  Objective: We present a technique for identification and statistical analysis
of quasiperiodic spatiotemporal pressure signals recorded from multiple closely
spaced sensors in the human colon. Methods: Identification is achieved by
computing the continuous wavelet transform and cross-wavelet transform of these
recorded signals. Statistical analysis is achieved by modelling the resulting
time-averaged amplitudes or coherences in the frequency and frequency-phase
domains as Gaussian processes over a regular grid, under the influence of
categorical and numerical predictors that are specified by the experimental
design as a mixed-effects model. Parameters of the model are inferred with
Hamiltonian Monte Carlo. Results and Conclusion: We present an application of
this method to colonic manometry data in healthy controls, to determine
statistical differences in the spectra of pressure signals between different
colonic regions and in response to a meal intervention. We are able to
successfully identify and contrast features in the spectra of pressure signals
between various predictors. Significance: This novel method provides fast
analysis of manometric signals at levels of detail orders of magnitude beyond
what was previously available. The proposed tractable mixed-effects model is
broadly applicable to experimental designs with functional responses.
