Non-Invasive Motion Detection and Classification in NICU Patients using Ballistographic Signals from a Pressure Sensitive Mat
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Patient movements can cause motion artifacts on physiological signals and can result in false alarms in a continuous patient monitoring environment. This thesis explores the use of centre of pressure (COP) signals from a pressure sensitive mat to (a) detect patient movement in real-time, and (b) classify the upper/lower directionality of movement. For (a), the sum-distance travelled by the COP is tracked over time using a sliding window with data from seven patients. Window-boundary-suppression led to improved motion detection with precision = 0.84 and recall = 0.71 with a window of 10 seconds. For (b), seven features were derived from the COP, and feature selection was done using out-of-bag-error ranking and sequential forward selection. It was found that using a sample imputation approach of adding ~13 minutes of hand-annotated new subject data to the training set makes the classifier most useful, producing accuracy = ~87.29%, precision = 0.90, and recall = 0.84.
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Copyright © 2022 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2022
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aziz-noninvasivemotiondetectionandclassification.pdf | 2023-05-05 | Public | Download |