Fig 3 - uploaded by An Vo
Content may be subject to copyright.
Flowchart of envelope Analysis. Data can be decomposed into three envelope signals: Maximum envelope (MAX), Median envelope (MED), Minimum envelope (MIN).
Source publication
Mass Spectrometry (MS) is increasingly being used to discover diseases-related proteomic patterns. The peak detection step is one of the most important steps in the typical analysis of MS data. Recently, many new algorithms have been proposed to increase true position rate with low false discovery rate in peak detection. Most of them follow two app...
Context in source publication
Context 1
... 2: taking interpolation of signal obtained from step 1 so that MED will have the same length as y(t) as follows: MED = Interp(Med(y(t))). to classify or to detect some important information. Any finite energy signal y(t) can be analyzed into three envelope signals (Fig. 3) including MAX (M 11 ), MED (M 12 ), and MIN (M 13 ) at the first level. Each of three above envelope signals will be decomposed into three envelope signals at the second level and we get 3 2 = 9 envelope signals totally. This process is iterated and at the i th level, y(t) is decomposed into 3 i envelope signals from M i1 to M i3 i . ...
Similar publications
The application of Neural Networks to radar detection has many open questions and some of them are solved in this paper. First, we propose a network structure useful for the problem of binary detection. We model the input as signal and noise (given by its complex envelope), and the binary output are 1 or 0. We evaluate different structures and thei...
Citations
... One such popular application is the detection of the QRS complex in electrocardiogram (ECG) signals. Many different methods have been developed for detection of such events, which can be summarized as those based on wavelet transform [6], [8], [52], [54]- [57], traditional window-threshold techniques [58]- [60], Hilbert transform [61], combining Hilbert and wavelet transform [62], artificial neural networks [63], [64], techniques using templates [65], [66], morphology filtering [67], [68], nonlinear filtering [69]- [72], Kalman filtering [73], Gabor filtering [74], Gaussian second derivative filtering [75], linear prediction analysis [76], higher-order statistics [77], K-Means clustering [78], fuzzy C-Means clustering [79], Empirical Mode Decomposition [80], hidden Markov models [81], and techniques using histogram/cumulative distribution function [82], intensity weighted variance [83], stochastic resonance [84], or a smoothed nonlinear energy operator [85]. ...
In this paper, we propose a nonparametric approach that can be used in envelope extraction, peak-burst detection and clustering in time series. Our problem formalization results in a naturally defined splitting/forking of the time series. With a possibly hierarchical implementation, it can be used for various applications in machine learning, signal processing and mathematical finance. From an incoming input signal, our iterative procedure sequentially creates two signals (one upper bounding and one lower bounding signal) by minimizing the cumulative $L_1$ drift. We show that a solution can be efficiently calculated by use of a Viterbi-like path tracking algorithm together with an optimal elimination rule. We consider many interesting settings, where our algorithm has near-linear time complexities.
... Here, the Gabor filter has been used for signal denoising. The Gabor filter is a handy tool for signal analysis and has been applied to mass spectrometry for peak detection and envelope analysis [30]. A discrete 2f /1f signal X [k] could be expanded as a sequence in terms of a weighted collection of time-shifted and frequency-modulated elements through the Gabor transform: ...
This paper introduces a compact and portable sensor based on mid-infrared absorption spectroscopy for NO detection employing a room-temperature continuous wave (CW) distributed feedback quantum cascade laser (DFB-QCL) emitting at ${1900.08}\;{{\rm cm}^{- 1}}$ 1900.08 c m − 1 . A software-based digital signal generator and lock-in amplifier, in combination with the wavelength modulation spectroscopy (WMS) technique, were used for the concentration measurement of NO. In addition, a Gabor filter denoising method was developed to improve the performance of the measurement system. As a result, a minimum detection limit of 42 ppbv can be achieved at 3 s integration time, and a measurement precision of 450 ppbv can be reached with a time resolution of 0.1 s. The performance of the compact portable sensor was verified by a series of experiments, denoting great potential of field application for sensitive NO sensing.
... [10] In practice, however, the accuracy and repeatability of chemical fingerprints are influenced by noise (or background), retention time shifts, and different maximum absorption wavelengths used in the analysis, [11,12] leading to errors in the qualitative and quantitative results. A series of chemometric tools have been introduced to standardize the chromatographic signal by preprocessing to remove noise, [13,14] eliminating background, [15,16] correcting retention times, [17,18] and integrating multiwavelength chromatographic data. [19] These tools can be used to develop a practical method to accurately evaluate the chemical stability of XBJ. ...
... [14,31] Background was corrected with an expectation-maximization estimation, using a probabilistic model that estimated the baseline in small windows. [15,16] The variability in scaling among the wavelengths was minimized by normalizing each data vector (each chromatogram in the denoised and background-corrected matrix at four wavelengths), standardizing the area under the curve (AUC) to the group median, and adjusting the overall maximum intensity to 100. [32] The corrected matrix was then unfolded and folded in such a way that large data vectors replaced small data vectors to construct a single data vector with dimensions of 6000 � 1, namely multiwavelength fingerprints. ...
A strategy for monitoring and analyzing the chemical stability of Xuebijing injection (XBJ) by multi-wavelength chromatographic fingerprints and multivariate classification techniques is presented in this paper. Multi-wavelength chromatographic fingerprints were constructed using chromatographic data obtained at four wavelengths (260, 280, 320 and 400 nm). The raw chromatography data were preprocessed by noise reduction, baseline correction, data normalization, and interval correlation optimized shifting (icoshift). Using this method, fingerprints of 166 samples of XBJ subjected to different forced degradation conditions (irradiation, high temperature and a range of pH values) were properly represented. Forty-one chemical components were identified using the iPeak program. Additionally, the identified peak area profiling of chemical components were used for multivariate classification analysis. Principal component analysis (PCA) and Ward's method were used to classify different XBJ degradation samples. The PCA score plot showed that XBJ degradation samples were clustered into four groups, and the results are confirmed by Ward's method. Ten key chemical markers under different degradation conditions were found and identified by counterpropagation artificial neural networks (CP-ANN), statistical t-tests and UPLC-Q-TOF-MS. The results suggest that the proposed strategy could be successfully applied to the comprehensive analysis of complex chemical systems.
... Gabor filtering [38], Gaussian second derivative filtering [39], linear prediction analysis [40] and smoothed nonlinear energy operator [41]. ...
Peaks signify important events in a signal. In a pair of signals how peaks
are occurring with mutual correspondence may offer us significant insights into
the mutual interdependence between the two signals based on important events.
In this work we proposed a novel synchronization measure between two signals,
called peak synchronization, which measures the simultaneity of occurrence of
peaks in the signals. We subsequently generalized it to more than two signals.
We showed that our measure of synchronization is largely independent of the
underlying parameter values. A time complexity analysis of the algorithm has
also been presented. We applied the measure on intracranial EEG signals of
epileptic patients and found that the enhanced synchronization during an
epileptic seizure can be modeled better by the new peak synchronization measure
than the classical amplitude correlation method.
... The detection of peaks in signals is an important step in many signal processing applications. Up to now, many different methods have been developed, including those based on traditional window-threshold techniques [1][2][3][4], wavelet transform [5][6][7][8][9][10][11][12][13], Hilbert transform [14], combining Hilbert and wavelet transform [15], artificial neural networks [16,17], techniques using templates [18,19], morphology filtering [20][21][22], nonlinear filtering [23][24][25][26], Kalman filtering [27], Gabor filtering [28], Gaussian second derivative filtering [29], linear prediction analysis [30], higher-order statistics [31], K-Means clustering [32], fuzzy C-Means clustering [33], Empirical Mode Decomposition [34], hidden Markov models [35], and techniques using entropy [36], momentum [37], histogram/cumulative OPEN ACCESS distribution function [38], intensity weighted variance [39], stochastic resonance [40], or a smoothed nonlinear energy operator [41]. ...
We present a new method for automatic detection of peaks in noisy periodic and quasi-periodic signals. The new method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima. The usefulness of the proposed method is shown by applying the AMPD algorithm to simulated and real-world signals.
... Thus, in this article, instead of SNR, frequency response, height and SD of Gaussian peaks calculated by zero-crossing in Gaussian derivative wavelet domain are used to remove false peaks. In order to improve sensitivity, the Envelope analysis (Nguyen et al., 2009) is also used to save some important peaks with small energy. ...
... Since smoothing performed in denoising step only reduces noise and keeps small true peaks, multi-scales of Gaussian derivative wavelet are used to make local maxima and minima more robust to noise instead of only one Gaussian filter in Nguyen et al. (2009). The wavelet transform can be written as convolution product in (4): ...
... Envelope analysis was introduced by Nguyen et al. (2009). Any finite energy signal y(t) can be analyzed into three envelope signals including MAX, MIN and MED envelops at the first level. ...
Peaks are the key information in mass spectrometry (MS) which has been increasingly used to discover diseases-related proteomic patterns. Peak detection is an essential step for MS-based proteomic data analysis. Recently, several peak detection algorithms have been proposed. However, in these algorithms, there are three major deficiencies: (i) because the noise is often removed, the true signal could also be removed; (ii) baseline removal step may get rid of true peaks and create new false peaks; (iii) in peak quantification step, a threshold of signal-to-noise ratio (SNR) is usually used to remove false peaks; however, noise estimations in SNR calculation are often inaccurate in either time or wavelet domain. In this article, we propose new algorithms to solve these problems. First, we use bivariate shrinkage estimator in stationary wavelet domain to avoid removing true peaks in denoising step. Second, without baseline removal, zero-crossing lines in multi-scale of derivative Gaussian wavelets are investigated with mixture of Gaussian to estimate discriminative parameters of peaks. Third, in quantification step, the frequency, SD, height and rank of peaks are used to detect both high and small energy peaks with robustness to noise.
We propose a novel Gaussian Derivative Wavelet (GDWavelet) method to more accurately detect true peaks with a lower false discovery rate than existing methods. The proposed GDWavelet method has been performed on the real Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight (SELDI-TOF) spectrum with known polypeptide positions and on two synthetic data with Gaussian and real noise. All experimental results demonstrate that our method outperforms other commonly used methods. The standard receiver operating characteristic (ROC) curves are used to evaluate the experimental results.
http://ranger.uta.edu/~heng/MS/GDWavelet.html or http://www.naaan.org/nhanguyen/archive.htm.
... Peak selection, also known as model selection, stands for the identification of spectral components in a measured sample, i.e. the number of addends in (2.12). As far as frequency-domain methods are concerned, they are commonly found based on the occurrence of local maxima and applying a height threshold afterwards, a strategy which is often proposed in the context of spectroscopic data analysis (Yasui et al., 2003;Jarvi et al., 1997;Koradi et al., 1998;Moseley et al., 2004;Nguyen et al., 2009;Dijkstra et al., 2006). By this, the identification of overlapped Lorentz functions called "shoulders" is not well supported, which will be discussed in more detail in Section 3.3.2. ...
