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Benchmarking Changepoint Detection Algorithms on Cardiac Time Series by Cakmak, Ayse; Reinertsen, Erik; Nemati, Shamim; Clifford, Gari D. is a scholarly article available to read on EtoBox.

What is Benchmarking Changepoint Detection Algorithms on Cardiac Time Series about?

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. A

Author

Cakmak, Ayse; Reinertsen, Erik; Nemati, Shamim; Clifford, Gari D.

Published

2024

Language

EN