Publication
Title
Efficient Extreme Learning Machine (ELM) based algorithm for electrocardiogram (ECG) heartbeat classification
Author
Abstract
Electrocardiogram (ECG) estimates the electric signals activity of the human heart and is extensively used for sensing heart aberrations due to ease of use and non-invasive application on human body. Human heart is a one of the vital organs of human body. In an industrial environment, heart impairments and abnormalities are attributed to the different causes including work overload, occupational and workplace stress. Cardiovascular Disease (CD) of heart refers the conditions involving different heart’s frequency deviations and are mostly ascribed to the workplace stress, fatigue and strain. Early detection of deviated heartbeats may prevent premature morbidity and unhealthy rhythms under occupational stress. The Electrocardiography (ECG) is one of the widely used diagnostic test tools that cardiologists use to diagnose heart anomalies, impairments and diseases. Various approaches have been proposed to correctly classify the ECG signals. In this study, a fast ECG classification method based on Extreme Learning Machines (ELM) algorithm is proposed to classify the frequency rhythms in heartbeat. The MIT-BIH Arrhythmia Database having recordings of 47 subjects is used in this study. Proposed ELM method is evaluated and analyzed by dividing diagnostics datasets in 60:40 train-test split ratio and findings are compared with similar studies. Results confirm the feasibility of newly proposed ELM method both in terms of classification accuracy 97.55%, speed and computational power.
Language
English
Source (book)
Advances in Neuroergonomics and Cognitive Engineering, Proceedings of the AHFE 2020 Virtual Conferences on Neuroergonomics and Cognitive Engineering, and Industrial Cognitive Ergonomics and Engineering Psychology, July 16-20, 2020, USA / Ayaz, Hasan [edit.]; et al. [edit.]
Source (series)
Advances in intelligent systems and computing (AISC) ; 1201)
Publication
Cham : Springer , 2020
ISBN
978-3-030-51040-4 [print]
978-3-030-51041-1 [online]
DOI
10.1007/978-3-030-51041-1_41
Volume/pages
p. 312-318
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
VABB-SHW
Record
Identifier
Creation 16.07.2020
Last edited 10.06.2022
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