Unsupervised non-parametric change point detection in quasi-periodic signals
This work addresses the challenge of unsupervised change point detection in quasi-periodic signals, which is important for applications like medical monitoring, but it appears incremental as it combines existing techniques like optimal transport and bootstrap procedures.
The paper tackles the problem of detecting change points in complex quasi-periodic signals, such as physiological data, by proposing an unsupervised non-parametric method based on optimal transport and topological analysis, achieving performance comparable to supervised state-of-the-art techniques and successfully identifying abnormal cardiac cycles in six types of clinical arrhythmias.
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.