A novel spectral representation of electromyographic signals

被引:8
|
作者
Andrade, AO [1 ]
Kyberd, PJ [1 ]
Taffler, SD [1 ]
机构
[1] Univ Reading, Dept Cybernet, Reading RG6 2AH, Berks, England
关键词
D O I
10.1109/IEMBS.2003.1280447
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Time/frequency and temporal analyses have been widely used in biomedical signal processing. These methods represent important characteristics of a signal in both time and frequency domain. In this way, essential features of the signal can be viewed and analysed in order to understand or model the physiological system. Historically, Fourier spectral analyses have provided a general method for examining the global energy/frequency distributions. However, an assumption inherent to these methods is the stationarity of the signal. As a result, Fourier methods are not generally an appropriate approach in the investigation of signals with transient components. This work presents the application of a new signal processing technique, empirical mode decomposition and the Hilbert spectrum, in the analysis of electromyographic signals. The results show that this method may provide not only an increase in the spectral resolution but also an insight into the underlying process of the muscle contraction.
引用
收藏
页码:2598 / 2601
页数:4
相关论文
共 50 条
  • [21] Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation
    Humbert, Pierre
    Le Bars, Batiste
    Oudre, Laurent
    Kalogeratos, Argyris
    Vayatis, Nicolas
    JOURNAL OF MACHINE LEARNING RESEARCH, 2021, 22
  • [22] Simulation of stochastic signals for FSO communication systems through spectral representation
    de Oliveira, Jose Paulo G.
    ATMOSPHERIC PROPAGATION X, 2013, 8732
  • [23] Music Genre Classification Using Spectral Analysis and Sparse Representation of the Signals
    Banitalebi-Dehkordi, Mehdi
    Banitalebi-Dehkordi, Amin
    JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY, 2014, 74 (02): : 273 - 280
  • [24] Towards a Novel Data Representation for Classifying Acoustic Signals
    Thomas, Mark
    ADVANCES IN ARTIFICIAL INTELLIGENCE, 2019, 11489 : 601 - 604
  • [25] Cross spectral analysis of the force and surface electromyographic signals for examining steadiness following different exercise interventions
    Beck, Travis W.
    Ye, Xin
    Wages, Nathan P.
    Carr, Joshua C.
    BIOMEDICAL PHYSICS & ENGINEERING EXPRESS, 2016, 2 (04):
  • [26] Influence of muscle fibre shortening on estimates of conduction velocity and spectral frequencies from surface electromyographic signals
    Schulte, E
    Farina, D
    Merletti, R
    Rau, G
    Disselhorst-Klug, C
    MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2004, 42 (04) : 477 - 486
  • [27] MULTIRESOLUTION SEGMENTATION OF RESPIRATORY ELECTROMYOGRAPHIC SIGNALS
    CHOI, HG
    PRINCIPE, JC
    HUTCHINSON, AA
    WOZNIAK, JA
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 1994, 41 (03) : 257 - 266
  • [28] Separation of Superimposed Electrocardiographic and Electromyographic Signals
    Sbrollini, A.
    Agostinelli, A.
    Morettini, M.
    Verdini, F.
    Di Nardo, F.
    Fioretti, S.
    Burattini, L.
    EMBEC & NBC 2017, 2018, 65 : 518 - 521
  • [29] Influence of muscle fibre shortening on estimates of conduction velocity and spectral frequencies from surface electromyographic signals
    E. Schulte
    D. Farina
    R. Merletti
    G. Rau
    C. Disselhorst-Klug
    Medical and Biological Engineering and Computing, 2004, 42 : 477 - 486
  • [30] KONTRAKTION Sonification of Metagestures with electromyographic Signals
    Weber, Maximilian
    Kuhn, Marco
    PROCEEDINGS OF AUDIO MOSTLY 2016 - A CONFERENCE ON INTERACTION WITH SOUND IN COOPERATION WITH ACM, 2016, : 132 - 138