Fast Basis Searching Method of Adaptive Fourier Decomposition Based on Nelder-Mead Algorithm for ECG Signals

Wang, Ze; Yang, Limin; Wong, Chi Man; Wan, Feng

doi:10.1007/978-3-319-25393-0_34

Cited by 4 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the UKF method, since it requires that the objective function values are non-negative, the reformulated optimization problem shown in (6) is not further suitable. According to the orthogonal property of {B n } ∞ n=1 , (5) is equivalent to…”

Section: Efficient Implementation Of Basis Search For Afd 211 Maximmentioning

confidence: 99%

“…In addition, for some specific types of signals, such as electrocardiography (ECG) signals, the distributions of a n have already been recognized, which can be considered as the pre-knowledge for searching suitable a n [6,7]. Accordingly, the number of points in the coarse search process for initial points can be further reduced [7].…”

Section: Determination Of Initial Pointsmentioning

confidence: 99%

“…In the next section, the NM algorithm, the UKF method, the GA, and the PSO algorithm will be reviewed, which will be adopted to solve (6). The pseudocode of the AFD based on the four optimization algorithms is shown in Algorithm 1.…”

Section: Determination Of Initial Pointsmentioning

confidence: 99%

“…All the traditional methods, including the wavelet one, are of the same nature. Accordingly, the AFD has been successfully applied to the system identification, modeling, signal compression and denoising [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, calculating the gradient information and objective function values of (5) is complicated and time consuming. In our previous work shown in [6], a preliminary study of applying the NM method to improve the computation efficiency of the AFD shows that the NM method can reduce the computation time of the AFD to the half of that based on the conventional exhaustive search method. However, in [6], the performance of the NM method is only verified by one kind of special signals, i.e., the ECG signals, with non-optimal parameter selection, and is only compared with that of the exhaustive search.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Fast basis search for adaptive Fourier decomposition

Wang

Wan

Wong

et al. 2018

EURASIP J. Adv. Signal Process.

Self Cite

View full text Add to dashboard Cite

The adaptive Fourier decomposition (AFD) uses an adaptive basis instead of a fixed basis in the rational analytic function and thus achieves a fast energy convergence rate. At each decomposition level, an important step is to determine a new basis element from a dictionary to maximize the extracted energy. The existing basis searching method, however, is only the exhaustive searching method that is rather inefficient. This paper proposes four methods to accelerate the AFD algorithm based on four typical optimization techniques including the unscented Kalman filter (UKF) method, the Nelder-Mead (NM) algorithm, the genetic algorithm (GA), and the particle swarm optimization (PSO) algorithm. In the simulation of decomposing four representative signals and real ECG signals, compared with the existing exhaustive search method, the proposed schemes can achieve much higher computation speed with a fast energy convergence, that is, in particular, to make the AFD possible for real-time applications.

show abstract

Section: Efficient Implementation Of Basis Search For Afd 211 Maximmentioning

confidence: 99%