  The pattern of state changes in a biomedical time series can be related to
health or disease. This work presents a principled approach for selecting a
changepoint detection algorithm for a specific task, such as disease
classification. Eight key algorithms were compared, and the performance of each
algorithm was evaluated as a function of temporal tolerance, noise, and
abnormal conduction (ectopy) on realistic artificial cardiovascular time series
data. All algorithms were applied to real data (cardiac time series of 22
patients with REM-behavior disorder (RBD) and 15 healthy controls) using the
parameters selected on artificial data. Finally, features were derived from the
detected changepoints to classify RBD patients from healthy controls using a
K-Nearest Neighbors approach. On artificial data, Modified Bayesian Changepoint
Detection algorithm provided superior positive predictive value for state
change identification while Recursive Mean Difference Maximization (RMDM)
achieved the highest true positive rate. For the classification task, features
derived from the RMDM algorithm provided the highest leave one out cross
validated accuracy of 0.89 and true positive rate of 0.87. Automatically
detected changepoints provide useful information about subject's physiological
state which cannot be directly observed. However, the choice of change point
detection algorithm depends on the nature of the underlying data and the
downstream application, such as a classification task. This work represents the
first time change point detection algorithms have been compared in a meaningful
way and utilized in a classification task, which demonstrates the effect of
changepoint algorithm choice on application performance.
