  As a new method for detecting change-points in high-resolution time series,
we apply Maximum Mean Discrepancy to the distributions of ordinal patterns in
different parts of a time series. The main advantage of this approach is its
computational simplicity and robustness with respect to (non-linear) monotonic
transformations, which makes it particularly well-suited for the analysis of
long biophysical time series where the exact calibration of measurement devices
is unknown or varies with time. We establish consistency of the method and
evaluate its performance in simulation studies. Furthermore, we demonstrate the
application to the analysis of electroencephalography (EEG) and
electrocardiography (ECG) recordings.
