Literature DB >> 2731947

Evaluation of techniques for recognition of ventricular arrhythmias by implanted devices.

K L Ripley, T E Bump, R C Arzbaecher.   

Abstract

Implantable devices that provide antitachycardia and defibrillation capability currently have limited ability to distinguish among different cardiac rhythms. We have investigated three methods of electrogram analysis: rate, irregularity, and amplitude distribution. In 35 episodes in 19 patients, we applied these three algorithms to 15 s recorded passages of ventricular electrograms during supraventricular tachycardia (N = 11), ventricular tachycardia (N = 11), and ventricular fibrillation (N = 13). Each was individually paired with a recording of sinus rhythm from the same patient. All recordings were obtained during standard electrophysiologic testing. Each algorithm was successful at distinguishing the tachyarrhythmias from sinus rhythm at one or more levels of algorithm parameterization. Rate alone discriminated supraventricular tachycardia from ventricular fibrillation but did not distinguish between supraventricular and ventricular tachycardia. Rate combined with irregularity distinguished between ventricular tachycardia and ventricular fibrillation, but did not discriminate between ventricular and supraventricular tachycardia. Although the amplitude distribution algorithm was unable to separate perfectly any of the three tachyarrhythmias, it provided the best performance in separating supraventricular and ventricular tachycardia (82 percent sensitivity and specificity). We conclude that algorithms based on rate, irregularity, and amplitude distribution analysis of ventricular electrograms may distinguish sinus rhythm from tachyarrhythmias, but may not distinguish among tachyarrhythmias.

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Year:  1989        PMID: 2731947     DOI: 10.1109/10.29456

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


  5 in total

1.  Classification of premature ventricular complexes using filter bank features, induction of decision trees and a fuzzy rule-based system.

Authors:  O Wieben; V X Afonso; W J Tompkins
Journal:  Med Biol Eng Comput       Date:  1999-09       Impact factor: 2.602

2.  Adaptive filters for analysis of intra-cardiac signals.

Authors:  N V Thakor
Journal:  Med Biol Eng Comput       Date:  1994-07       Impact factor: 2.602

3.  Comparison of four techniques for recognition of ventricular fibrillation from the surface ECG.

Authors:  R H Clayton; A Murray; R W Campbell
Journal:  Med Biol Eng Comput       Date:  1993-03       Impact factor: 2.602

4.  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

5.  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

  5 in total

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