Gaussian process robust regression for noisy heart rate data
Gaussian process robust regression for noisy heart rate data
复制标题
DOI：
10.1109/tbme.2008.923118
复制
发表时间：
2008-09-01
影响因子：
4.6
通讯作者：
Brage, Soren
中科院分区：
文献类型：
--
作者：
Stegle, Oliver;Fallert, Sebastian V.;Brage, Soren
在非实验室条件下收集的心率数据提出了几个数据建模的挑战。首先，这种数据中的噪声通常不能很好地用简单的高斯描述;它有异常值和突发的错误。其次，在大规模研究中，ECG波形通常不会被完整记录，因此必须处理丢失的信息。在本文中，我们提出了一个强大的后处理模型，这样的应用程序。我们的模型来推断潜伏心率时间序列包括两个主要组成部分：无监督聚类，其次是贝叶斯回归。聚类组件使用辅助数据来学习离群值和噪声突发的结构。随后的高斯过程回归模型使用聚类分配作为先验信息，并结合了关于心脏生理学的专家知识。我们将该方法应用于广泛的心率数据，并获得令人信服的预测沿着与不确定性估计。在与现有的后处理方法的定量比较中，我们的模型实现了性能的显着提高。
Heart rate data collected during nonlaboratory conditions present several data-modeling challenges. First, the noise in such data is often poorly described by a simple Gaussian; it has outliers and errors come in bursts. Second, in large-scale studies the ECG waveform is usually not recorded in full, so one has to deal with missing information. In this paper, we propose a robust postprocessing model for such applications. Our model to infer the latent heart rate time series consists of two main components: unsupervised clustering followed by Bayesian regression. The clustering component uses auxiliary data to learn the structure of outliers and noise bursts. The subsequent Gaussian process regression model uses the cluster assignments as prior information and incorporates expert knowledge about the physiology of the heart. We apply the method to a wide range of heart rate data and obtain convincing predictions along With uncertainty estimates. In a quantitative comparison with existing postprocessing methodology, our model achieves a significant increase in performance.