Není k dispozici

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F04%3A03099608" target="_blank" >RIV/68407700:21230/04:03099608 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Feature Extraction From Biological Signals: A Case Study

Popis výsledku v původním jazyce

This paper presents two case studies illustrating the problem of data preprocessing as the first step in computer-aided analysis of biological signals used in clinical decision support. Methods for data extraction from ECG and EEG signals are described.We show the differences between these two signal types and the reasons why different approaches are used for their preprocessing. Analysis of ECG records is performed by the wavelet transform, and analysis of EEG records is performed using adaptive segmentation and the Fourier transform. The wavelet transform allows good localisation of QRS complexes, P and T waves in time and amplitude. The average accuracy of detection of all events is above 87 per cent. Adaptive segmentation abstracts the EEG signaldata into stationary segments and the Fourier transform calculates their basic characteristics. In both cases extracted data are used as inputs for machine learning methods.

Název v anglickém jazyce

Feature Extraction From Biological Signals: A Case Study

Popis výsledku anglicky

This paper presents two case studies illustrating the problem of data preprocessing as the first step in computer-aided analysis of biological signals used in clinical decision support. Methods for data extraction from ECG and EEG signals are described.We show the differences between these two signal types and the reasons why different approaches are used for their preprocessing. Analysis of ECG records is performed by the wavelet transform, and analysis of EEG records is performed using adaptive segmentation and the Fourier transform. The wavelet transform allows good localisation of QRS complexes, P and T waves in time and amplitude. The average accuracy of detection of all events is above 87 per cent. Adaptive segmentation abstracts the EEG signaldata into stationary segments and the Fourier transform calculates their basic characteristics. In both cases extracted data are used as inputs for machine learning methods.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2004

Kód důvěrnosti údajů

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

Název statě ve sborníku

IEEE 4th International Conference on Intelligent Systems Design and Application

ISBN

963-7154-29-9

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

139-144

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Budapest

Datum konání akce

26. 8. 2004

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—