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[大幅放大心电图在时间和频率方面的自动分类]

[Automatic classification of the greatly amplified ECG in relation to time and frequency].

作者信息

Voss A, Kurths J, Fiehring H, Kleiner H J

机构信息

Institut für Herz-Kreislauf-Forschung, Berlin-Buch.

出版信息

Biomed Tech (Berl). 1991 Mar;36(3):51-5. doi: 10.1515/bmte.1991.36.3.51.

Abstract

The non-invasive recording of cardiac micro-potentials from the surface of the body, in particular of the so-called ventricular late potentials from the highly amplified ECG, makes it possible to identify those patients at high risk from severe arrhythmics or sudden death. By using more extensive automatic parameter extraction methods, we were able to increase the detection rate of pathological ECGs. We were also able to show that the calculation of the power density spectrum, in particular on the basis of maximum entropy spectral estimation, in combination with the variance extraction method of eliminating the influence of residual noise, is capable of providing separate additional valuable information.

摘要

从身体表面无创记录心脏微电位,特别是从高度放大的心电图中记录所谓的心室晚电位,使得识别那些有严重心律失常或猝死高风险的患者成为可能。通过使用更广泛的自动参数提取方法,我们能够提高病理性心电图的检测率。我们还能够表明,功率密度谱的计算,特别是基于最大熵谱估计,并结合消除残余噪声影响的方差提取方法,能够提供单独的额外有价值信息。

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