Automatic Generation of Personalised Alert Thresholds for Patients with COPD

Carmelo Velardo*, Syed Ahmar Shah, Oliver Gibson, Heather Rutter, Andrew Farmer, Lionel Tarassenko

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Chronic Obstructive Pulmonary Disease (COPD) is a chronic disease predicted to become the third leading cause of death by 2030. Patients with COPD are at risk of exacerbations in their symptoms, which have an adverse effect on their quality of life and may require emergency hospital admission. Using the results of a pilot study of an m-Health system for COPD self-management and tele-monitoring, we demonstrate a data-driven approach for computing personalised alert thresholds to prioritise patients for clinical review. Univariate and multivariate methodologies are used to analyse and fuse daily symptom scores, heart rate, and oxygen saturation measurements. We discuss the benefits of a multivariate kernel density estimator which improves on univariate approaches.

Original languageEnglish
Title of host publication2014 PROCEEDINGS OF THE 22ND EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1990-1994
Number of pages5
ISBN (Electronic)978-0-9928-6261-9
Publication statusPublished - 13 Nov 2014
Event22nd European Signal Processing Conference (EUSIPCO) - Lisbon, Portugal
Duration: 1 Sept 20145 Sept 2014

Publication series

NameEuropean Signal Processing Conference
PublisherIEEE
ISSN (Print)2076-1465

Conference

Conference22nd European Signal Processing Conference (EUSIPCO)
Country/TerritoryPortugal
CityLisbon
Period1/09/145/09/14

Keywords / Materials (for Non-textual outputs)

  • m-Health
  • novelty detection
  • COPD
  • chronic diseases
  • digital health
  • NOVELTY DETECTION

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