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 procedu...
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Published in | Frontiers in physiology Vol. 11; p. 605066 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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11.02.2021
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ISSN | 1664-042X 1664-042X |
DOI | 10.3389/fphys.2020.605066 |
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Abstract | 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. |
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AbstractList | 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. 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.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. |
Author | Wiklendt, Lukasz Scott, Mark S. Dinning, Phil G. Brookes, Simon J. H. Costa, Marcello |
AuthorAffiliation | 2 Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Queen Mary University of London , London , United Kingdom 3 Discipline of Surgery and Gastroenterology, Flinders Medical Centre , Bedford Park, SA , Australia 1 College of Medicine and Public Health, Centre for Neuroscience, Flinders University , Bedford Park, SA , Australia |
AuthorAffiliation_xml | – name: 1 College of Medicine and Public Health, Centre for Neuroscience, Flinders University , Bedford Park, SA , Australia – name: 3 Discipline of Surgery and Gastroenterology, Flinders Medical Centre , Bedford Park, SA , Australia – name: 2 Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Queen Mary University of London , London , United Kingdom |
Author_xml | – sequence: 1 givenname: Lukasz surname: Wiklendt fullname: Wiklendt, Lukasz – sequence: 2 givenname: Marcello surname: Costa fullname: Costa, Marcello – sequence: 3 givenname: Mark S. surname: Scott fullname: Scott, Mark S. – sequence: 4 givenname: Simon J. H. surname: Brookes fullname: Brookes, Simon J. H. – sequence: 5 givenname: Phil G. surname: Dinning fullname: Dinning, Phil G. |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33643057$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.18637/jss.v076.i01 10.1016/s0016-5085(19)36410-8 10.1097/00000441-192608000-00012 10.1159/000199488 10.1136/gut.41.3.381 10.1111/nmo.12884 10.1136/gut.29.9.1173 10.1152/ajpgi.2000.278.2.G329 10.1046/j.1365-2982.2002.00371.x 10.3389/fphys.2018.01248 10.1111/nmo.12502 10.1111/j.1365-2982.2010.01597.x 10.1038/ajg.2016.341 10.1111/nmo.13807 10.3389/fnins.2011.00093 10.18637/jss.v067.i01 10.1007/bf01318114 10.1146/annurev.fl.24.010192.002143 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2 10.1111/1440-1681.12760 10.1152/ajpgi.1988.255.5.G660 10.1111/nmo.12129 10.1111/nmo.12408 10.3389/fphys.2020.00484 10.1111/nmo.12077 10.1111/j.1572-0241.2001.03924.x 10.1152/ajpgi.00227.2013 10.1175/jtech-d-11-00140.1 10.1152/ajpgi.00021.2017 10.1046/j.1365-2982.2001.00292.x 10.1016/j.acha.2010.08.002 10.1111/nmo.13395 10.1152/ajpgi.1989.257.4.G601 10.3109/03091902.2010.514973 10.1111/j.1572-0241.2000.01839.x 10.5194/npg-11-561-2004 10.1111/j.1469-7793.1998.309bz.x 10.1152/ajpgi.2001.280.4.G629 10.1111/j.1572-0241.2001.03804.x |
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Keywords | spatiotemporal analysis Gaussian process Bayesian mixed effects continuous wavelet transform colonic manometry |
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License | Copyright © 2021 Wiklendt, Costa, Scott, Brookes and Dinning. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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Title | Automated Analysis Using a Bayesian Functional Mixed-Effects Model With Gaussian Process Responses for Wavelet Spectra of Spatiotemporal Colonic Manometry Signals |
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