarchical multi‐innovation gradient‐based iterative algorithm is derived for the nonlinear system according to the multi‐innovation identification theory. Finally, the numerical simulation results illustrate the effectiveness of the proposed algorithms.

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Arrhythmia classification algorithm based on multi-head self-attention mechanism

Cited by 65 publications

References 38 publications

Identification of dual‐rate sampled errors‐in‐variables systems with time delays

Identification of dual‐rate sampled errors‐in‐variables systems with time delays

Filtered auxiliary model recursive generalized extended parameter estimation methods for Box–Jenkins systems by means of the filtering identification idea

Hierarchical iterative identification algorithms for a nonlinear system with dead‐zone and saturation nonlinearity based on the auxiliary model

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