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Towards an affordable magnetomyography instrumentation and low model complexity approach for labour imminency prediction using a novel multiresolution analysis
  • Ejay Nsugbe,
  • Ibrahim Sanusi
Ejay Nsugbe

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Ibrahim Sanusi
Alten UK
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The ability to predict the onset of labour is seen to be an important tool in a clinical setting. Magnetomyography has shown promise in the area of labour imminency prediction, but its clinical application remains limited due to high resource consumption associated with its broad number of channels. In this study, five electrode channels, which account for 3.3% of the total, are used alongside a novel signal decomposition algorithm and low complexity classifiers (logistic regression and linear-SVM) to classify between labour imminency due within 0–48hrs and >48hrs. The results suggest that the parsimonious representation comprising of five electrode channels and novel signal decomposition method alongside the candidate classifiers could allow for greater affordability and hence clinical viability of the magnetomyography-based prediction model, which carries a good degree of model interpretability.
07 Feb 2021Submitted to Applied AI Letters
08 Feb 2021Submission Checks Completed
08 Feb 2021Assigned to Editor
09 Feb 2021Reviewer(s) Assigned
30 Apr 2021Review(s) Completed, Editorial Evaluation Pending
06 May 2021Editorial Decision: Revise Major
10 May 20211st Revision Received
11 May 2021Submission Checks Completed
11 May 2021Assigned to Editor
12 May 2021Reviewer(s) Assigned
09 Jun 2021Review(s) Completed, Editorial Evaluation Pending
09 Jun 2021Editorial Decision: Accept