Ventricular late potentials characterization in time-frequency domain by means of a wavelet transform

1994 ◽  
Vol 41 (7) ◽  
pp. 625-634 ◽  
Author(s):  
O. Meste ◽  
H. Rix ◽  
P. Caminal ◽  
N.V. Thakor
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Late Potentials ◽  
Ventricular Late Potentials ◽  
Time Frequency

scholarly journals A Method for Ventricular Late Potentials Detection Using Time-Frequency Representation and Wavelet Denoising

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Matteo Gadaleta ◽  
Agostino Giorgio
Keyword(s):  
High Resolution ◽  
Frequency Domain ◽  
Real Time ◽  
Frequency Analysis ◽  
Wavelet Denoising ◽  
Late Potentials ◽  
Time Frequency Analysis ◽  
Ventricular Late Potentials ◽  
Time Frequency ◽  
Frequency Representation

This study proposes a method for ventricular late potentials (VLPs) detection using time-frequency representation and wavelet denoising in high-resolution electrocardiography (HRECG). The analysis is performed both with the signal averaged electrocardiography (SAECG) and in real time. A comparison between the temporal and the time-frequency analysis is also reported. In the first analysis the standard parameters QRSd, LAS40, and RMS40 were used; in the second normalized energy in time-frequency domain was calculated. The algorithm was tested adding artificial VLPs to real ECGs.

Reflection technique in time‐frequency domain using multicomponent acoustic emission signals and application to geothermal reservoirs

Geophysics ◽  
2002 ◽  
Vol 67 (3) ◽  
pp. 928-938 ◽  
Author(s):  
Nobukazu Soma ◽  
Hiroaki Niitsuma ◽  
Roy Baria
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Random Noise ◽  
Spherical Shape ◽  
Source Distribution ◽  
S Wave ◽  
Spectral Matrix ◽  
Time Frequency ◽  
Reflected Wave

We have developed a reflection technique for estimating deep geothermal reservoir structures using acoustic emission signals as a source, which is useful when there is no proper estimating technique because of high temperature, high pressure, and great depth. Because its resolution is not high enough for comparison with methods such as well logging, we have enhanced the technique by developing a time–frequency‐domain analysis of multicomponent acoustic emission signals using a wavelet transform. The reflected wave is separated from an incoherent coda by analyzing the shape of a 3‐D hodogram: a linear shape indicates the arrival of a coherent signal such as a reflected wave, and an incoherent signal such as a coda makes a spherical shape. We construct a spectral matrix of 3‐D particle motion using a wavelet transform, as is done in a time–frequency domain. We evaluate the linearity of the 3‐D hodogram for each time and frequency by using the eigenvalues of the spectral matrix. Three‐dimensional inversion of the distribution of hodogram linearity in the time–frequency domain lets us image the deep subsurface structure. The inversion is based on the diffraction stack. We reduce the uncertainties by investigating S‐wave polarization direction, and we compensate for inhomogeneous source distribution to get reliable estimates with high resolution. We then evaluate our methods with synthetic signals. We discriminate a coherent wave from incoherent random noise in the presence of an S/N ratio of −3.7 dB and detect reflectors at correct depths with a small number of detectors. We apply the method to data from the European hot, dry rock site in Soultz‐sous‐Forêts, France, and compare our estimates with those from a number of borehole observations. The detected reflectors agree with the location of fracture zones. We demonstrate the feasibility of the method for detecting reflectors at great depths.

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