Capillary electrophoresis (CE) peak detection using a wavelet transform technique

Stewart, Robert L.; Wee, Andrew Thye Shen; Grayden, David B.; Zhu, Yonggang

doi:10.1117/12.813449

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…where A i is the peak's amplitude, μ i is the migration time, W i is the width of the peak and T s is the sampling interval. In addition, the triangle model [32], resonance model [33] and other models were also proposed to describe the peaks present in the CE signal. For the noise component n(t), it is usually assumed as pure white noise, which is independent and identically following a zeromean Gaussian distribution or uniform random noise.…”

Section: Signal Modelmentioning

confidence: 99%

“…For example, a Gaussian‐like model proposed in [4] is

p_{i} false(t false) = A_{i} \exp ()(, - 4 {()(, \frac{T_{normals} t - μ_{i}}{W_{i}})}^{2})

where

A_{i}

is the peak's amplitude,

μ_{i}

is the migration time,

W_{i}

is the width of the peak and

T_{normals}

is the sampling interval. In addition, the triangle model [32], resonance model [33] and other models were also proposed to describe the peaks present in the CE signal.…”

Section: Signal Model and Denoising Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Denoising method for capillary electrophoresis signal via learned tight frame

Wang

Gao

et al. 2020

IET signal process.

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Since capillary electrophoresis (CE) signals are always contaminated by random noise, which has negative influence on the accuracy of detection and analysis, it is necessary to remove noise before further applications of the CE signals. In this study, a tight frame learned from the data itself is applied to the removal of noise for CE signals. To achieve an effective decomposition of the CE signal, a one-dimensional discrete tight frame tailored to the input signal is first constructed by introducing tight frame constraint into the popular dictionary learning model. Then, due to each subband containing different information of the noise, an adaptive threshold is computed to shrink the detail coefficients instead of using a global threshold. Finally, the denoised CE signal is reconstructed from the thresholded coefficients by using the inverse transform of the tight frame. To evaluate the denoising efficiency, the proposed method is applied to the simulated CE signals and real CE signals. Experimental results indicate that compared with other denoising methods, the proposed method obtains a better shape preservation of the peaks as well as a higher signal-to-noise ratio.

show abstract

Section: Signal Modelmentioning

confidence: 99%

“…For example, a Gaussian‐like model proposed in [4] is

p_{i} false(t false) = A_{i} \exp ()(, - 4 {()(, \frac{T_{normals} t - μ_{i}}{W_{i}})}^{2})

where

A_{i}

is the peak's amplitude,

μ_{i}

is the migration time,

W_{i}

is the width of the peak and

T_{normals}

is the sampling interval. In addition, the triangle model [32], resonance model [33] and other models were also proposed to describe the peaks present in the CE signal.…”

Section: Signal Model and Denoising Algorithmmentioning

confidence: 99%

Denoising method for capillary electrophoresis signal via learned tight frame

Wang

Gao

et al. 2020

IET signal process.

View full text Add to dashboard Cite

show abstract

“…The triangle is likely to be the earliest peak model used (Dyson 1998) and is perhaps the simplest. It has been used as a peak model in a number of studies (Barclay and Bonner 1997;Stewart et al 2008). One definition for a triangle function is (Couch 1990),…”

Section: Peak Modelsmentioning

confidence: 99%

“…Capillary electrophoresis (CE) is a separation technique that can be used as a sample pretreatment step in the analysis of ionic analytes (Grossman and Colburn 1992;Stewart et al 2008). Compared with other separation technologies it can offer advantages such as higher speed and sensitivity, smaller injection volumes and reduced consumption of solvent and samples, the possibility of miniaturisation, and reduced cost (Issaq 2001;Jarméus and Emmer 2008;Polesello and Valsecchi 1999;Wang 2005;Wee et al 2008).…”

Section: Introductionmentioning

confidence: 99%