Using evolutionary algorithms for ECG Arrhythmia detection and classification

Komal Waseem, Awais Javed, Rashad Ramzan, Muddassar Farooq

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

The electrocardiogram (ECG) is the most clinically accepted diagnostic tool used by physicians for interpreting the functional activity of the heart. The existing ECG machines require an expert-in-the-loop for identifying abnormalities in cardiac activity - commonly referred to as Arrhythmia - of a patient. The accuracy of diagnosis is directly dependent on the skill set of the physician; as a result, in rural and remote places, where no ECG specialist wants to relocate, the patients are unable to get any help in case of life threatening arrhythmias. In this paper, we investigate the suitability of evolutionary algorithms to discriminate a normal ECG from an abnormal one with minimum user intervention. Consequently, the human dependent errors are minimized. The intelligent framework is efficient and can be used for realtime ECG analysis to complement the diagnostic efficiency and accuracy of ECG specialists. Moreover, the system could also be used to raise early alarms for patients where no ECG specialist is available. In this paper, we aim at autonomously detecting six types of Arrhythmia: (1) Tachycardia, (2) Bradycardia, (3) Right Bundle Branch Block, (4) Left Bundle Branch Block, (5) Old Inferior Myocardial Infarction, and (6) Old Anterior Myocardial Infarction. We evaluate the accuracy of our system by selecting the best back end classifier from a set of 8 evolutionary classifiers. The results of our experiments show that our system is able to achieve more than 98% accuracy in detecting most types of Arrhythmia.

Original languageEnglish
Title of host publicationProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
Pages2386-2390
Number of pages5
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai, China
Duration: Jul 26 2011Jul 28 2011

Publication series

NameProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
Volume4

Other

Other2011 7th International Conference on Natural Computation, ICNC 2011
Country/TerritoryChina
CityShanghai
Period7/26/117/28/11

Keywords

  • arrhythmia
  • electrocardiogram
  • evolutionary
  • genetic

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • General Neuroscience

Fingerprint

Dive into the research topics of 'Using evolutionary algorithms for ECG Arrhythmia detection and classification'. Together they form a unique fingerprint.

Cite this