Multielectrode Mapping of the Heart
Cardiac Bioelectric Therapy(2009)
摘要
Multielectrode cardiac mapping has at least a 50-year history in cardiac research, and the development of this methodology
has closely followed the technological advances in instrumentation and computing. The methodology has proven to be quite effective
in characterizing potential distributions on both the body surface and the epicardial surface of the heart.1–4 However, the more challenging problem for multielectrode systems is the identification and display of cardiac activation
or isochronal maps. In the earlier era of cardiac mapping, hardware limitations, particularly the speed of computer processing
and digital data acquisition, were the major challenges for obtaining continuous data from a high number of recording channels.
For the current generation of digital electronics and computers this is no longer a significant challenge. The analysis and
interpretation of the data still pose a number of challenges, since in many cases, such as diseased myocardium or during complex
tachyarrhythmias, the biophysical basis of conduction is not fully developed. For example, the use of contour-generation software
often does not consider the actual nature of the underlying pathophysiology. Many standard interpolation algorithms will indeed
create contours overlying scar tissue within infarcted regions. This is an inherent error.
A number of newer mapping approaches rely on mathematical models to create images based on data at some distance from the
actual sources. In some cases these systems are proprietary and may have indeed conquered some long-standing problems. In
other cases, because the systems produce “good looking” images that fit a preconceived model of activation, their underlying
models are not challenged. This chapter focuses on the issues surrounding direct contact, multielectrode mapping approaches
and will concentrate on the problems associated with producing activation maps, especially from regions surrounding and within
infarct regions.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要