An improved algorithm for peak detection in mass spectra based on continuous wavelet transform

The R peak detection algorithm is a necessary tool for monitoring and diagnosing the cardiovascular disease. This paper presents the R peak detection algorithm based on continuous wavelet transform (CWT) and Shannon energy. We evaluate the proposed algorithm with the 48 record of ECG data from MIT-BIH arrhythmia database. Results show that the proposed algorithm gives very good DER (0.48%-0.50%) compared to those from previous publications (0.168%-0.87%). We demonstrated that the use of the CWT with a single scaling parameter is capable of removing noises. In addition, we found that Shannon energy cannot improve the DER value but it can highlight the R peak from the low QRS complex in ECG beat leading to the improvement in the robustness of the R peak detection algorithm.

Download Full-text

Detection and Analysis of Power Quality Disturbances using Continuous Wavelet Transform and Discrete Wavelet Transform Coefficients

i-manager s Journal on Power Systems Engineering ◽

10.26634/jps.1.3.2465 ◽

2013 ◽

Vol 1 (3) ◽

pp. 28-35

Author(s):

S. Elvin Richards ◽

◽

M. Sai Veerraju ◽

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Power Quality ◽

Continuous Wavelet Transform ◽

Discrete Wavelet ◽

Continuous Wavelet ◽

Transform Coefficients ◽

Power Quality Disturbances

Download Full-text

The continuous wavelet transform with rotations

Sampling Theory, Signal Processing, and Data Analysis ◽

10.1007/bf03549424 ◽

2005 ◽

Vol 4 (1) ◽

pp. 45-55

Author(s):

Jaime Navarro ◽

Miguel Angel Alvarez

Keyword(s):

Wavelet Transform ◽

Continuous Wavelet Transform ◽

Continuous Wavelet

Download Full-text

Some uncertainty inequalities for the continuous wavelet transform

Journal of Pseudo-Differential Operators and Applications ◽

10.1007/s11868-021-00388-w ◽

2021 ◽

Vol 12 (2) ◽

Author(s):

Fatima Alhajji ◽

Saifallah Ghobber

Keyword(s):

Wavelet Transform ◽

Continuous Wavelet Transform ◽

Continuous Wavelet ◽

Uncertainty Inequalities

Download Full-text

Development of rapid and simple spectrophotometric method for the simultaneous determination of anti-parkinson drugs in combined dosage form using continuous wavelet transform and radial basis function neural network

Optik ◽

10.1016/j.ijleo.2021.167088 ◽

2021 ◽

pp. 167088

Author(s):

Shaghayegh Ghadimloozadeh ◽

Mahmoud Reza Sohrabi ◽

Hassan Kabiri Fard

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Radial Basis Function ◽

Simultaneous Determination ◽

Spectrophotometric Method ◽

Basis Function ◽

Continuous Wavelet Transform ◽

Dosage Form ◽

Continuous Wavelet

Download Full-text

Continuous Wavelet Transform of Schwartz Distributions in DL2′(Rn), n ≥ 1

Symmetry ◽

10.3390/sym13071106 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1106

Author(s):

Jagdish N. Pandey

Keyword(s):

Wavelet Transform ◽

Function Space ◽

Continuous Wavelet Transform ◽

Inversion Formula ◽

Continuous Wavelet ◽

The Real ◽

Schwartz Distributions ◽

Distributional Sense ◽

Wavelet Inversion

We define a testing function space DL2(Rn) consisting of a class of C∞ functions defined on Rn, n≥1 whose every derivtive is L2(Rn) integrable and equip it with a topology generated by a separating collection of seminorms {γk}|k|=0∞ on DL2(Rn), where |k|=0,1,2,… and γk(ϕ)=∥ϕ(k)∥2,ϕ∈DL2(Rn). We then extend the continuous wavelet transform to distributions in DL2′(Rn), n≥1 and derive the corresponding wavelet inversion formula interpreting convergence in the weak distributional sense. The kernel of our wavelet transform is defined by an element ψ(x) of DL2(Rn)∩DL1(Rn), n≥1 which, when integrated along each of the real axes X1,X2,…Xn vanishes, but none of its moments ∫Rnxmψ(x)dx is zero; here xm=x1m1x2m2⋯xnmn, dx=dx1dx2⋯dxn and m=(m1,m2,…mn) and each of m1,m2,…mn is ≥1. The set of such wavelets will be denoted by DM(Rn).

Download Full-text