NDLI: Reliable signal classification using quality assurance and clustering-an application to sleep stage scoring

Content Provider	IEEE Xplore Digital Library
Author	Bentrup, J.A.
Copyright Year	1994
Description	Author affiliation: Dept. of Comput. Sci., Illinois Univ., Urbana, IL, USA (Bentrup, J.A.)
Abstract	This paper examines how quality assurance techniques can be applied to the challenging, real-world problem of sleep stage scoring. Quality assurance not only promotes the user's confidence in computer-generated scoring, but more importantly acts as a safeguard against poorly scored sleep studies. Ninety all-night studies were examined. Without quality assurance, the average agreement with a human expert was 86%. However, only 28% of the studies met the desired goal of 90% agreement. Eleven of the studies were below 80% agreement, with a low of 66%. By verifying the correctness of random samples, and performing additional work on poorly scored studies, the agreement rose to 91%, with a low of 84%. 76% of the studies met the target of 80% agreement. A second approach, using clustering to help pick the samples to be verified, further increased the average agreement to 92%, with the low rising to 86%, while the number of events needed to be examined by the somnologist decreased. Additionally, 88% of the studies met or exceeded the 90% agreement. As a result of using clustering, greater and more efficient protection is provided.
Starting Page	2717
Ending Page	2722
File Size	589695
Page Count	6
File Format	PDF
ISBN	0780321294
DOI	10.1109/ICSMC.1994.400284
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-10-02
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern classification Quality assurance Sleep Application software Protection Expert systems Computer science Humans Pattern recognition Electronic mail
Content Type	Text
Resource Type	Article

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