Colonic pressure data processing based on independent component analysis

Automated Analysis Using a Bayesian Functional Mixed-Effects Model With Gaussian Process Responses for Wavelet Spectra of Spatiotemporal Colonic Manometry Signals

Manual analysis of human high-resolution colonic manometry data is time consuming, non-standardized and subject to laboratory bias. In this article we present a technique for spectral analysis and statistical inference of quasiperiodic spatiotemporal signals recorded during colonic manometry procedures. Spectral analysis is achieved by computing the continuous wavelet transform and cross-wavelet transform of these signals. Statistical inference is achieved by modeling the resulting time-averaged amplitudes in the frequency and frequency-phase domains as Gaussian processes over a regular grid, under the influence of categorical and numerical predictors specified by the experimental design as a functional mixed-effects model. Parameters of the model are inferred with Hamiltonian Monte Carlo. Using this method, we re-analyzed our previously published colonic manometry data, comparing healthy controls and patients with slow transit constipation. The output from our automated method, supports and adds to our previous manual analysis. To obtain these results took less than two days. In comparison the manual analysis took 5 weeks. The proposed mixed-effects model approach described here can also be used to gain an appreciation of cyclical activity in individual subjects during control periods and in response to any form of intervention.

Classification of normal and abnormal colonic motility based on cross-correlations of pancolonic manometry data

BACKGROUND

Manual analysis of data acquired from manometric studies of colonic motility is laborious, subject to laboratory bias and not specific enough to differentiate all patients from control subjects. Utilizing a cross-correlation technique, we have developed an automated analysis technique that can reliably differentiate the motor patterns of patients with slow transit constipation (STC) from those recorded in healthy controls.

METHODS

Pancolonic manometric data were recorded from 17 patients with STC and 14 healthy controls. The automated analysis involved calculation of an indicator value derived from cross-correlations calculated between adjacent recording sites in a manometric trace. The automated technique was conducted on blinded real data sets (observed) and then to determine the likelihood of positive indicator values occurring by chance, the channel number within each individual data set were randomized (expected) and reanalyzed.

KEY RESULTS

In controls, the observed indicator value (3.2 ± 1.4) was significantly greater than that predicted by chance (0.8 ± 1.5; P < 0.0001). In patients, the observed indicator value (-2.7 ± 1.8) did not differ from that predicted by chance (-3.5 ± 1.6; P = 0.1). The indicator value for controls differed significantly from that of patients (P < 0.0001), with all individual patients falling outside of the range of indicator values for controls.

CONCLUSIONS & INFERENCES

Automated analysis of colonic manometry data using cross-correlation separated all patients from controls. This automated technique indicates that the contractile motor patterns in STC patients differ from those recorded in healthy controls. The analytical technique may represent a means for defining subtypes of constipation.