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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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])

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Physiological Measurement

ISSN

0967-3334

e-ISSN

—

Svazek periodika

39

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

—

Kód UT WoS článku

000444733400001

EID výsledku v databázi Scopus

2-s2.0-85054609547