Morphological Processing of Physiological Signals for Feature Extraction

被引:3
|
作者
Samanta, B. [1 ]
机构
[1] Villanova Univ, Dept Mech Engn, Villanova, PA 19085 USA
关键词
SPECTRUM; ENTROPY;
D O I
10.1109/IEMBS.2009.5333783
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The paper proposes a novel method of extracting features from physiological signals using intrinsic mode decomposition (IMD) and morphological signal processing (MSP). The complex, nonlinear and non-stationary biomedical signals are first decomposed into intrinsic mode functions (IMF). Next each IMF is subjected to MSP for extracting features, namely, pattern spectrum entropy, that characterize the shape-size complexity of the component signals. These along with other features like energy and sample entropy are extracted from the individual IMF as well as the cumulative sums of IMF for characterizing the signals. The procedure is illustrated using heart sound signals digitally recorded during cardiac auscultation representing different cardiac conditions.
引用
收藏
页码:324 / 327
页数:4
相关论文
共 50 条
  • [1] INTRINSIC MODE DECOMPOSITION OF PHYSIOLOGICAL SIGNALS FOR FEATURE EXTRACTION
    Samanta, B.
    Nataraj, C.
    [J]. PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, VOL 1, PTS A AND B, 2010, : 233 - 239
  • [2] Post processing of separated signals for feature extraction and identification
    Belkasim, SO
    [J]. 40TH MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1 AND 2, 1998, : 1241 - 1243
  • [3] A New Morphological Filter for Fault Feature Extraction of Vibration Signals
    Yu, Jianbo
    Hu, Tianzhong
    Liu, Haiqiang
    [J]. IEEE ACCESS, 2019, 7 : 53743 - 53753
  • [4] Feature Finder: A Powerful Matlab Tool to Processing Physiological Signals
    Russo, Frank A.
    Andrews, Alex James
    Gabriel, Nespoli
    [J]. CANADIAN JOURNAL OF EXPERIMENTAL PSYCHOLOGY-REVUE CANADIENNE DE PSYCHOLOGIE EXPERIMENTALE, 2012, 66 (04): : 299 - 299
  • [5] Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals
    Campbell, Evan
    Phinyomark, Angkoon
    Scheme, Erik
    [J]. FRONTIERS IN NEUROSCIENCE, 2019, 13
  • [6] A Novel Feature Extraction Method for sEMG Signals using Image Processing
    Guo, Shuxiang
    Guo, Chunhua
    Pang, Muye
    [J]. 2013 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA), 2013, : 1765 - 1770
  • [7] Feature Extraction and Recognition of Human Physiological Signals Based on the Convolutional Neural Network
    Hurr, Chansol
    Li, Caiyan
    Li, Heng
    [J]. MOBILE INFORMATION SYSTEMS, 2022, 2022
  • [8] Feature Extraction Analysis for Emotion Recognition from ICEEMD of Multimodal Physiological Signals
    Gomez-Lara, J. F.
    Ordonez-Bolanos, O. A.
    Becerra, M. A.
    Castro-Ospina, A. E.
    Mejia-Arboleda, C.
    Duque-Mejia, C.
    Rodriguez, J.
    Revelo-Fuelagan, Javier
    Peluffo-Ordonez, Diego H.
    [J]. INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2019, PT I, 2019, 11431 : 351 - 362
  • [9] On Directionality in Morphological Feature Extraction
    Swiercz, Michal
    Iwanowski, Marcin
    [J]. COMPUTER VISION AND GRAPHICS, 2012, 7594 : 677 - 684
  • [10] Detection of Rail Faults Using Morphological Feature Extraction Based Image Processing
    Tastimur, Canan
    Akin, Erhan
    Karakose, Mehmet
    Aydin, Ilhan
    [J]. 2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2015, : 1244 - 1247