An Adaptive Sticky Hidden Markov Model for Robust State Inference in Non-Stationary Physiological Time Series

The accurate inference of hidden states from non-stationary physiological signals remains a significant challenge in stochastic process modeling. This paper proposes an Adaptive Sticky Hidden Markov Model (Sticky-HMM) framework designed to enhance the robustness of state decoding in noisy environments. To address the “state-flickering” issue inherent in traditional HMMs, we incorporate a “Sticky” parameter into the transition matrix, imposing a temporal penalty on spurious state switching to maintain continuity. Furthermore, we introduce a Dynamic Prior Strategy that adaptively calibrates self-transition probabilities by mapping frequency-domain features of the observed sequence to the model’s parameter space. The proposed decoding process employs a two-pass refinement strategy and the Viterbi algorithm in the logarithmic domain to ensure numerical stability. The model’s efficacy was validated using a high-fidelity dataset of simulated apnea events. This work provides a computationally efficient and mathematically rigorous approach that demonstrates strong potential for long-term respiratory health monitoring.