Abstract
A novel technique, the Delay Vector Variance method, which provides model-independent characterisation of time series in terms of their predictability is introduced and applied in a biomedical context. The merits of the procedure are demonstrated in a mode segmentation context on a set of long nonstationary physiological signals, obtained from subjects undergoing different sleep and wake stages. It is shown that the features extracted remain consistent within and across subjects. Next, the presence of nonlinearity associated with the different modes is investigated. A comparison with other measures supports the obtained results, namely that the signals show a higher degree of nonlinearity during wake than during sleep stages.
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© 2004 Springer-Verlag Berlin Heidelberg
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Pauwels, K., Gautama, T., Mandic, D.P., Van Hulle, M.M. (2004). Towards Model-Independent Mode Detection and Characterisation of Very Long Biomedical Time Series. In: Lotfi, A., Garibaldi, J.M. (eds) Applications and Science in Soft Computing. Advances in Soft Computing, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45240-9_29
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DOI: https://doi.org/10.1007/978-3-540-45240-9_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40856-7
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