Loss-analysis via Attention-scale for Physiologic Time Series
This incremental method addresses the analysis of physiologic signals for studying ageing, diseases, and other phenomena, but does not claim broad SOTA impact.
The authors tackled the problem that multiscale analysis of physiologic time series may not fully reflect original signal properties due to scaling losses and lack of attention to key observations, by introducing a new loss-analysis via attention-scale method, which complements complexity-analysis to capture previously missed aspects.
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.