P04-Spatial geometric analysis in sleep polysomnographic data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00331865" target="_blank" >RIV/68407700:21230/18:00331865 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21460/18:00331865 RIV/68407700:21730/18:00331865

Result on the web

<a href="http://dx.doi.org/10.1016/j.clinph.2018.01.049" target="_blank" >http://dx.doi.org/10.1016/j.clinph.2018.01.049</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.clinph.2018.01.049" target="_blank" >10.1016/j.clinph.2018.01.049</a>

Result language

angličtina

Original language name

P04-Spatial geometric analysis in sleep polysomnographic data

Original language description

The study is devoted to data processing methods in automatic sleep polysomnography (PSG) analysis. The idea is in using covariance matrices a carrier of a discriminative information. In the study, we are challenging with a problem of sleep stage classification. We are trying to solve that problem using spatial geometric analysis. For experiments, we took data from seven patients; data were recorded in National Institute of Mental Health. Artifact-free segments were extracted from the data. The covariance matrix was obtained for each segment. The classification was performed using a minimum distance to a class or in k-nearest-neighbor (KNN) method. A distance between objects was calculated using Riemannian Geometry. Classification methods were tested by cross-validation scheme. Using only covariance matrix of multimodal data and without additional information divided by frequency ranges, it is possible to classify sleep stages with high accuracy: the average accuracy for KNN is 0.929, for minimum distance to a class center it is only 0.816. Advantages of the method are working with data from different domains, adjustability to a different number of channels. Support: project No. 17-20480S of GACR, project “National Institute of Mental Health (NIMH-CZ),” Grant No. ED2.1.00/03.0078 and project No. LO1611.

Czech name

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Czech description

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Type

O - Miscellaneous

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