Iterative expert-in-the-loop classification of sleep PSG recordings using a hierarchical clustering

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F19%3A43919767" target="_blank" >RIV/00023752:_____/19:43919767 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/19:00328669 RIV/68407700:21460/19:00328669 RIV/68407700:21730/19:00328669 RIV/00216208:11110/19:10391627 RIV/00216208:11120/19:43917741

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0165027019300172?dgcid=author#" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0165027019300172?dgcid=author#</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jneumeth.2019.01.013" target="_blank" >10.1016/j.jneumeth.2019.01.013</a>

Result language

angličtina

Original language name

Iterative expert-in-the-loop classification of sleep PSG recordings using a hierarchical clustering

Original language description

Background: The classification of sleep signals is a subjective and time consuming task. A large number ofautomatic classifiers have been published in the past decade but a sleep community has no strong confidence touse them in clinical practice and still remains using a standard manual scoring according standardized rules. New method: We developed a semi-supervised data-driven approach for objective and efficient evaluation ofpolysomnographic (PSG) data. The proposed algorithm finds a representative set of signal segments that are subsequently scored by a sleep neurologist. The remaining part of the recording is then automatically classified using these templates. Results: The method was evaluated on 36 PSG recordings (18 chronic insomniacs, 18 healthy controls). We show a faster and objective evaluation of PSG data compared to the manual scoring that is over-performing automated classifiers (accuracy increases ∼14%). The classification results are comparable on both datasets. Comparison with existing method(s): The methodology that we propose has not yet been published in the area ofsleep PSG data processing. The performance of our method is comparable to various published automated approaches (a typical published classification accuracy is ∼75–95%). The method allows the evaluation of PSGrecordings in more general terms and across different recording devices and standards. Conclusions: The proposed solution is not based on a single-purpose rules or heuristics and training model is not trained on other patient&apos;s sleep recordings. The method is applicable to wide range of similar tasks and varioustypes of physiological signals.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

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

Name of the periodical

Journal of Neuroscience Methods

ISSN

0165-0270

e-ISSN

—

Volume of the periodical

317

Issue of the periodical within the volume

April

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

10

Pages from-to

61-70

UT code for WoS article

000461264000008

EID of the result in the Scopus database

2-s2.0-85061539886