Format

Send to:

Choose Destination
See comment in PubMed Commons below
Comput Methods Programs Biomed. 2013 Dec;112(3):466-80. doi: 10.1016/j.cmpb.2013.08.006. Epub 2013 Sep 2.

Broadband noise suppression and feature identification of ECG waveforms using mathematical morphology and embedding theorem.

Author information

  • 1School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China.

Abstract

The paper presents an adaptive morphological filter developed using multiscale mathematical morphology (MM) to reject broadband noise from ECG signals without affecting the feature waveforms. As a pre-processing procedure, the adaptive morphological filter cleans an ECG signal to prepare it for further analysis. The noiseless ECG signal is embedded within a two-dimensional phase space to form a binary image and the identification of the feature waveforms is carried out based on the information presented by the image. The classification of the feature waveforms is implemented by an adaptive clustering technique according to the geometric information represented by the image in the phase space. Simulation studies on ECG records from the MIT-BIH and BIDMC databases have demonstrated the effectiveness and accuracy of the proposed methods.

Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

KEYWORDS:

Adaptive filter; ECG signal processing; Embedding theorem; Feature identification; Multiscale mathematical morphology

PMID:
24094825
[PubMed - indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science
    Loading ...
    Write to the Help Desk