A New Approach to Gaussian Signal Smoothing: Application to ECG Components Separation

被引：5

作者：

Kheirati Roonizi, Arman ^{[1
]}

机构：

[1] Fasa Univ, Fac Sci, Dept Comp Sci, Fasa 7461686131, Iran

来源：

IEEE SIGNAL PROCESSING LETTERS | 2020年 / 27卷

关键词：

Electrocardiography; Kalman filters; Noise measurement; State estimation; Mathematical model; Atmospheric modeling; Smoothing methods; Constrained least-squares optimization; ECG components separation; Gaussian functions; state estimation; EXTRACTION; FRAMEWORK; MODEL;

D O I：

10.1109/LSP.2020.3031501

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

An efficient smoothing algorithm is presented in this letter. In this algorithm, while the signal is represented by sum of Gaussian functions-based dynamical model, its states are estimated using a constrained optimization problem where the Gaussian recurrence relation enters as constrain. The accuracy of the proposed method depends on the proper choice of regularization parameters. The value of the parameters is obtained using L-curve, U-curve, or V-curve method. As an application, the method is used for decomposing electrocardiogram (ECG) signal into its components waveform.

引用

页码：1924 / 1928

页数：5

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