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Title Signal Processing of Nano Sensor Data
Author Jensen, Thomas
Supervisor Larsen, Jan (Intelligent Signal Processing, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Duun, Sune (Department of Micro and Nanotechnology, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Institution Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
Thesis level Master's thesis
Year 2009
Abstract Pulse oximetry is a non-invasive method for measuring the arterial oxygen saturation. The measured signals suffer from noise and motion artifacts which make the measurement results very unstable. Signal processing is therefore needed to estimate the oxygen saturation from the pulse oximetry recordings. It is found that preprocessing the signals by well designed bandpass filters is important. Simple methods previously used for pulse oximetry are implemented as well as the more advanced Discrete Saturation Transform (DST) developed by Masimo Coorporation and results are compared to solving the problem by Independent Component Analysis (ICA). The methods are tested on to data sets; one with oxygen saturation levels from 75% to 100% recorded on a resting subject and one recorded at normal oxygen saturation level but with the subject doing motions. Good results are obtained using ICA with constraints from the first data set, and the other methods perform well on this data set as well. When using the data set with motion artifacts, all methods produce unreliable results. Beside estimation of the oxygen saturation, the heart rate can be detected from pulse oximetry. Heart rate detection is carried out by using the Fast Fourier Transform (FFT) and is compared to a Bayesian periodic component detector and both methods perform equally well.
Series IMM-M.Sc.-2009-13
Fulltext
Original PDF ep09_13_net.pdf (16.70 MB)
Admin Creation date: 2009-03-11    Update date: 2009-08-06    Source: dtu    ID: 239912    Original MXD