In radiation therapy, a treatment plan is designed to make the delivery of radiation to a target more accurate, effective, and less damaging to surrounding healthy tissues. In lung sites, the tumor is affected by the patient’s respiratory motion. Despite tumor motion, current practice still uses a static delivery plan. Unexpected changes due to coughs and sneezes are not taken into account and as a result, the tumor is not treated accurately and healthy tissues are damaged.
In this thesis we detail a framework of using an accelerometer device to detect and forecast coughs. The accelerometer measurements are modeled as a ARMA process to make forecasts. We draw from studies in cough physiology and use amplitudes and durations of the forecasted breathing cycles as features to estimate parameters of Gaussian Mixture Models for cough and normal breathing classes. The system was tested on 10 volunteers, where each data set consisted of one 3-5 minute accelerometer measurements to train the system, and two 1-3 minute accelerometer measurements for testing.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/24277 |
| Date | 06 April 2010 |
| Creators | Qiu, Zigang Jimmy |
| Contributors | Kwong, Raymond H., Alasti, Hamideh |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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