论文标题
通过注意力尺度的生理时间序列损失分析
Loss-analysis via Attention-scale for Physiologic Time Series
论文作者
论文摘要
生理信号在多个空间和时间尺度上具有特性，可以通过缩放多尺度的缩放技术对粗粒物生理信号的复杂性分析来显示。不幸的是，通过多尺度从粗粒粒子信号获得的结果可能无法完全反映原始信号的特性，因为缩放技术会造成损失，并且相同的缩放技术可能会给不同的信号带来不同的损失。另一个问题是，多尺度不考虑信号固有的关键观察。在这里，我们展示了一种新的时间序列分析方法，称为通过注意力尺度的损失分析。我们表明，多尺度是注意力等级的特殊情况。损失分析可以补充复杂性分析，以捕获未使用先前开发的措施捕获的信号的各个方面。这可以用于研究衰老，疾病和其他生理现象。
Physiologic signals have properties across multiple spatial and temporal scales, which can be shown by the complexity-analysis of the coarse-grained physiologic signals by scaling techniques such as the multiscale. Unfortunately, the results obtained from the coarse-grained signals by the multiscale may not fully reflect the properties of the original signals because there is a loss caused by scaling techniques and the same scaling technique may bring different losses to different signals. Another problem is that multiscale does not consider the key observations inherent in the signal. Here, we show a new analysis method for time series called the loss-analysis via attention-scale. We show that multiscale is a special case of attention-scale. The loss-analysis can complement to the complexity-analysis to capture aspects of the signals that are not captured using previously developed measures. This can be used to study ageing, diseases, and other physiologic phenomenon.
