Parkinson's Disease Digital Biomarker Discovery with Optimized Transitions and Inferred Markov Emissions
This work addresses the need for better diagnostic tools in Parkinson's Disease, but it is incremental as it modifies an existing method (HMM) for a specific domain.
The researchers tackled the problem of discovering digital biomarkers for Parkinson's Disease by modeling repetitive movement patterns as hidden states with optimized transitions and emissions, resulting in a Hidden Semi-Markov Model that improved characterization of patients and controls by incorporating state durations into features.
We search for digital biomarkers from Parkinson's Disease by observing approximate repetitive patterns matching hypothesized step and stride periodic cycles. These observations were modeled as a cycle of hidden states with randomness allowing deviation from a canonical pattern of transitions and emissions, under the hypothesis that the averaged features of hidden states would serve to informatively characterize classes of patients/controls. We propose a Hidden Semi-Markov Model (HSMM), a latent-state model, emitting 3D-acceleration vectors. Transitions and emissions are inferred from data. We fit separate models per unique device and training label. Hidden Markov Models (HMM) force geometric distributions of the duration spent at each state before transition to a new state. Instead, our HSMM allows us to specify the distribution of state duration. This modified version is more effective because we are interested more in each state's duration than the sequence of distinct states, allowing inclusion of these durations the feature vector.