ManoMap: an automated system for characterization of colonic propagating contractions recorded by high-resolution manometry.
Rationale: Colonic high-resolution manometry (cHRM) is an emerging clinical tool for defining colonic function in health and disease. Current analysis methods are conducted manually, thus being inefficient and open to interpretation bias.Objective: The main objective of the study was to build an aut...
| Published in: | Medical & Biological Engineering & Computing Vol. 59; no. 2; pp. 417 - 430 |
|---|---|
| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Feb2021
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148630199&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148630199 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2021 vid: 59 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148630199 148298040 148630199 NLM33496911 10.1007/s11517-021-02316-y NLM33496911 148630199 ppf: 417 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: ManoMap: an automated system for characterization of colonic propagating contractions recorded by high-resolution manometry. aug: au: Paskaranandavadivel, Niranchan Lin, Anthony Y. Cheng, Leo K. Bissett, Ian Lowe, Andrew Arkwright, John Mollaee, Saeed Dinning, Phil G. O'Grady, Gregory affil: Auckland Bioengineering Institute, University of Auckland, Private Bag 92019, 1142, Auckland, New Zealand sug: subj: Constipation Colon Manometry Algorithms Signal Processing, Computer Assisted Scales ab: Rationale: Colonic high-resolution manometry (cHRM) is an emerging clinical tool for defining colonic function in health and disease. Current analysis methods are conducted manually, thus being inefficient and open to interpretation bias.Objective: The main objective of the study was to build an automated system to identify propagating contractions and compare the performance to manual marking analysis.Methods: cHRM recordings were performed on 5 healthy subjects, 3 subjects with diarrhea-predominant irritable bowel syndrome, and 3 subjects with slow transit constipation. Two experts manually identified propagating contractions, from five randomly selected 10-min segments from each of the 11 subjects (72 channels per dataset, total duration 550 min). An automated signal processing and detection platform was developed to compare its effectiveness to manually identified propagating contractions. In the algorithm, individual pressure events over a threshold were identified and were then grouped into a propagating contraction. The detection platform allowed user-selectable thresholds, and a range of pressure thresholds was evaluated (2 to 20 mmHg).Key Results: The automated system was found to be reliable and accurate for analyzing cHRM with a threshold of 15 mmHg, resulting in a positive predictive value of 75%. For 5-h cHRM recordings, the automated method takes 22 ± 2 s for analysis, while manual identification would take many hours.Conclusions: An automated framework was developed to filter, detect, quantify, and visualize propagating contractions in cHRM recordings in an efficient manner that is reliable and consistent. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|