Our EMBC paper is accepted, congratulations to Anis, Raha, and Paul!
Physiological timeseries such as vital signs contain
important information about a patient and are used in
different clinical application. However, they suffer from
missing values and sampling irregularity. In recent years,
Gaussian Processes have been used as sophisticated
nonlinear value imputation methods on time series, however
there is a lack of comparison to other simpler methods.
This paper compares the ability of five methods that can be
used in missing data imputation in physiological time
series. These models are linear interpolation as the
baseline, cubic spline interpolation, and three non-linear
methods: Single Task Gaussian Processes, Multi-Task
Gaussian Processes, and Multivariate Imputation Chained
Equations. We used seven intraoperative physiological time
series from 27,481 patients. Piecewise aggregate
approximation was employed as a dimensionality reduction
and resampling strategy. Linear interpolation and cubic
splining show overall superiority in prediction of the
missing values, compared to the other complex models. The
performance of the kernel-based methods suggest that they
are highly sensitive to the kernel width and require
incorporation of domain knowledge for fine-tuning.