Literature DB >> 7503456

Wavelets in biomedical engineering.

M Akay1.   

Abstract

Wavelets analysis methods have been widely used in the signal processing of biomedical signals. These methods represent the temporal characteristics of a signal by its spectral components in the frequency domain. In this way, important features of the signal can be extracted in order to understand or model the physiological system. This paper reviews the widely used orthogonal wavelet transform method in the biomedical applications.

Mesh：

Year:  1995        PMID： 7503456     DOI： 10.1007/bf02584453

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN： 0090-6964            Impact factor:   3.934


  4 in total

1.  Wavelet analysis of high-resolution signal-averaged ECGs in postinfarction patients.

Authors:  D Morlet; F Peyrin; P Desseigne; P Touboul; P Rubel
Journal:  J Electrocardiol       Date:  1993-10       Impact factor: 1.438

2.  Time-frequency digital filtering based on an invertible wavelet transform: an application to evoked potentials.

Authors:  O Bertrand; J Bohorquez; J Pernier
Journal:  IEEE Trans Biomed Eng       Date:  1994-01       Impact factor: 4.538

3.  Time-frequency analysis of the electrocortical activity during maturation using wavelet transform.

Authors:  M Akay; Y M Akay; P Cheng; H H Szeto
Journal:  Biol Cybern       Date:  1994       Impact factor: 2.086

4.  Multiresolution wavelet analysis of evoked potentials.

Authors:  N V Thakor; X R Guo; Y C Sun; D F Hanley
Journal:  IEEE Trans Biomed Eng       Date:  1993-11       Impact factor: 4.538
  4 in total
  10 in total

1.  Application of a wavelet adaptive filter to minimise distortion of the ST-segment.

Authors:  K L Park; K J Lee; H R Yoon
Journal:  Med Biol Eng Comput       Date:  1998-09       Impact factor: 2.602

2.  Design of a wavelet interpolation filter for enhancement of the ST-segment.

Authors:  K L Park; M J Khil; B C Lee; K S Jeong; K J Lee; H R Yoon
Journal:  Med Biol Eng Comput       Date:  2001-05       Impact factor: 2.602

3.  A comparison of analytical methods for the study of fractional Brownian motion.

Authors:  R Fischer; M Akay
Journal:  Ann Biomed Eng       Date:  1996 Jul-Aug       Impact factor: 3.934

4.  Estimation of habituation and signal-to-noise ratio of cortical evoked potentials to oesophageal electrical and mechanical stimulation.

Authors:  M V Kamath; G Tougas; S Hollerbach; R Premji; D Fitzpatrick; G Shine; A R Upton
Journal:  Med Biol Eng Comput       Date:  1997-07       Impact factor: 2.602

5.  Ideal filtering approach on DCT domain for biomedical signals: index blocked DCT filtering method (IB-DCTFM).

Authors:  Hang Sik Shin; Chungkeun Lee; Myoungho Lee
Journal:  J Med Syst       Date:  2009-04-30       Impact factor: 4.460

6.  Muscle fatigue detection in EMG using time-frequency methods, ICA and neural networks.

Authors:  Abdulhamit Subasi; M Kemal Kiymik
Journal:  J Med Syst       Date:  2009-04-28       Impact factor: 4.460

7.  Risk evaluation of ventricular tachycardia using wavelet transform irregularity of the high-resolution electrocardiogram.

Authors:  P Lewandowski; O Meste; R Maniewski; T Mroczka; K Steinbach; H Rix
Journal:  Med Biol Eng Comput       Date:  2000-11       Impact factor: 2.602

8.  Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics.

Authors:  Yuhan Li; Ke Li; Shaofan Wang; Xiaodan Chen; Dongsheng Wen
Journal:  Biosensors (Basel)       Date:  2022-06-12

9.  Hardware design and implementation of a wavelet de-noising procedure for medical signal preprocessing.

Authors:  Szi-Wen Chen; Yuan-Ho Chen
Journal:  Sensors (Basel)       Date:  2015-10-16       Impact factor: 3.576

10.  Can Wavelet Denoising Improve Motor Unit Potential Template Estimation?

Authors:  Hasanzadeh S H; Parsaei H; Movahedi M M
Journal:  J Biomed Phys Eng       Date:  2020-04-01
  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.