心外膜电位标测信号的自动联合分析方法研究  

Utomated united analysis method for epicardial potential mapping signals

作　　者：周宇[1,2] 杨翠微[1] 方祖祥[1] 

机构地区：[1]复旦大学电子工程系,上海200433 [2]上海理工大学医疗器械与食品学院,上海200093

出　　处：《仪器仪表学报》2009年第5期926-931,共6页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(30400102);上海市重点学科建设(B112)资助项目

摘　　要：心外膜电位标测系统是心脏电生理机制研究和临床精密诊断的重要工具,其技术难点之一是如何对标测信号进行有效分析以获取关键信息、揭示心外膜电活动规律。为此,提出具有时间依赖性的相关波形分析和波形能量分析法,结合波动图联合对标测信号进行分析;同时,提出基于瞬时波形能量分析的特征点群识别算法,以实现分析方法自动化的目标。结果显示:特征点群识别算法能够准确定位除极波穿越电极片的时间;波动图可实时、动态反映除极波的传播;具有时间依赖性的相关波形分析和波形能量分析可揭示出除极波的传播方向和路径。因而,此自动联合分析方法能够自动、准确地揭示除极波的传播规律;分析方法耗时小、抗噪能力强,适用于心外膜电位标测系统。Epicardial potential mapping （EPM） system is an important tool used for cardiac electrophysiological research and clinically precise diagnosis. One of its technique difficulties is how to analyze EPM signals effectively to acquire pivotal information. Therefore time-dependent correlation waveform analysis （CWA） and energy distribution are put forward and united with fluctuation map to analyze EPM signals. A characteristic recognition algorithm based on instantaneous waveform energy is put forward to make the united analysis method automated. Results indicate that the characteristic recognition algorithm could recognize the time when the depolarization waves traverse the electrodes plaque correctly; the fluctuation map could reflect the spread of the depolarization wave in real-time and dynamically; and time-dependent CWA and energy distribution could reveal the spread directions and paths of the depolarization wave. In conclusion, the automated united method with strong anti-noise ability can reveal the spread regularity of the depolarization wave automatically and accurately, features time-saving and strong anti-noise capability, and fits for epicardial potential mapping system.

关 键 词：心外膜电位标测 相关波形分析 波形能量分析 波动图 特征点群识别 

分 类 号：R318.6[医药卫生—生物医学工程]