Effectiveness analysis of bio-electronic stimulation therapy to Parkinson’s diseases via joint singular spectrum analysis and discrete fourier transform approach

Lin, Yuxin; Ling, Bingo Wing‐Kuen; Xu, Nuo; Lam, Regan; Ho, Charlotte Yuk-Fan

doi:10.1016/j.bspc.2020.102131

Cited by 14 publications

(9 citation statements)

References 13 publications

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“…The SSA has various applications, such as denoising signals, extracting the underlying trend, and forecasting applications [26]. The SSA approach comprises two interrelated stages, i.e., decomposition and reconstruction, and each comprises two steps [27].…”

Section: Singular Spectrum Analysis (Ssa)mentioning

confidence: 99%

“…The first stage involves decomposing the time series into simple and meaningful signals through embedding succeeded by SVD [18]. The second stage includes reconstructing the series for forecasting purposes via grouping and diagonal averaging [26]. This section summarizes the different stages and parameters selection criteria of the SSA.…”

Section: Singular Spectrum Analysis (Ssa)mentioning

confidence: 99%

“…Grouping involves categorizing the elementary matrices, Xi, according to their eigentriples into groups. Then the matrices inside each group are summed up [26,27]. Let g = {i1, i2, …, ir} be a group of r selected eigentriples; hence, the matrix Xg for group g is expressed as:…”

Section: Reconstructionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting

Fathi¹,

El-Khodary²,

Saafan³

2021

RIA

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The primary purpose of trading in stock markets is to profit from buying and selling listed stocks. However, numerous factors can influence the stock prices, such as the company's present financial situation, news, rumor, macroeconomics, psychological, economic, political, and geopolitical factors. Consequently, tremendous challenges already exist in predicting noisy stock prices. This paper proposes a hybrid model integrating the singular spectrum analysis (SSA) and the backpropagation neural network (BPNN) to forecast daily closing prices in stock markets. The model first decomposes the stock prices into several components using the SSA. Then, the extracted components are utilized for training BPNNs to forecast future prices. Compared with the BPNN, the hybrid SSA-BPNN model demonstrates a better predictive performance, indicating the SSA's ability to extract hidden information and reduce the noise effect of the original time series.

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Section: Singular Spectrum Analysis (Ssa)mentioning

confidence: 99%

Section: Singular Spectrum Analysis (Ssa)mentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting

Fathi¹,

El-Khodary²,

Saafan³

2021

RIA

View full text Add to dashboard Cite

show abstract

“…It is a polynomial product and a fast algorithm for a discrete Fourier transform (DFT). The advantage of this method is that its time complexity is lower than that of DFT, thus reducing the computation time, (21) and the method is convenient and easy to operate when processing signals.…”

Section: Fftmentioning

confidence: 99%

Bridge Damage Identification by Ground-based Synthetic Aperture Radar Using Blind Source Separation and Noise Reduction Technology

Cheng¹,

Chen²,

Wang³

et al. 2020

Sensors and Materials

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“…It is worth noting that the singular spectrum analysis has a characteristic that the singular spectrum analysis components corresponding to the large singular values have the higher importance than those corresponding to the small singular values. This is because the singular spectrum analysis components corresponding to the small singular values usually refer to the noise components [20]. Hence, the conventional method for selecting the singular spectrum analysis components is to determine the singular spectrum analysis index such that the reconstructed signal is the sum from the singular spectrum analysis component with the largest singular value to the determined singular spectrum analysis component [21,22].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Enhancement by Two Dimensional Quaternion Valued Singular Spectrum Analysis for Object Recognition

Lin

Ling

et al. 2021

Remote Sensing

Self Cite

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This paper proposes a two dimensional quaternion valued singular spectrum analysis based method for enhancing the hyperspectral image. Here, the enhancement is for performing the object recognition, but neither for improving the visual quality nor suppressing the artifacts. In particular, the two dimensional quaternion valued singular spectrum analysis components are selected in such a way that the ratio of the interclass separation to the intraclass separation of the pixel vectors is maximized. Next, the support vector machine is employed for performing the object recognition. Compared to the conventional two dimensional real valued singular spectrum analysis based method where only the pixels in a color plane is exploited, the two dimensional quaternion valued singular spectrum analysis based method fuses four color planes together for performing the enhancement. Hence, both the spatial information among the pixels in the same color plane and the spectral information among various color planes are exploited. The computer numerical simulation results show that the overall classification accuracy based on our proposed method is higher than the two dimensional real valued singular spectrum analysis based method, the three dimensional singular spectrum analysis based method, the multivariate two dimensional singular spectrum analysis based method, the median filtering based method, the principal component analysis based method, the Tucker decomposition based method and the hybrid spectral convolutional neural network (hybrid SN) based method.

show abstract

Effectiveness analysis of bio-electronic stimulation therapy to Parkinson’s diseases via joint singular spectrum analysis and discrete fourier transform approach

Cited by 14 publications

References 13 publications

A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting

A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting

Bridge Damage Identification by Ground-based Synthetic Aperture Radar Using Blind Source Separation and Noise Reduction Technology

Hyperspectral Image Enhancement by Two Dimensional Quaternion Valued Singular Spectrum Analysis for Object Recognition

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