Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F18%3A00495384" target="_blank" >RIV/68081731:_____/18:00495384 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1088/1361-6579/aad9ee" target="_blank" >http://dx.doi.org/10.1088/1361-6579/aad9ee</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1361-6579/aad9ee" target="_blank" >10.1088/1361-6579/aad9ee</a>

Result language

angličtina

Original language name

Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG

Original language description

The automated detection of arrhythmia in a Holter ECG signal is a challenging task due to its complex clinical content and data quantity. It is also challenging due to the fact that Holter ECG is usually affected by noise. Such noise may be the result of the regular activity of patients using the Holter ECG-partially unplugged electrodes, short-time disconnections due to movement, or disturbances caused by electric devices or infrastructure. Furthermore, regular patient activities such as movement also affect the ECG signals and, in connection with artificial noise, may render the ECG non-readable or may lead to misinterpretation of the ECG.

Czech name

—

Czech description

—

Type

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

CEP classification

—

OECD FORD branch

20602 - Medical laboratory technology (including laboratory samples analysis; diagnostic technologies) (Biomaterials to be 2.9 [physical characteristics of living material as related to medical implants, devices, sensors])

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

2018

Confidentiality

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

Name of the periodical

Physiological Measurement

ISSN

0967-3334

e-ISSN

—

Volume of the periodical

39

Issue of the periodical within the volume

9

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

—

UT code for WoS article

000444733400001

EID of the result in the Scopus database

2-s2.0-85054609547