| dc.description.abstract | This thesis describes the development and implementation of a bowel sound detection
algorithm, which ultimately might be used as part of a meal detection system in an artificial
pancreas. An artificial pancreas is an advanced blood glucose regulation system for patients with diabetes. In order to correctly provide insulin doses to the user, artificial pancreases need to detect when the patient is eating. One possible approach for meal detection is monitoring bowel sound occurrence frequency. This further necessitates a bowel sound detection system, such as the one described in this thesis.
The detection algorithm will consist of a combination of signal processing techniques
for feature extraction, and pattern recognition in order to classify sound segments as either
bowel sound segments or non-bowel sound segments. A linear support vector machine was
chosen for classification in the final solution. The Python implementation of the detection
system yielded promising results on a provided data set of sound measurements from the
stomach. However, certain improvements and refinements should be done to the system
before potential integration into an artificial pancreas. The thesis also briefly discusses
these further development needs.
Chapter 1 explains the motivation behind the assignment, as well as the problem description
and approach. Chapter 2 provides the most important theoretical background for the
work described in this thesis. Chapters 3 and 4 describe the method and work towards the
final product. These chapters also contain intermediate results which constitute the basis
for decisions made throughout the process. Results obtained from the final solution can be
found in chapter 5, while chapter 6 summarizes and concludes the report. | |
