Schreck D M, Tricarico V J, Frank J D, Thielen L E, Chhibber P, Brotea C, Leber I B
Muhlenberg Regional Medical Center, Plainfield, NJ, USA.
Acad Emerg Med. 1998 Sep;5(9):929-34. doi: 10.1111/j.1553-2712.1998.tb02825.x.
The ECG is a 12-lead-vector system and is known to contain redundant information. Factor analysis (FA) is a statistical technique that improves measured data and eliminates redundancy by identifying a minimum number of factors accounting for variance in the data set.
To identify the minimum number of lead-vectors required to predict the 12-lead ECG.
A total of 104 ECGs were obtained from 24 normal men, 22 normal women, and 28 men and 30 women with variable pathologies. Each ECG lead was simultaneously acquired and digitized, resulting in a voltage-time data array stored for mathematical analysis. Each array was factor-analyzed to identify the minimum number of lead-vectors spanning the ECG data space. The 12-lead ECG was then predicted from this minimum lead-vector set. ANOVA was used to test for statistical significance between normal and pathologic data groups.
FA revealed that 3 lead-vectors accounted for 99.12%+/-0.92% (95% CI+/-0.18%) of the variance contained in the 12-lead ECG voltage-time data for all 104 cases. There were no statistically significant differences between men and women (99.25%+/-0.66% vs 98.98+/-1.11%; p=0.139). Statistically significant differences were noted between normal and acute myocardial infarction ECGs (99.5%+/-0.27% vs 98.66+/-1.25%; p=0.00003). The measured and predicted leads were almost identical. A 3-dimensional spatial ECG derived from the 3-lead-vector set resulted in variable curved surfaces that differed by pathology.
The 12-lead ECG can be derived from only 3 measured leads and graphed as a 3-D spatial ECG. This type of data processing may lead to instantaneous acquisition and may enhance the diagnostic capability of the ECG from routine bedside telemetry equipment.
心电图是一个12导联向量系统，已知其包含冗余信息。因子分析（FA）是一种统计技术，通过识别解释数据集中方差的最少因子数量来改善测量数据并消除冗余。
确定预测12导联心电图所需的最少导联向量数量。
从24名正常男性、22名正常女性以及28名患有各种病理状况的男性和30名患有各种病理状况的女性中总共获取了104份心电图。同时采集并数字化每个心电图导联，得到一个存储用于数学分析的电压 - 时间数据阵列。对每个阵列进行因子分析，以识别跨越心电图数据空间的最少导联向量数量。然后从这个最少导联向量集预测12导联心电图。使用方差分析来检验正常和病理数据组之间的统计学显著性。
因子分析显示，对于所有104例病例，3个导联向量占12导联心电图电压 - 时间数据中所含方差的99.12%±0.92%（95%置信区间±0.18%）。男性和女性之间无统计学显著差异（99.25%±0.66%对98.98±1.11%；p = 0.139）。在正常心电图和急性心肌梗死心电图之间观察到统计学显著差异（99.5%±0.27%对98.66±1.25%；p = 0.00003）。测量导联和预测导联几乎相同。从3导联向量集导出的三维空间心电图产生了因病理状况而异的可变曲面。
12导联心电图仅可从3个测量导联导出，并绘制成三维空间心电图。这种类型的数据处理可能导致即时采集，并可能增强常规床边遥测设备心电图的诊断能力。
