Wavelet transform-based methods for denoising of Coulter counter signals

Abstract

A process based on discrete wavelet transforms is developed for denoising and baseline correction of measured signals from Coulter counters. Given signals from a particular Coulter counting experiment, which detect passage of particles through a fluid-filled microchannel, the process uses a cross-validation procedure to pick appropriate parameters for signal denoising; these parameters include the choice of the particular wavelet, the number of levels of decomposition, the threshold value and the threshold strategy. The process is demonstrated on simulated and experimental single channel data obtained from a particular multi-channel Coulter counter processing. For these example experimental signals from 20 µm polymethacrylate and Cottonwood/Eastern Deltoid pollen particles and the simulated signals, denoising is aimed at removing Gaussian white noise, 60 Hz power line interference and low frequency baseline drift. The process can be easily adapted for other Coulter counters and other sources of noise. Overall, wavelets are presented as a tool to aid in accurate detection of particles in Coulter counters.

Electronic-nose for detecting environmental pollutants: signal processing and analog front-end design

“…DWT based denoising techniques for biosensors have been discussed in [27]. Denoising using the DWT is a nonlinear operation that involves the following steps:…”

Signal Processing Methods for Capillary Electrophoresis

“…Wavelet denoising strategies were also used (Ceballos et al 2008) on pattern recognition in CE. Similar wavelet based denoising strategies to those cited above have also been applied to liquid chromatography data (Barclay and Bonner 1997;Shao et al 2004), Raman spectroscopy (Hu et al 2007), mass spectrometry data (Barclay and Bonner 1997;Coombes et al 2005), as well as numerous other areas of research (Jagtiani et al 2008;Komsta 2009). The DWT is sufficient in many scenarios when removing high and low frequency noise from signals.…”

“…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). In addition to these performance indicators, an analysis of an algorithm's computational complexity (Arora and Barak 2009) would also be worth reporting.…”

Analyte detection using an ion-channel sensor array

“…We used the biorthogonal wavelet (bior3.7) with 3 levels of decomposition for preprocessing the data. These parameters were obtained using cross-validation method as described in [14]. Soft thresholding was used and the limit was chosen using the universal threshold [15].…”