Hilbert transform and neural networks for identification and modeling of ECG complex

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F13%3A00211084" target="_blank" >RIV/68407700:21220/13:00211084 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Hilbert transform and neural networks for identification and modeling of ECG complex
Original language description
This paper presents a method for modeling and identification of electrocardiogram signals; the proposed method consists of two phases; the first one is focused on obtaining the period of an ECG signal using a procedure of autocorrelation. The second phase obtains R-peaks using the Hilbert transform. Finally, an Artificial Neural Network using a retraining technique is applied for the prediction stage; this has been validated using the record 100 from the MIT-BIH arrhythmia database. Results confirm thatthe presented approach for detection of the ECG complex obtains 100% accuracy. The performance of the prediction method is promising due to the root mean squared errors of the prediction are of 0.029, 0.04, and 0.059 of the ECG amplitude, for 1, 2, and3 steps ahead, respectively.
Czech name
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Czech description
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Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2013 3rd International Conference on Innovative Computing Technology, INTECH 2013
ISBN
978-1-4799-0047-3
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
327-332
Publisher name
IEE
Place of publication
London
Event location
London
Event date
Aug 29, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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