Barnett Ezekiel, Kaiser Olga, Masci Jonathan, Wit Ernst C, Fulda Stephany
NNAISENSE, 6900 Lugano, Switzerland.
Institute of Computing, Università della Svizzera Italiana, 6962 Lugano, Switzerland.
Clocks Sleep. 2024 Jul 23;6(3):359-388. doi: 10.3390/clockssleep6030025.
We present the Gaussian Mixture Periodicity Detection Algorithm (GMPDA), a novel method for detecting periodicity in the binary time series of event onsets. The GMPDA addresses the periodicity detection problem by inferring parameters of a generative model. We introduce two models, the Clock Model and the Random Walk Model, which describe distinct periodic phenomena and provide a comprehensive generative framework. The GMPDA demonstrates robust performance in test cases involving single and multiple periodicities, as well as varying noise levels. Additionally, we evaluate the GMPDA on real-world data from recorded leg movements during sleep, where it successfully identifies expected periodicities despite high noise levels. The primary contributions of this paper include the development of two new models for generating periodic event behavior and the GMPDA, which exhibits high accuracy in detecting multiple periodicities even in noisy environments.
我们提出了高斯混合周期性检测算法（GMPDA），这是一种用于检测事件发作二元时间序列中周期性的新方法。GMPDA通过推断生成模型的参数来解决周期性检测问题。我们引入了两种模型，时钟模型和随机游走模型，它们描述了不同的周期性现象，并提供了一个全面的生成框架。在涉及单周期和多周期以及不同噪声水平的测试案例中，GMPDA表现出强大的性能。此外，我们在睡眠期间记录的腿部运动的真实数据上评估了GMPDA，在高噪声水平下它成功识别出了预期的周期性。本文的主要贡献包括开发了两种用于生成周期性事件行为的新模型以及GMPDA，即使在嘈杂环境中，GMPDA在检测多个周期性方面也具有很高的准确性。
