IDENTIFYING THE RECURRENCE OF SLEEP APNEA USING A HARMONIC HIDDEN MARKOV MODEL

• 成果类型: Article
• 署名作者: Hadj-Amar, Beniamino; Finkenstadt, Barbel; Fiecas, Mark; Huckstepp, Robert
• 署名单位: University of Warwick; University of Minnesota System; University of Minnesota Twin Cities; University of Warwick
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1455
• 发表日期: 2021
• 页码: 1171-1193
• 关键词: label switching problem; nonstationary time-series; monte-carlo; bayesian mixture; spectral-analysis; probabilistic functions; poisson-dirichlet; population; inference; REPRESENTATIONS

摘要

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 hiddenMarkov 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 transdimensional 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.