Inference of mechanical states of intestinal motor activity using hidden Markov models

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    Background: Contractions and relaxations of the muscle layers within the digestive tract alter the external diameter and the internal pressures. These changes in diameter and pressure move digesting food and waste products. Defining these complex relationships is a fundamental step for neurogastroenterologists to be able define normal and abnormal gut motility. Results: Utilising an in vitro technique that allows for the simultaneous recording of intraluminal pressure (manometry) and gut diameter (video) in an isolated section of rabbit colon, we developed a technique to help define the mechanical states of the muscle at any point in space and time during actual peristaltic movements. This was achieved by directly relating the changes in pressure to the changes in diameter along the length of the gut studied. For each individual measure of pressure or diameter, 3 dynamic state components were identified; increasing or decreasing changes or a stable period. Two additional static state components, fully contracted and fully distended, were defined for the diameter. Then qualitative mechanical states of the muscle activity were defined as combinations of these state components. A hidden Markov model was used to correlate adjacent-in-time samples, and the Viterbi algorithm was used to infer the most likely sequence of mechanical states based on the observed data. From this a spatiotemporal map of the mechanical states was produced, showing the regions of active contractions, active relaxations, or passive states along the length of the gut throughout the entire recording period. Conclusions: The identification of mechanical muscles states based on gut diameter and intraluminal pressure was possible by modelling muscle activation with a hidden Markov model.

    Original languageEnglish
    Article number14
    Number of pages10
    Issue number1
    Publication statusPublished - 11 Dec 2013


    • Manometry
    • Muscle mechanics
    • Peristalsis
    • Time-series analysis


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