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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Hilbert-huang transform and neural networks for electrocardiogram modeling and prediction
Original language description
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.
Czech name
—
Czech description
—

Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
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
—
Number of pages
7
Pages from-to
561-567
Publisher name
IEEE
Place of publication
Beijing
Event location
Xiamen
Event date
Aug 19, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000393406200097

Similar results(10)