NDLI: A real-time novelty detector for artefact identification in physiological measurements

Content Provider	IEEE Xplore Digital Library
Author	Beatty, P.C.W. Hoare, S.W. Asbridge, D.
Copyright Year	2000
Description	Author affiliation: Div. of Imaging Sci. & Biomed. Eng., Manchester Univ., UK (Beatty, P.C.W.)
Abstract	Reports the design of a kernel-based on-line novelty detector (ADDaM Automatic Dynamic Data Mapper) and its use in the detecting of artefacts in physioiogical data streams gathered during general anaesthesia. ADDaM is an on-line method, that produces a robust and principled statistically partitioned history of any ordered data stream. It then constructs a probability distribution function (PDF) of the values in the stream by placing suitable Gaussian kernels at the centres of each of the partitions. The novelty of the next point entering the stream is assessed by testing against the current PDF. The more novel the point the more likely it is to be an artefact. The partitions and the PDFs are then updated after the novelty of each new point is assessed. The performance of this method is compared with artefact detection using both conventional on and off-line methods including Kalman filters, ARIMA and moving median or mean methods. The study shows that the performance of the authors' novelty detector is as least as good as the best alternative on or off-line methods. Typically, error rates for artefact identification of 9.296 were achieved by ADDaM, compared with 16.596 for the best Kalman filtering, 9.356 for the best ARIMA model tested, 5.396 for the best moving mean method and 9.6% for the best moving median method.
Starting Page	616
Ending Page	618
File Size	229625
Page Count	3
File Format	PDF
ISBN	0780364651
ISSN	1094687X
DOI	10.1109/IEMBS.2000.900818
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-07-23
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Biomedical monitoring Filtering Testing Biomedical measurements History Kalman filters Patient monitoring Smoothing methods Robustness
Content Type	Text
Resource Type	Article

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