Neural networks and wavelet analysis in the computer interpretationof pulse oximetry data
Dowla, F.U.; Skokowski, P.G.; Leach, R.R., Jr.
Neural Networks for Signal Processing [1996] VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop
Volume , Issue , 4-6 Sep 1996 Page(s):527 - 536
Digital Object Identifier 10.1109/NNSP.1996.548383
Summary:Pulse oximeters determine the oxygen saturation level of blood by
measuring the light absorption of arterial blood. The sensor consists of
red and infrared light sources and photodetectors. A method based on
neural networks and wavelet analysis is developed for improved
saturation estimation in the presence of sensor motion. Spectral and
correlation functions of the dual channel oximetry data are used by a
backpropagation neural network to characterize the type of motion.
Amplitude ratios of red to infrared signals as a function of time scale
are obtained from the multiresolution wavelet decomposition of the
two-channel data. Motion class and amplitude ratios are then combined to
obtain a short-time estimate of the oxygen saturation level. A final
estimate of oxygen saturation is obtained by applying a 15 s smoothing
filter on the short-time measurements based on 3.5 s windows sampled
every 1.75 s. The design employs two backpropagation neural networks.
The proposed algorithm is numerically efficient and has stable
characteristics with a reduced false alarm rate with a small loss in
detection
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