A continuous wavelet transform algorithm for peak detection

Abstract

Contactless conductivity detector technology has unique advantages for microfluidic applications. However, the low S/N and varying baseline makes the signal analysis difficult. In this paper, a continuous wavelet transform-based peak detection algorithm was developed for CE signals from microfluidic chips. The Ridger peak detection algorithm is based on the MassSpecWavelet algorithm by Du et al. [Bioinformatics 2006[Bioinformatics , 22, 2059[Bioinformatics -2065, and performs a continuous wavelet transform on data, using a wavelet proportional to the first derivative of a Gaussian function. It forms sequences of local maxima and minima in the continuous wavelet transform, before pairing sequences of maxima to minima to define peaks. The peak detection algorithm was tested against the Cromwell, MassSpecWavelet, and Linear Matrix-assisted laser desorption/ionizationtime-of-flight-mass spectrometer Peak Indication and Classification algorithms using experimental data. Its sensitivity to false discovery rate curve is superior to other techniques tested.

Design rationale and performance evaluation of Wavelet Health Wristband: bench-top validation of a wrist-worn physiological signal recorder (Preprint)

“…RR is extracted by both time-domain algorithm analysis of the baseline wander, and the frequency-domain algorithm analysis of frequency modulation. The time-domain algorithm leverages a continuous wavelet transform to detect inspiration and expiration phases and measure the RR rate [47]. The frequency domain method also leverages multiple continuous wavelet transformations along with a dynamic programming ridge detection scheme to estimate respiration rate [48].…”

Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra

“…To deal with the problem, the continuous wavelet transform (CWT) was applied for each of the analysed 215 spectra separately (Figure 1b). Thanks to the CWT properties, the true signal can be successfully separated from the low frequency background (mainly attributed to the fluorescence effect) [27]. The baseline drift of each individual spectrum was removed using the Mexican hat wavelet [22] with the same set of CWT parameters for each spectrum (Fig.…”

Signal Processing Methods for Capillary Electrophoresis

“…However, little effort has been devoted to the comparison of performance from these different methods in spite of the few published work (Barclay and Bonner 1997;Cruz-Marcelo et al 2008;Wee et al 2008;Yang et al 2009). In order to select or develop most efficient method, comparative studies are needed to test the methods on the same data (Leptos et al 2006).…”

“…For example, peak detection algorithms could be assessed by their false discovery rate (FDR) and sensitivity (CruzMarcelo et al 2008;Wee et al 2008;Yang et al 2009) or receiver operating characteristic (ROC) curve (Mantini et al 2007). For assessing noise removal efficiency or for evaluating the preservation of peak properties after peak resolution, different measures that might be used include: root square error (RSE) or integrated square error (ISE) (Jagtiani et al 2008), root mean square (RMS) (Barclay and Bonner 1997), relative error (RE) (Zhang et al 2001;Zheng et al 1998), individual sum of squared residuals (Vivó-Truyols et al 2005b), signal-tonoise ratio (SNR) and correlation coefficient (Jakubowska and Kubiak 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). CE is based on the difference of the electrical mobilities of molecules within a capillary tube filled with electrolyte solution.…”