An Adaptive Sticky Hidden Markov Model framework enhanced the robustness of state decoding in noisy environments, demonstrating efficacy on a simulated dataset of apnea events.
The Adaptive Sticky-HMM framework provides a computationally efficient approach for robust state inference in non-stationary physiological signals, with potential for respiratory health monitoring.
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.
Wang et al. (Wed,) conducted a other in Simulated apnea events. Adaptive Sticky Hidden Markov Model (Sticky-HMM) vs. Traditional HMMs was evaluated on Robustness of state decoding. An Adaptive Sticky Hidden Markov Model framework enhanced the robustness of state decoding in noisy environments, demonstrating efficacy on a simulated dataset of apnea events.