A New Intrinsic Mode Function Selection Method Based on Power Spectral Density

被引:0
作者
Kotan, Soner [1 ]
Akan, Aydin [2 ]
机构
[1] Istanbul Univ, Biyomed Muhendisligi Anabilim Dali, Istanbul, Turkey
[2] Izmir Katip Celebi Univ, Biyomed Muhendisligi Bolumu, Izmir, Turkey
来源
2018 MEDICAL TECHNOLOGIES NATIONAL CONGRESS (TIPTEKNO) | 2018年
关键词
empirical mode decomposition; intrinsic mode function selection; power spectral density;
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Emprical Mode Decomposition is one of the mostly used decomposition method to decompose non-linear and non stationary multivariate signals into linear and stationary sub signals. Intrinsic mode functions, which are the sub-signals obtained as an output of the process are used in many studies, primarily classification. Even though, some of the intrinsic mode functions are noise dominant while some of them are signal dominant. For this reason and that to reduce the computational cost, intrinsic mode function selection methods have been proposed in the past. In this study, a new selection method is proposed which is inspired of proposed methods in the past.
引用
收藏
页数:4
相关论文
共 15 条
[1]   An Improved Design of High-Resolution Quadratic Time-Frequency Distributions for the Analysis of Nonstationary Multicomponent Signals Using Directional Compact Kernels [J].
Boashash, Boualem ;
Ouelha, Samir .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2017, 65 (10) :2701-2713
[2]   Research on the intrinsic mode function (IMF) criterion in EMD method [J].
Cheng, JS ;
Yu, DJ ;
Yang, Y .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2006, 20 (04) :817-822
[3]  
GOLDBERGER AL, CIRCULATION, V101, pE21
[4]   The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J].
Huang, NE ;
Shen, Z ;
Long, SR ;
Wu, MLC ;
Shih, HH ;
Zheng, QN ;
Yen, NC ;
Tung, CC ;
Liu, HH .
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 1998, 454 (1971) :903-995
[5]   Applications of empirical mode decomposition for processing nonstationary signals [J].
Klionski D.M. ;
Oreshko N.I. ;
Geppener V.V. ;
Vasiljev A.V. .
Pattern Recognition and Image Analysis, 2008, 18 (03) :390-399
[6]   EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs [J].
Komaty, Ali ;
Boudraa, Abdel-Ouahab ;
Augier, Benoit ;
Dare-Emzivat, Delphine .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2014, 63 (01) :27-34
[7]   Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing [J].
Lv, Yong ;
Yuan, Rui ;
Song, Gangbing .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2016, 81 :219-234
[8]   A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing [J].
Peng, ZK ;
Tse, PW ;
Chu, FL .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2005, 19 (05) :974-988
[9]   Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions [J].
Ricci, Roberto ;
Pennacchi, Paolo .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2011, 25 (03) :821-838
[10]   BCI2000: A general-purpose, brain-computer interface (BCI) system [J].
Schalk, G ;
McFarland, DJ ;
Hinterberger, T ;
Birbaumer, N ;
Wolpaw, JR .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2004, 51 (06) :1034-1043