Active Learning for Semiautomatic Sleep Staging and Transitional EEG Segments

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00328067" target="_blank" >RIV/68407700:21230/18:00328067 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21460/18:00328067 RIV/68407700:21730/18:00328067

Result on the web

<a href="https://ieeexplore.ieee.org/document/8621339" target="_blank" >https://ieeexplore.ieee.org/document/8621339</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/BIBM.2018.8621339" target="_blank" >10.1109/BIBM.2018.8621339</a>

Result language

angličtina

Original language name

Active Learning for Semiautomatic Sleep Staging and Transitional EEG Segments

Original language description

Semiautomatic sleep staging system based on EEG classification is focused in the paper. Such an expert-in-the-loop system interacts with the annotating expert by suggesting him EEG segments that should be annotated. After a sufficient number of labeled segments is reached, a pattern classification model is trained and used for automatic annotation of the rest of the signal. It is shown that this can save 85% of the labeling effort and consequently improve the annotation quality due to the prevention of errors caused by expert’s fatigue. The selection of the data for labeling is based on confidence based active learning approach. It is shown that such a strategy is statistically significantly better in terms of mean class error than baseline random sampling strategy. Moreover, it is argued that transitional instances that correspond to transitions between sleep stages are often erroneously labeled and their elimination can improve especially the active learning process. This hypothesis is examined and surprisingly such elimination significantly improved the random sampling strategy which became comparable to the active learning without removal of transitions. Although the active learning strategy with transitions removal performed better in terms of mean, there were not sufficient data that would prove this statistically.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Proceedings

ISBN

978-1-5386-5488-0

ISSN

—

e-ISSN

—

Number of pages

7

Pages from-to

2621-2627

Publisher name

IEEE

Place of publication

—

Event location

Madrid

Event date

Dec 3, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000458654000449