Premium
Identifying the End of Ventricular Activation:
Author(s) -
BERBARI EDWARD J.,
LANDER PAUL,
GESELOWITZ DAVID B.,
SCHERLAG BENJAMIN J.,
LAZZARA RALPH
Publication year - 1994
Publication title -
journal of cardiovascular electrophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.193
H-Index - 138
eISSN - 1540-8167
pISSN - 1045-3873
DOI - 10.1111/j.1540-8167.1994.tb01112.x
Subject(s) - signal averaged electrocardiogram , medicine , qrs complex , cardiac resynchronization therapy , identification (biology) , pattern recognition (psychology) , cardiology , artificial intelligence , computer science , heart failure , botany , biology , ejection fraction
Late Potentials: Epicardial and Body Surface. Introduction: Identification of the end of the QRS is perhaps the single most important feature obtained from the high resolution signal‐averaged electrocardiogram (SAECG). This point relies on computer algorithms to select a point ahove the noise levels. Prior studies to suhstantiate this approach using eleetrograms for comparison have demonstrated many examples of the hody surface recordings failing to detect the full extent of the late potentials. Methods and Results: An animal model that generates late potentials was used in conjunction with epicardial cardiac mapping system to systematically examine the rea.sons for these failures. In I I of 13 dogs we found a concordance hetween the signal‐averaged recordings and Ihe epicardial recordings within 5 msec. The two discordant studies were attrihuted to a failure of epicardial mapping to record all late potential sources. Also, a means of accurately comparing measurements from the two recording technologies was required in this study as well as a new definition for identifying the end of activation currents in epicardial eleetrograms. Conclusion: To achieve these results required approaches different from those used in the clinical setting to record the SAECG. These include: (1) the analysis of individual XYZ leads as opposed to the vector magnitude derived from these leads; (2) visual identification of very low level signals, as automatic algorithms often fail to detect low level signals: and (3) the use of finite impulse response digital filters instead of the bidirectional Butterworth filter.