scholarly journals Detection of Abnormal High-Frequency Components in the QRS Complex by the Wavelet Transform in Patients With Idiopathic Dilated Cardiomyopathy

1999 ◽  
Vol 63 (1) ◽  
pp. 25-32 ◽  
Author(s):  
Kazuhira Maehara ◽  
Toshinori Kokubun ◽  
Naoyuki Awano ◽  
Kayano Taira ◽  
Masahiro Ono ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Dilated Cardiomyopathy ◽  
High Frequency ◽  
Idiopathic Dilated Cardiomyopathy ◽  
Qrs Complex ◽  
Frequency Components

scholarly journals Abnormal high-frequency components in the QRS complex detected by the wavelet transform in Dahl salt-sensitive hypertensive rats.

2000 ◽  
Vol 20 (1) ◽  
pp. 11-19
Author(s):  
Kazuhira Maehara ◽  
Hiromichi Hashimoto ◽  
Osamu Nemoto ◽  
Kenichi Kamei ◽  
Reiko Matsuoka ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
High Frequency ◽  
Qrs Complex ◽  
Hypertensive Rats ◽  
Frequency Components

scholarly journals Electrocardiography Subtraction Method of Detecting Low-Amplitude, High-Frequency Components (L-HFCs) Within the QRS Complex of Patients With Ventricular Tachycardia

2002 ◽  
Vol 66 (7) ◽  
pp. 649-649
Author(s):  
Toshihiko Nanke ◽  
Kiyoshi Nakazawa ◽  
Mariko Arai ◽  
Shounosuke Ryu ◽  
Tsuneharu Sakurai ◽  
...  
Keyword(s):  
Ventricular Tachycardia ◽  
High Frequency ◽  
Qrs Complex ◽  
Subtraction Method ◽  
Frequency Components ◽  
Low Amplitude

scholarly journals Assessing the Contribution of the Oscillatory Potentials to the Genesis of the Photopic ERG with the Discrete Wavelet Transform

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Mathieu Gauvin ◽  
Allison L. Dorfman ◽  
Nataly Trang ◽  
Mercedes Gauthier ◽  
John M. Little ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Normal Subjects ◽  
Oscillatory Potentials ◽  
Discrete Wavelet ◽  
Wide Range ◽  
Frequency Components ◽  
Photopic Erg

The electroretinogram (ERG) is composed of slow (i.e., a-, b-waves) and fast (i.e., oscillatory potentials: OPs) components. OPs have been shown to be preferably affected in some diseases (such as diabetic retinopathy), while the a- and b-waves remain relatively intact. The purpose of this study was to determine the contribution of OPs to the building of the ERG and to examine whether a signal mostly composed of OPs could also exist. DWT analyses were performed on photopic ERGs (flash intensities: −2.23 to 2.64 log cd·s·m−2in 21 steps) obtained from normal subjects (n=40) and patients (n=21) affected with a retinopathy. In controls, the %OP value (i.e., OPs energy/ERG energy) is stimulus- and amplitude-independent (range: 56.6–61.6%; CV = 6.3%). In contrast, the %OPs measured from the ERGs of our patients varied significantly more (range: 35.4%–89.2%;p<0.05) depending on the pathology, some presenting with ERGs that are almost solely composed of OPs. In conclusion, patients may present with a wide range of %OP values. Findings herein also support the hypothesis that, in certain conditions, the photopic ERG can be mostly composed of high-frequency components.

scholarly journals Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Priyadharsini Ravisankar
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Stationary Wavelet Transform ◽  
Acoustic Image ◽  
Underwater Acoustic ◽  
Acoustic Images ◽  
Frequency Components ◽  
Shrinkage Functions

Underwater acoustic images are captured by sonar technology which uses sound as a source. The noise in the acoustic images may occur only during acquisition. These noises may be multiplicative in nature and cause serious effects on the images affecting their visual quality. Generally image denoising techniques that remove the noise from the images can use linear and non-linear filters. In this paper, wavelet based denoising method is used to reduce the noise from the images. The image is decomposed using Stationary Wavelet Transform (SWT) into low and high frequency components. The various shrinkage functions such as Visushrink and Sureshrink are used for selecting the threshold to remove the undesirable signals in the low frequency component. The high frequency components such as edges and corners are retained. Then the inverse SWT is used for reconstruction of denoised image by combining the modified low frequency components with the high frequency components. The performance measure Peak Signal to Noise Ratio (PSNR) is obtained for various wavelets such as Haar, Daubechies,Coiflet and by changing the thresholding methods.

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