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Adaptive Segmentation Optimization for Sleep Spindle Detector

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F16%3A00307027" target="_blank" >RIV/68407700:21730/16:00307027 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21460/16:00307027 RIV/68407700:21230/16:00307027

  • Result on the web

    <a href="http://80.link.springer.com.dialog.cvut.cz/chapter/10.1007/978-3-319-43949-5_6" target="_blank" >http://80.link.springer.com.dialog.cvut.cz/chapter/10.1007/978-3-319-43949-5_6</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-319-43949-5_6" target="_blank" >10.1007/978-3-319-43949-5_6</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Adaptive Segmentation Optimization for Sleep Spindle Detector

  • Original language description

    Segmentation is a crucial part of the signal processing as it has a significant influence on further analysis quality. Adaptive segmentation based on sliding windows is relatively simple, works quite good and can work online. It has however many tunable parameters whose proper values depend on the task and signal type. The paper proposes a method of defining optimal parameters for detection of sleep spindles in electroencephalogram. Segmentation algorithm based on Varri method was utilized. Fitness function was proposed for estimation of agreement between the segmentation result and borders of the target classification. Particle swarm optimization was used to find optimal parameters. On the data of 11 insomniac subjects the method reached 28% improvement in comparison to the baseline method using default parameters.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JC - Computer hardware and software

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GP13-21696P" target="_blank" >GP13-21696P: Feature selection for temporal context aware models of multivariate time series</a><br>

  • Continuities

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

Others

  • Publication year

    2016

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Information Technology in Bio- and Medical Informatics

  • ISBN

    978-3-319-43948-8

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    12

  • Pages from-to

    85-96

  • Publisher name

    Springer

  • Place of publication

    Basel

  • Event location

    Porto

  • Event date

    Sep 5, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000389336800006