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.
An algorithm which utilizes digital image processing and pattern recognition methods for automated definition of left ventricular (LV) contours is presented. Digital image processing and pattern recognition techniques are applied to digitally acquired radiographic images of the heart to extract the LV contours required for quantitative analysis of cardiac function. Knowledge of the image domain is invoked at each step of the algorithm to orient the data search and thereby the complexity of the solution. A knowledge-based image transformation, directional gradient search, expectations of object versus background location, least-cost path searches by dynamic programming, and a digital representation of possible versus impossible ventricular shape are exploited. The digital representation, composed of a set of characteristic templates, was created using contours obtained by manual tracing. The algorithm was tested by application of three sets of 25 images each. Test set one and two were used as training sets for creation of the model for contour correction. Model-based correction proved to be an effective technique, producing significant reduction of error in the final contours.
This paper preserirs a revie~v of ttle evok~rion of racli~cardia j~hrillariotr derecrior~ algorirhms designed for implantable cardioverrer defibrillafors (ICD) inclltdirig rhose thar have been incorporated into 1st. 2nd, and 3rd getzerarion devices. The major enlphasis of tliis review is an overview of rtle developmetir of new and innovative means for improved detection in ne.rt-generarior~ devices. Time-domain and frequetlcv-domain tnerhods of elec.rrogratn anal~ses are described, lirnifariorls are cited, and prorni~ing tlew proposals for increased specificity ~tlhicll address the false ~tlock incidence are presented. A. Third-Generation lmphntuble Cardioverter Defibrillators Third-generation ICD' s offer high-energy defibrillation, low-energy cardioversion. antitachycardia and antibradycardia pacing. multiple programmable tachycardia detection zones which utilize the cardiac cycle length, onset, stability, and sustained high rate features which can be selected or deselected, noninvasive programmed stimulation. teleme-001 8-92 19196S05.00 F 19% IEEE PKO('t I.l)lS(iS OF I'HE I l l-E. \'()I.. 84. SO. 3. \!.ARCH 1% I Stephanie A. Camell (Student Member. IEEI:) received the B.S. in electrical engineering from the University of Iowq Iowa City. LA. in 1992. She received the S1.S. de-we in electrical engineering from the Yniversity of Michigan. Ann Arbor, MI, in 1994. and is now working to complete the Ph.D. there. As a graduate student at the Cniversity of Michigan, she has been a research assistant and a teaching assistant, and is currently a Nhitaker Foundation Graduate Fellow. Her priis signal processing applied to biological signals, :arical signals.
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