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首页> 外文期刊>IEEE Transactions on Biomedical Engineering >On-line segmentation algorithm for continuously monitored data in intensive care units
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On-line segmentation algorithm for continuously monitored data in intensive care units

机译:密集护理单位连续监测数据的在线分割算法

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An on-line segmentation algorithm is presented in this paper. It is developed to preprocess data describing the patient's state, sampled at high frequencies in intensive care units, with a further purpose of alarm filtering. The algorithm splits the signal monitored into line segments-continuous or discontinuous-of various lengths and determines on-line when a new segment must be calculated. The delay of detection of a new line segment depends on the importance of the change: the more important the change, the quicker the detection. The linear segments are a correct approximation of the structure of the signal. They emphasise steady-states, level changes and trends occurring on the data. The information returned by the algorithm, which is the time at which the segment begins, its ordinate and its slope, is sufficient to completely reconstruct the filtered signal. This makes the algorithm an interesting tool to provide a processed time history record of the monitored variable. It can also be used to extract on-line information on the signal, such as its trend, in the short or long term.
机译:本文提出了一个在线分割算法。它开发成用于描述患者状态的预处理数据,在重症监护单元中的高频下采样,具有报警滤波的进一步目的。该算法将监视到线段的信号分成连续或不连续的各种长度,并且当必须计算新段时,在线确定。新线段检测的延迟取决于变化的重要性:更改更重要,检测更快。线性段是信号结构的正确近似。他们强调数据上发生的稳态,水平变化和趋势。算法返回的信息,即段开始的时间,其纵坐标及其斜率足以完全重建滤波信号。这使得算法成为有趣的工具来提供所监视变量的处理时间历史记录。它还可以用于在短期或长期内提取关于信号的在线信息,例如其趋势。

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