1 research outputs found
Unsupervised non-parametric change point detection in quasi-periodic signals
We propose a new unsupervised and non-parametric method to detect change
points in intricate quasi-periodic signals. The detection relies on optimal
transport theory combined with topological analysis and the bootstrap
procedure. The algorithm is designed to detect changes in virtually any
harmonic or a partially harmonic signal and is verified on three different
sources of physiological data streams. We successfully find abnormal or
irregular cardiac cycles in the waveforms for the six of the most frequent
types of clinical arrhythmias using a single algorithm. The validation and the
efficiency of the method are shown both on synthetic and on real time series.
Our unsupervised approach reaches the level of performance of the supervised
state-of-the-art techniques. We provide conceptual justification for the
efficiency of the method and prove the convergence of the bootstrap procedure
theoretically.Comment: 8 pages, 7 figures, 1 tabl