Active Learning for Semi-automated Sleep Scoring

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F18%3A00316523" target="_blank" >RIV/68407700:21730/18:00316523 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-981-10-7419-6_24" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-10-7419-6_24</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-10-7419-6_24" target="_blank" >10.1007/978-981-10-7419-6_24</a>

Result language
angličtina
Original language name
Active Learning for Semi-automated Sleep Scoring
Original language description
This paper introduces the semi-automatic process using active learning methods which could improve the current state, where a human specialist has to annotate a multiple hours long polysomnographical record to sleep stages. This work is focused on the utilization of density-weighted methods of active learning, one of them turned out to be well-suited for this type of task. Moreover, we proposed several criteria for the comparison of active learning methods. The method saves more than 80% of expert’s annotation effort.
Czech name
—
Czech description
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Project
<a href="/en/project/GA17-20480S" target="_blank" >GA17-20480S: Temporal context in analysis of long-term non-stationary multidimensional signal</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Precision Medicine Powered by pHealth and Connected Health
ISBN
978-981-10-7418-9
ISSN
1680-0737
e-ISSN
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Number of pages
5
Pages from-to
139-143
Publisher name
Springer Nature Singapore Pte Ltd.
Place of publication
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Event location
Thessaloniki
Event date
Nov 18, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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