Identifying the Recurrence of Sleep Apnea Using A Harmonic Hidden Markov Model
Identifying the Recurrence of Sleep Apnea Using A Harmonic Hidden Markov Model
复制标题
DOI：
10.1214/21-aoas1455
复制
发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Huckstepp R
中科院分区：
文献类型：
--
作者：
Hadj-Amar B;Finkenstädt B;Fiecas M;Huckstepp R
我们建议通过隐马尔可夫模型，其中的状态是通过一个周期性制度的谱特性定义的模型随时间变化的周期性和振荡过程。状态的数量是未知的，沿着的是相关的周期性，其作用和数量可能因状态而异。我们解决这个推理问题的贝叶斯非参数隐马尔可夫模型假设一个粘性的分层狄利克雷过程的切换动态之间的不同状态，而每个状态的周期性特征的跨维马尔可夫链蒙特卡罗采样步骤的装置进行了探索。我们开发了完整的贝叶斯推理算法，并说明了使用我们提出的方法，不同的模拟研究，以及呼吸研究，重点是在人类呼吸痕迹的呼吸暂停情况下的检测相关的应用程序。
We propose to model time-varying periodic and oscillatory processes by means of a hidden Markov model where the states are defined through the spectral properties of a periodic regime. The number of states is unknown along with the relevant periodicities, the role and number of which may vary across states. We address this inference problem by a Bayesian nonparametric hidden Markov model assuming a sticky hierarchical Dirichlet process for the switching dynamics between different states while the periodicities characterizing each state are explored by means of a trans-dimensional Markov chain Monte Carlo sampling step. We develop the full Bayesian inference algorithm and illustrate the use of our proposed methodology for different simulation studies as well as an application related to respiratory research which focuses on the detection of apnea instances in human breathing traces.