Abstract Details
Activity Number:
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243
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Type:
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Contributed
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Date/Time:
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Monday, August 10, 2015 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #315653
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View Presentation
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Title:
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Signal Extraction from an Innovative Noninvasive Muscular Dystrophy Biomarker Using Functional Mixed-Effects Models
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Author(s):
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Kush Kapur* and Basil Darras and Seward Rutkove
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Companies:
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Boston Children's Hospital and Boston Children's Hospital and Beth Israel Deaconess Medical Center
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Keywords:
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Functional data ;
State space approach ;
Kalman filtering and smoothing ;
Mixed-effects models
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Abstract:
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Duchenne muscular dystrophy (DMD) affects approximately 1 in every 3,500 live male births. Electrical impedance myography (EIM) is an innovative method to assess the progression of neuromuscular diseases such as DMD non-invasively. The technique relies on the application of a very low intensity, alternating electrical current at a variety of frequencies between 1 kHz to 10 MHz to a localized area of the muscle. It captures changes in the resulting voltages of the applied current as the current travels through the disease-affected tissue. In this work, we develop a functional mixed-effects model for the EIM longitudinal outcomes while taking into account of the complex frequency-temporal correlations. This model allows us to differentiate the smooth surface of the underlying signal over the pre-specified frequency range and various time-points between the DMD and control groups efficiently. Additionally, it allows us to differentiate the subject-specific deviations from the population averaged response surface. We present results using computationally efficient state space formulation in order to estimate model parameters.
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Authors who are presenting talks have a * after their name.
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