Detection of Sleep Stages in Temporal Profiles in Neonatal EEG—k-NN versus k-Means Approach: A Feasibility Study

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F19%3A00321988" target="_blank" >RIV/68407700:21460/19:00321988 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/19:00321988

Result on the web

<a href="http://dx.doi.org/10.1007/978-981-10-9038-7_96" target="_blank" >http://dx.doi.org/10.1007/978-981-10-9038-7_96</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-10-9038-7_96" target="_blank" >10.1007/978-981-10-9038-7_96</a>

Result language

angličtina

Original language name

Detection of Sleep Stages in Temporal Profiles in Neonatal EEG—k-NN versus k-Means Approach: A Feasibility Study

Original language description

The aim of this feasibility study is to experimentally verify the detection of changes of sleep stages in neonates with our proposed semi-automated approach using k-NN classification in comparison with a fully automated approach using simple k-means cluster analysis for classification (instead of k-NN). Our semi-automatic approach uses the k-NN classifier trained on etalons (prototypes) created by semi-automated etalons extraction (k-means for etalons suggestion and expert-in-the-loop for verification). Both methods are compared to labelling of sleep stages made by an experienced physician Dr. K. Paul. An EEG recording of full-term neonate is chosen from group of EEG recordings: full-term and preterm neonates recorded from eight electrodes positioned in standard conditions. The EEG recording is digitally preprocessed by mean-removal filter (no other filters are applied) and segmented adaptively. For each segment, 24 features are extracted and send to two classification processes: k-means and k-NN. Classified segments are plotted in temporal profiles (class membership in time) that are analysed for sleep stages using our method of creating a single detection curve from all channels and a threshold is applied on this detection curve to detect sleep stages.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20601 - Medical engineering

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

2019

Confidentiality

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

Article name in the collection

World Congress on Medical Physics and Biomedical Engineering 2018 (Vol. 2)

ISBN

978-981-10-9037-0

ISSN

1680-0737

e-ISSN

—

Number of pages

5

Pages from-to

523-527

Publisher name

Springer Nature Singapore Pte Ltd.

Place of publication

—

Event location

Prague

Event date

Jun 3, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000449742700096