Hidden Markov models can effectively capture the beat-to-beat dynamics of noncontact carotid pulse measurements using laser Doppler vibrometry, reflecting respiratory effort.
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Supports noncontact respiratory monitoring development; leaves open clinical validation and utility.
The method of laser Doppler vibrometry (LDV) is used to sense movements of the skin overlying the carotid artery. When pointed at the skin overlying the carotid artery, the mechanical movements of the skin disclose physiological activity relating to the blood pressure pulse over the cardiac cycle. In this paper, signal modeling is addressed, with close attention to the underlying physiology. Segments of the LDV signal corresponding to single heartbeats, called LDV pulses, are extracted. Hidden Markov models (HMMs) are used to capture the dynamics of the LDV pulses from beat to beat based on pulse morphology; under resting conditions these dynamics are primarily due to respiration-related effects. LDV pulses are classified according to state, by computing the optimal state path through the data using trained HMMs. HMM state dynamics are examined within the context of respiratory effort using strain gauges placed around the abdomen. This study presented here provides a graphical model approach to modeling the dependence of the LDV pulse on latent states.
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Kaplan et al. (2011) studied this question.
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