This thesis presents novel techniques for the reduction of audio recordings and signal processing techniques as part of cough recognition. Evidence collected shows the reduction technique to be effective and the recognition techniques to give consistent performance across different patients. Cough is one of the commonest symptoms reported by patients to GPs. Despite this, it remains a significantly unmet medical need. At present, there exists no practical and validated technique for assessing the efficacy of therapies to treat cough on a large enough scale. Research that is presently undertaken requires fitting a patient with a recording system which will record their coughing and all other sound for a predefined period, usually 24 hours or less. This audio is then counted manually by trained cough counters to produce counts for each record which can be used as data for cough studies. Research in this field is relatively new, but a number of attempts have been made to automate this process. None so far have shown sufficient reliability or precision to be of sufficient use. The aim of this research is to analyse from the ground up signal processing techniques which can aid cough research. Specifically, the research will look into data minimisation techniques to improve the efficiency of manual counting techniques and recognition algorithmsThe research has produced a published record reduction system which can reduce 24 hour cough records down to around 10% of their original size without compromising the statistics of subsequent manual counts. Additionally, a review of signal processing techniques for cough recognition has produced a robust event detection technique and measurement techniques which have shown remarkable consistency between patients and conditions. Throughout the research a clear understanding of the limitations and possible solutions are pursued and reported on to aid further progress on what is a young and developing research field.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:607397 |
| Date | January 2013 |
| Creators | Barton, Antony James |
| Contributors | Gaydecki, Patrick |
| Publisher | University of Manchester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.research.manchester.ac.uk/portal/en/theses/signal-processing-techniques-for-data-reduction-and-event-recognition-in-cough-counting(dc73495a-35b0-4d17-a6f8-cc2f88008659).html |
Page generated in 0.0014 seconds