用于准确检测心肌缺血的运动心电图的多变量分区分析。

Compartmental multivariate analysis of exercise ECGs for accurate detection of myocardial ischaemia.

作者信息

Sievänen H, Karhumäki L, Vuori I, Malmivuo J

机构信息

UKK Institute for Health Promotion Research, Tampere, Finland.

出版信息

Med Biol Eng Comput. 1994 Jul;32(4 Suppl):S3-8. doi: 10.1007/BF02523320.

DOI:10.1007/BF02523320

PMID:7967836

Abstract

An accurate computer-assisted diagnostic method for detection of myocardial ischaemia, called MUSTA, is developed. MUSTA is based on compartmental multivariate analysis of variables available in the exercise ECGs, and is definitively implemented in Prolog. It is heuristically developed by determining diagnostic criteria, which interrelate a modified ST/HR-slope, ST-segment value and shape, and maximum heart rate, so that concordance with the TI-201 SPECT is maximised. In the learning group consisting of 47 patients, MUSTA provides a diagnostic accuracy of 98%, the detection of ischaemia being in absolute concordance with TI-201 SPECT. MUSTA is evaluated in a similar but independent group of 60 patients. Then, accuracy is 90%, and sensitivity is 94%. The performance characteristics are significantly better than those of the standard exercise ECG, whose diagnostic accuracy in these groups is 77% and 70%, respectively. This study suggests that MUSTA is a significant improvement for computerised assessment of myocardial ischaemia.

摘要

一种名为MUSTA的用于检测心肌缺血的精确计算机辅助诊断方法被研发出来。MUSTA基于运动心电图中可用变量的多房室分析，并在Prolog中最终实现。它是通过确定诊断标准启发式开发的，这些标准将修正的ST/HR斜率、ST段值和形态以及最大心率相互关联起来，从而使与TI-201单光子发射计算机断层扫描（SPECT）的一致性最大化。在由47名患者组成的学习组中，MUSTA的诊断准确率为98%，缺血检测结果与TI-201 SPECT完全一致。MUSTA在另一组60名类似但独立的患者中进行评估。此时，准确率为90%，敏感性为94%。其性能特征明显优于标准运动心电图，标准运动心电图在这些组中的诊断准确率分别为77%和70%。这项研究表明，MUSTA在心肌缺血的计算机评估方面有显著改进。