Abstract
During polysomnography, multivariate physiological measurements are recorded, and analysed to identify episodes of breathing disorders occur during patients sleep for the diagnosis of sleep disordered breathing disorders. Measurement distortions, such as signal losses that may occur due to loosening of a sensor, are often present in these measurements. Reliability and accuracy of automated diagnostic procedures using polysomnographic data can be improved through automated identification and recovery of such measurement distortions. In this study is an attempt towards that focusing on the respiratory measurements. Respiratory measurements are a main criterion in assessing sleep disordered breathing episodes. Treating respiratory system as a deterministic dynamic system, functional mapping that exists between two state space embeddings are approximated using artificial neural networks. Performance of the trained neural networks in identification of measurement distortions and measurement recovery are reported.
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Rathnayake, S.I., Abeyratne, U.R. (2007). Prediction of Polysomnographic Measurements. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_15
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DOI: https://doi.org/10.1007/978-3-540-76928-6_15
Publisher Name: Springer, Berlin, Heidelberg
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