AN APPROACH TO ARTIFACT IDENTIFICATION - APPLICATION TO HEART PERIOD DATA
AN APPROACH TO ARTIFACT IDENTIFICATION - APPLICATION TO HEART PERIOD DATA
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DOI：
10.1111/j.1469-8986.1990.tb01982.x
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发表时间：
1990-09-01
期刊：
影响因子：
3.7
通讯作者：
BOYSEN, ST
中科院分区：
文献类型：
--
作者：
BERNTSON, GG;QUIGLEY, KS;BOYSEN, ST
提出并评价了心期数据中伪影自动检测的合理策略。该方法对心期数据的具体实现是基于连续心期差异的分布特征。由于与正常的心期变异性相比，由伪象产生的搏动差异很大，因此连续心期之间的极端差异有助于识别潜在的伪象。该方法的关键是：1)从个体受试者的温度差异分布中推导出伪标准；2)使用基于百分位数的分布指数，与最小二乘估计相比，它对伪值的存在不太敏感。人工算法能够有效地识别嵌入在心脏周期记录中的人工心跳，标记来自人类和黑猩猩的数据集中的1494个刺激和实际人工心跳。同时，伪影算法产生的虚警率小于0.3%。虽然目前的实现仅限于心脏周期数据，但所概述的伪影检测方法也可适用于其他生物信号。
A rational strategy for the automated detection of artifacts in the heart period data is outlined and evaluated. The specific implementation of this approach for heart period data is based on the distribution characteristics of successive heart period differences. Because beat-to-beat differences generated by artifacts are large, relative to normal heart period variability, extreme differences between successive heart periods serve to identify potential artifacts. Critical to this approach are: 1) the derivation of the artifact criterion from the distribution of beat differences of the individual subject and 2) the use of percentile-based distribution indexes, which are less sensitive to corruption by the presence of artifactual values than are least-square estimates. The artifact algorithms were able to effectively identify artifactual beats embedded in heart period records, flagging each of the 1494 stimulated and actual artifacts in data sets derived from both humans and chimpanzees. At the same time, the artifact algorithms yielded a false alarm rate of less than 0.3%. Although the present implementation was restricted to heart period data, the outlined approach to artifact detection may also be applicable to other biological signals.