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Weighted Time Warping for Temporal Segmentation of Multi-Parameter Physiological Signals

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dc.contributor.author Ganeshapillai, Gartheeban
dc.contributor.author Guttag, John V.
dc.date.accessioned 2012-10-12T18:34:58Z
dc.date.available 2012-10-12T18:34:58Z
dc.date.issued 2011-01
dc.identifier.isbn 978-989-8425-35-5
dc.identifier.uri http://hdl.handle.net/1721.1/73944
dc.description.abstract We present a novel approach to segmenting a quasiperiodic multi-parameter physiological signal in the presence of noise and transient corruption. We use Weighted Time Warping (WTW), to combine the partially correlated signals. We then use the relationship between the channels and the repetitive morphology of the time series to partition it into quasiperiodic units by matching it against a constantly evolving template. The method can accurately segment a multi-parameter signal, even when all the individual channels are so corrupted that they cannot be individually segmented. Experiments carried out on MIMIC, a multi-parameter physiological dataset recorded on ICU patients, demonstrate the effectiveness of the method. Our method performs as well as a widely used QRS detector on clean raw data, and outperforms it on corrupted data. Under additive noise at SNR 0 dB the average errors were 5:81 ms for our method and 303:48 ms for the QRS detector. Under transient corruption they were 2:89 ms and 387:32 ms respectively. en_US
dc.language.iso en_US
dc.publisher Biosignals en_US
dc.relation.isversionof http://www.biosignals.biostec.org/Abstracts/2011/BIOSIGNALS_2011_Abstracts.htm en_US
dc.rights Creative Commons Attribution-Noncommercial-Share Alike 3.0 en_US
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/ en_US
dc.source MIT web domain en_US
dc.title Weighted Time Warping for Temporal Segmentation of Multi-Parameter Physiological Signals en_US
dc.type Article en_US
dc.identifier.citation "Weighted Time Warping for Temporal Segmentation of Multi-parameter Physiological Signals." Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, Rome, Italy, (January, 2011) 125-131. en_US
dc.contributor.department Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science en_US
dc.contributor.mitauthor Ganeshapillai, Gartheeban
dc.contributor.mitauthor Guttag, John V.
dc.relation.journal Proceedings of the International Conference on Bio-inspired Systems and Signal Processing en_US
dc.identifier.mitlicense OPEN_ACCESS_POLICY en_US
dc.eprint.version Author's final manuscript en_US
dc.type.uri http://purl.org/eprint/type/ConferencePaper en_US


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Creative Commons Attribution-Noncommercial-Share Alike 3.0 Except where otherwise noted, this item's license is described as Creative Commons Attribution-Noncommercial-Share Alike 3.0
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