McMechan S R, MacKenzie G, Allen J, Wright G T, Dempsey G J, Crawley M, Anderson J, Adgey A A
Regional Medical Cardiology Centre, Royal Victoria Hospital, Belfast, UK.
J Electrocardiol. 1995;28 Suppl:184-90. doi: 10.1016/s0022-0736(95)80054-9.
An algorithm for the early detection of acute myocardial infarction (MI) using body surface electrocardiographic potential mapping has been developed. The mapping system consists of a 64-hydrogel electrode harness applied rapidly to the anterior chest, from which electrocardiographic signals are stored on a memory card and processed by computer. At each of the 64 points, QRS and ST-T isointegrals and 10 other features of the QRST segment are measured. Using these measurements, new variables are derived that express the shape of the three-dimensional geometric surface of the map. The isointegrals, features, and shape variables are used in a variety of techniques to discriminate between MI and control subjects. Maps were recorded from 69 patients at initial presentation of chest pain suggestive of acute MI and from 80 healthy control subjects. Using a multiple logistic regression technique, 14 variables were identified that correctly classified 79 of the 80 control subjects (specificity, 98.8%) and 65 of the 69 MI patients (sensitivity, 94.2%). The algorithm based on these 14 variables was applied prospectively to maps recorded on a further 48 control subjects and 59 patients with acute MI. Of the MI patients, 31 had inferior, 13 inferoposterior, 10 anterior, 2 posterior, 1 lateral, 1 inferior with right bundle branch block, and 1 anterior non Q wave MI. The algorithm correctly classified all 48 control subjects (specificity, 100%) and 57 of the 59 MI patients (sensitivity, 96.6%). Marked differences in the three-dimensional geometric map surfaces between the control subjects and MI patients were demonstrated. Variables derived from these surfaces form the basis of an algorithm with a high sensitivity and specificity for the automated detection of acute MI. The design of adaptive algorithms and their application to patients with chest pain and atypical electrocardiographic changes, particularly ST depression, may lead to the earlier detection of MI and greater numbers of patients receiving thrombolytic therapy.
一种利用体表心电图电位标测技术早期检测急性心肌梗死(MI)的算法已经开发出来。该标测系统由一个能快速贴于前胸的64个水凝胶电极束组成,心电图信号从这里存储在存储卡上并由计算机处理。在这64个点的每一处,均测量QRS波群和ST - T等电位积分以及QRST段的其他10个特征。利用这些测量结果,可导出表达标测图三维几何表面形状的新变量。这些等电位积分、特征及形状变量被用于多种技术中,以区分MI患者和对照受试者。对69例初发提示急性MI胸痛的患者以及80例健康对照受试者记录了标测图。采用多元逻辑回归技术,确定了14个变量,这些变量能正确分类80例对照受试者中的79例(特异性为98.8%)以及69例MI患者中的65例(敏感性为94.2%)。基于这14个变量的算法被前瞻性地应用于另外48例对照受试者和59例急性MI患者记录的标测图。在MI患者中,31例为下壁心肌梗死,13例为下后壁心肌梗死,10例为前壁心肌梗死,2例为后壁心肌梗死,1例为侧壁心肌梗死,1例为下壁合并右束支传导阻滞,1例为前壁非Q波心肌梗死。该算法正确分类了所有48例对照受试者(特异性为100%)以及59例MI患者中的57例(敏感性为96.6%)。对照受试者和MI患者之间的三维几何标测图表面存在明显差异。源自这些表面的变量构成了一种对急性MI自动检测具有高敏感性和特异性的算法基础。适应性算法的设计及其在胸痛和心电图改变不典型(尤其是ST段压低)患者中的应用,可能会使MI得到更早检测,更多患者接受溶栓治疗。