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Efficient Extreme Learning Machine (ELM) Based Algorithm for Electrocardiogram (ECG) Heartbeat Classification

  • Khurram KhalilEmail author
  • Umer Asgher
  • Yasar Ayaz
  • Riaz Ahmad
  • Salman Nazir
  • Sara Ali
  • Sofia Scataglini
  • Noriyuki Oka
Conference paper
  • 332 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1201)

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.

Keywords

Cardiovascular Disease Electrocardiography (ECG) Extreme Learning Machines (ELM) Heartbeat classification Myocardial infraction 

Notes

Acknowledgment

手机体育投注平台We acknowledge School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Pakistan and European Union (EU)’s Horizon 2020, Research and Innovation Staff Exchange Evaluations (RISE) under grant agreement No 823904 - ENHANCE project (MSCA-RISE 823904) for technical support and funding.

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Khurram Khalil
    • 1
    Email author
  • Umer Asgher
    • 1
  • Yasar Ayaz
    • 1
  • Riaz Ahmad
    • 1
    • 2
  • Salman Nazir
    • 3
  • Sara Ali
    • 1
  • Sofia Scataglini
    • 4
  • Noriyuki Oka
    • 5
  1. 1.School of Mechanical and Manufacturing Engineering (SMME)National University of Sciences and Technology (NUST)IslamabadPakistan
  2. 2.Directorate of Quality Assurance and International CollaborationNational University of Sciences and Technology (NUST)IslamabadPakistan
  3. 3.Department of Maritime OperationsUniversity of South-Eastern Norway (USN)BorreNorway
  4. 4.Department of Product Development, Faculty of Design SciencesUniversity of AntwerpAntwerpBelgium
  5. 5.Department of RehabilitationNerima Ken-ikukai HospitalTokyoJapan

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