论文标题
使用谐波隐藏的马尔可夫模型识别睡眠呼吸暂停的复发
Identifying the Recurrence of Sleep Apnea Using a Harmonic Hidden Markov Model
论文作者
论文摘要
我们建议通过隐藏的马尔可夫模型对时间变化的周期性和振荡过程来建模，在该模型中，通过周期性制度的光谱特性来定义状态。国家的数量以及相关周期性未知，其作用和数量在各州之间可能有所不同。我们通过贝叶斯非参数隐藏的马尔可夫模型来解决此推理问题，假设在不同状态之间进行切换动力学的粘性层次dirichlet过程，而通过跨维度马尔特·马尔特·马尔特·马尔特·卡洛·卡洛·卡洛（Monte Carlo）卡洛·卡洛·卡洛（Monte Carlo Carlo）采样步骤探索了每个状态的周期性。我们开发了完整的贝叶斯推论算法，并说明了我们提出的方法在不同的仿真研究中的使用以及与呼吸研究有关的应用，该应用集中于检测人类呼吸痕迹中呼吸暂停实例。
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.
