Characterization of surface EMG signals using improved approximate entropy.

被引：26
|
作者：
Chen W.T. [1 ]
Wang Z.Z. [1 ]
Ren X.M. [1 ]
机构：
[1] Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai
来源：
Journal of Zhejiang University SCIENCE B | 2006年 / 7卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Surface EMG (sEMG) signal; Nonlinear analysis; Approximate entropy (ApEn); Fractal dimension; R318.04;
D O I：
10.1631/jzus.2006.B0844
中图分类号：
学科分类号：
摘要：
An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accurately. The method introduced here can also be applied to other medium-sized and noisy physiological signals.
引用
收藏
页码：844 / 848
页数：4
相关论文
共 50 条
  • [41] A Japanese Input Method for Mobile Terminals Using Surface EMG Signals
    Hatano, Akira
    Araki, Kenji
    Matsuhara, Masafumi
    NEW FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2009, 5447 : 5 - +
  • [42] Hand Motion Recognition with Postural Changes using Surface EMG Signals
    Matsubara, Takamitsu
    Sugimoto, Kenji
    2015 54TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE), 2015, : 1101 - 1104
  • [43] An efficient method for analysis of EMG signals using improved empirical mode decomposition
    Mishra, Vipin K.
    Bajaj, Varun
    Kumar, Anil
    Sharma, Dheeraj
    Singh, G. K.
    AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS, 2017, 72 : 200 - 209
  • [44] On the Use of Fuzzy and Permutation Entropy in Hand Gesture Characterization from EMG Signals: Parameters Selection and Comparison
    Mengarelli, Alessandro
    Tigrini, Andrea
    Fioretti, Sandro
    Cardarelli, Stefano
    Verdini, Federica
    APPLIED SCIENCES-BASEL, 2020, 10 (20): : 1 - 19
  • [45] Motor State Classification based on Electromyography (EMG) Signals using Wavelet Entropy and Neural Networks
    Suppiah, Ravi
    Abidi, Khalid
    Kim, Noori
    Sharma, Anurag
    2021 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI 2021), 2021, : 1189 - 1195
  • [46] Measuring the Complexity of EMG Signal by Using Fuzzy Approximate Entropy in Post-Stroke Patients Rehabilitation
    Rulaningtyas R.
    Prasetyo A.B.
    Rahmatillah A.
    Putra A.P.
    Rahma O.N.
    Ain K.
    Pawana I.P.A.
    Journal of Engineering Science and Technology Review, 2021, 14 (03): : 85 - 90
  • [47] Effects of Task Demands on Kinematics and EMG Signals during Tracking Tasks Using Multiscale Entropy
    Wu, Yuanyu
    Song, Rong
    ENTROPY, 2017, 19 (07)
  • [48] Real-Time Surgical Tool Classification and Characterization with Surface-EMG Signals
    Sultania, Chinmay
    Singhal, Divyansh
    Chauhan, Siddharth
    Rao, Madhav
    Vazhiyal, Vikas
    2024 16TH INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING, ICCAE 2024, 2024, : 373 - 377
  • [49] Physiological validation of the decomposition of surface EMG signals
    Enoka, Roger M.
    JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY, 2019, 46 : 70 - 83
  • [50] Extraction of the envelope from surface EMG signals
    D'Alessio, T
    Conforto, S
    IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE, 2001, 20 (06): : 55 - 61
12345