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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

2018

Kód důvěrnosti údajů

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

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

—

Svazek periodika

28

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

14

Strana od-do

241-254

Kód UT WoS článku

000440210500004

EID výsledku v databázi Scopus

—