Title: Towards Model-Independent Mode Detection and Characterisation of Very Long Biomedical Time Series
Authors: Pauwels, Karl
Gautama, Temujin
Mandic, Danilo
Van Hulle, Marc
Issue Date: 2004
Publisher: Springer-Verlag
Host Document: Applications and Science in Soft Computing Series: Advances in Soft Computing
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.
Publication status: published
KU Leuven publication type: IHb
Appears in Collections:Research Group Neurophysiology

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