Redundancy reduction for improved display and analysis of body surface potential maps. II. Temporal compression.
Redundancy reduction for improved display and analysis of body surface potential maps. II. Temporal compression.
复制标题
减少冗余以改进体表电位图的显示和分析。
DOI：
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发表时间：
1981
影响因子：
20.1
通讯作者：
J. .. Abildskov
中科院分区：
文献类型：
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作者：
A. Evans;R. Lux;M. Burgess;R. Wyatt;J. .. Abildskov
本文介绍了使用 Karhunen-Loeve 展开来识别和减少心电图体表电位图中的时间冗余（以 1 kHz/通道同时记录约 600 毫秒的 192 个体表导联）。通过准确表示原始数据，获得了大约 20 比 1 的时间数据压缩。使用独立的 QRS 和 ST-T 正交基函数集比从 QRST 导出的基函数提供了更准确的表示。结合前文所述的空间压缩，获得了约 320 比 1 的整体地图数据压缩，而没有显着损失表示或地图外观的准确性。通过空间和时间压缩，通常组成单个心脏复合波的 100,000 个数字可以由 216 个系数准确表示。使用从单个心脏复合体导出的基函数由 216 个系数精确表示。使用从 221 个图的训练集导出的基函数，ST-T 期间估计的平均均方根表示误差为 60 微伏。对于不属于训练集的 34 个测试图，在 QRS 期间测量的平均误差为 64 microV，在 ST-T 期间测量的平均误差为 23 microV。该技术为地图诊断内容的量化和地图的自动分类提供了基础。
This paper describes use of the Karhunen-Loeve expansion to identify and reduce temporal redundancy in electrocardiographic body surface potential maps (192 body surface leads recorded simultaneously at 1 kHz/channel for approximately 600 msec). Temporal data compression of about 20 to 1 was obtained with accurate representation of the original data. Use of separate sets of orthonormal basis functions for QRS and ST-T provided a more accurate representation than the basis derived from QRST. Combined with the spatial compression described in the preceding paper, overall map data compression of about 320 to 1 was obtained without significant loss of accuracy of representation or map appearance. With both spatial and temporal compression the 100,000 numbers which typically comprise a single cardiac complex were accurately represented by 216 coefficients. Using basis functions derived from a single cardiac complex were accurately represented by 216 coefficients. Using basis functions derived from a training set of 221 maps, the estimated average rms error of representation was 60 microV during the ST-T. For 34 test maps which were not part of the training set, measured average errors were 64 microV during the QRS and 23 microV during the ST-T. This technique provides a basis for quantification of the diagnostic content of maps and automated classification of maps.