Literature DB >> 12669985

A dynamical model for generating synthetic electrocardiogram signals.

Patrick E McSharry1, Gari D Clifford, Lionel Tarassenko, Leonard A Smith.   

Abstract

A dynamical model based on three coupled ordinary differential equations is introduced which is capable of generating realistic synthetic electrocardiogram (ECG) signals. The operator can specify the mean and standard deviation of the heart rate, the morphology of the PQRST cycle, and the power spectrum of the RR tachogram. In particular, both respiratory sinus arrhythmia at the high frequencies (HFs) and Mayer waves at the low frequencies (LFs) together with the LF/HF ratio are incorporated in the model. Much of the beat-to-beat variation in morphology and timing of the human ECG, including QT dispersion and R-peak amplitude modulation are shown to result. This model may be employed to assess biomedical signal processing techniques which are used to compute clinical statistics from the ECG.

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Year:  2003        PMID: 12669985     DOI: 10.1109/TBME.2003.808805

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  64 in total

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Authors:  Gari Clifford; Reza Sameni; Jay Ward; Julian Robinson; Adam J Wolfberg
Journal:  Am J Obstet Gynecol       Date:  2011-03-05       Impact factor: 8.661

2.  A dynamical model for generating synthetic Phonocardiogram signals.

Authors:  Ali Almasi; Mohammad B Shamsollahi; Lotfi Senhadji
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

3.  An Artificial Multi-Channel Model for Generating Abnormal Electrocardiographic Rhythms.

Authors:  Gd Clifford; S Nemati; R Sameni
Journal:  Comput Cardiol       Date:  2008

4.  Synthetic ECG generation and Bayesian filtering using a Gaussian wave-based dynamical model.

Authors:  Omid Sayadi; Mohammad B Shamsollahi; Gari D Clifford
Journal:  Physiol Meas       Date:  2010-08-18       Impact factor: 2.833

5.  Baseline wander removal of electrocardiogram signals using multivariate empirical mode decomposition.

Authors:  Praveen Gupta; Kamalesh Kumar Sharma; Shiv Dutt Joshi
Journal:  Healthc Technol Lett       Date:  2015-11-26

6.  FPGA Implementation of Heart Rate Monitoring System.

Authors:  D Panigrahy; M Rakshit; P K Sahu
Journal:  J Med Syst       Date:  2015-12-07       Impact factor: 4.460

7.  A new feature detection mechanism and its application in secured ECG transmission with noise masking.

Authors:  Fahim Sufi; Ibrahim Khalil
Journal:  J Med Syst       Date:  2009-04       Impact factor: 4.460

8.  Enhanced modified moving average analysis of T-wave alternans using a curve matching method: a simulation study.

Authors:  D Cuesta-Frau; Pau Micó-Tormos; M Aboy; Marcelo O Biagetti; D Austin; Ricardo A Quinteiro
Journal:  Med Biol Eng Comput       Date:  2008-10-21       Impact factor: 2.602

9.  Comparison of three artificial models of the magnetohydrodynamic effect on the electrocardiogram.

Authors:  Julien Oster; Raul Llinares; Stephen Payne; Zion Tsz Ho Tse; Ehud Jeruham Schmidt; Gari D Clifford
Journal:  Comput Methods Biomech Biomed Engin       Date:  2014-04-24       Impact factor: 1.763

10.  Extraction of fetal electrocardiogram using H(infinity) adaptive algorithms.

Authors:  Sadasivan Puthusserypady
Journal:  Med Biol Eng Comput       Date:  2007-08-21       Impact factor: 2.602

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