Hilbert-huang transform and neural networks for electrocardiogram modeling and prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F14%3A00232148" target="_blank" >RIV/68407700:21220/14:00232148 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICNC.2014.6975896" target="_blank" >http://dx.doi.org/10.1109/ICNC.2014.6975896</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICNC.2014.6975896" target="_blank" >10.1109/ICNC.2014.6975896</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hilbert-huang transform and neural networks for electrocardiogram modeling and prediction

Popis výsledku v původním jazyce

This paper presents a predictive model for the prediction and modeling of nonlinear, chaotic, and nonstationary electrocardiogram signals. The model is based on the combined usage of Hilbert-Huang transform, False nearest neighbors, and a novel neural network architecture. This model is intended to increase the prediction accuracy by applying the Empirical Mode Decomposition over a signal, and to reconstruct the signal by adding each calculated Intrinsic Mode Function and its residue. The Intrinsic Mode Function that obtains the highest frequency oscillation is not considered during the reconstruction. The optimal embedding dimension space of the reconstructed signal is obtained by False Nearest Neighbors algorithm. Finally, for the prediction horizon, a neural network retraining technique is applied to the reconstructed signal. The method has been validated using the record 103 from MIT-BIH arrhythmia database. Results are very promising since the measured root mean squared errors are 0.031, 0.05, and 0.085 of the ECG amplitude, for the prediction horizons of 0.0028, 0.0056, 0.0083 seconds, respectively.

Název v anglickém jazyce

Hilbert-huang transform and neural networks for electrocardiogram modeling and prediction

Popis výsledku anglicky

This paper presents a predictive model for the prediction and modeling of nonlinear, chaotic, and nonstationary electrocardiogram signals. The model is based on the combined usage of Hilbert-Huang transform, False nearest neighbors, and a novel neural network architecture. This model is intended to increase the prediction accuracy by applying the Empirical Mode Decomposition over a signal, and to reconstruct the signal by adding each calculated Intrinsic Mode Function and its residue. The Intrinsic Mode Function that obtains the highest frequency oscillation is not considered during the reconstruction. The optimal embedding dimension space of the reconstructed signal is obtained by False Nearest Neighbors algorithm. Finally, for the prediction horizon, a neural network retraining technique is applied to the reconstructed signal. The method has been validated using the record 103 from MIT-BIH arrhythmia database. Results are very promising since the measured root mean squared errors are 0.031, 0.05, and 0.085 of the ECG amplitude, for the prediction horizons of 0.0028, 0.0056, 0.0083 seconds, respectively.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

10th International Conference on Natural Computation, ICNC 2014; Xiamen; China; 19 August 2014 through 21 August 2014; Code 111723

ISBN

978-1-4799-5151-2

ISSN

2469-8814

e-ISSN

—

Počet stran výsledku

7

Strana od-do

561-567

Název nakladatele

IEEE

Místo vydání

Beijing

Místo konání akce

Xiamen

Datum konání akce

19. 8. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000393406200097