PATIENT-ADAPTED AND INTER-PATIENT ECG CLASSIFICATION USING NEURAL NETWORK AND GRADIENT BOOSTING

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10244587" target="_blank" >RIV/61989100:27240/18:10244587 - isvavai.cz</a>

Result on the web

<a href="http://nnw.cz/doi/2018/NNW.2018.28.015.pdf" target="_blank" >http://nnw.cz/doi/2018/NNW.2018.28.015.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2018.28.015" target="_blank" >10.14311/NNW.2018.28.015</a>

Result language

angličtina

Original language name

PATIENT-ADAPTED AND INTER-PATIENT ECG CLASSIFICATION USING NEURAL NETWORK AND GRADIENT BOOSTING

Original language description

Heart disease diagnosis is an important non-invasive technique. Therefore, there exists an effort to increase the accuracy of arrhythmia classification based on ECG signals. In this work, we present a novel approach of heart arrhythmia detection. The model consists of two parts. The first part extracts important features from raw ECG signal using Auto-Encoder Neural Network. Extracted features obtained by Auto-Encoder represent an input for the second part of the model, the Gradient Boosting and Feedforward Neural Network classifiers. For comparison purposes, we evaluated our approach by using MIT-BIH ECG database and also following recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for ECG class labeling. We divided our experiment into two scenarios. The first scenario represents the classification task for the patient-adapted paradigm and the second one was dedicated to the inter-patient paradigm. We compared the measured results to the state-of-the-art methods and it shows that our method outperforms the state-of-the art methods in the Ventricular Ectopic (VEB) class for both paradigms and Supraventricular Ectopic (SVEB) class in the inter-patient paradigm.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

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

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

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

Name of the periodical

Neural Network World

ISSN

1210-0552

e-ISSN

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Volume of the periodical

28

Issue of the periodical within the volume

3

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

14

Pages from-to

241-254

UT code for WoS article

000440210500004

EID of the result in the Scopus database

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