Literature DB >> 1879845

A comparison of four new time-domain techniques for discriminating monomorphic ventricular tachycardia from sinus rhythm using ventricular waveform morphology.

R D Throne1, J M Jenkins, L A DiCarlo.   

Abstract

Electrical management of intractable tachycardia via implantable antitachycardia devices has become a major form of therapy. Newly advanced methods of ventricular tachycardia detection propose examination of changes in intraventricular electrogram morphology in addition to or in combination with earlier rate-based detection algorithms. Unfortunately, most of the proposed morphology analysis techniques have computational demands beyond the capabilities of present devices or may be adversely affected by amplitude and baseline fluctuations of the intraventricular electrogram. We have designed four new computationally efficient time-domain algorithms for distinguishing ventricular electrograms during monomorphic ventricular tachycardia (VT) from those during sinus rhythm using direct analysis of the ventricular electrogram morphology. All four techniques are independent of amplitude fluctuations and three of the four are independent of baseline changes. These new techniques were compared to correlation waveform analysis, a previously proposed method for distinction of VT from sinus rhythm. Evaluation of these four new algorithms was performed on data from 19 consecutive patients with 31 distinct monomorphic ventricular tachycardia morphologies. Three of the algorithms performed as well or better than correlation waveform analysis but with one-tenth to one-half the computational demands.

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Year:  1991        PMID: 1879845     DOI: 10.1109/10.81581

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


  7 in total

1.  A pilot study examining the performance of polynomial-modeled ventricular shock electrograms for rhythm discrimination in implantable devices.

Authors:  Jeffrey L Williams; Vladimir Shusterman; Samir Saba
Journal:  Pacing Clin Electrophysiol       Date:  2006-09       Impact factor: 1.976

2.  Automated screening of arrhythmia using wavelet based machine learning techniques.

Authors:  Roshan Joy Martis; M Muthu Rama Krishnan; Chandan Chakraborty; Sarbajit Pal; Debranjan Sarkar; K M Mandana; Ajoy Kumar Ray
Journal:  J Med Syst       Date:  2010-06-16       Impact factor: 4.460

3.  A segmental polynomial model of ventricular electrograms as a simple and efficient morphology discriminator for implantable devices.

Authors:  Jeffrey L Williams; Vladimir Shusterman; Samir Saba
Journal:  Ann Noninvasive Electrocardiol       Date:  2006-07       Impact factor: 1.468

4.  [New algorithms for discrimination between supraventricular and ventricular tachyarrhythmias in patients with implantable cardioverter/defibrillator].

Authors:  J Neuzner; M Schlepper
Journal:  Herzschrittmacherther Elektrophysiol       Date:  1997-03

5.  Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions.

Authors:  Emran M Abu Anas; Soo Y Lee; Md K Hasan
Journal:  Biomed Eng Online       Date:  2010-09-04       Impact factor: 2.819

6.  Life-threatening ventricular arrhythmia recognition by nonlinear descriptor.

Authors:  Yan Sun; Kap Luk Chan; Shankar Muthu Krishnan
Journal:  Biomed Eng Online       Date:  2005-01-24       Impact factor: 2.819

7.  Arrhythmia identification with two-lead electrocardiograms using artificial neural networks and support vector machines for a portable ECG monitor system.

Authors:  Shing-Hong Liu; Da-Chuan Cheng; Chih-Ming Lin
Journal:  Sensors (Basel)       Date:  2013-01-09       Impact factor: 3.576

  7 in total

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