论文标题
准周期信号中无监督的非参数变更点检测
Unsupervised non-parametric change point detection in quasi-periodic signals
论文作者
论文摘要
我们提出了一种新的无监督和非参数方法，以检测复杂的准周期信号中的变化点。该检测依赖于最佳运输理论与拓扑分析和引导程序相结合。该算法旨在检测几乎任何谐波或部分谐波信号的变化，并在生理数据流的三个不同来源进行验证。我们成功地发现了使用单个算法的六种最常见的临床心律不齐类型的六种类型的波形中的异常或不规则的心脏周期。该方法的验证和效率均显示在合成和实时序列上。我们无监督的方法达到了监督最新技术的性能水平。我们为该方法的效率提供了概念上的理由，并从理论上证明了引导程序的收敛性。
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.
