An efficient technique for automated diagnosis of cardiac rhythms using electrocardiogram

Usha Desai, C. Gurudas Nayak, G. Seshikala

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

8 Citations (Scopus)

Abstract

Electrocardiogram (ECG) is the most reliable and low-cost diagnostic tool to evaluate the patients with cardiac arrhythmias. Manual diagnosis of arrhythmia beats is very tedious due to the nonlinear and complex nature of ECG. The current paper, describes pattern recognition and machine learning-based approach for computer-Aided detection of five classes of ECG arrhythmia beats using Discrete Cosine Transform (DCT) coefficients. Further, methodology comprises dimensionality reduction using Independent Component Analysis (ICA), ten-fold cross-validation and diagnosis using Support Vector Machine (SVM) quadratic kernel function. Using ANOVA clinically (p<0.05) and statistically (F-value) significant features are selected and reliability of accuracy is measured by Cohen's kappa (κ) statistic. Large database of 110,093 heartbeats from 48 records of MIT-BIH Arrhythmia Database recommended by ANSI/AAMI EC57:1998, which are grouped into five classes of arrhythmia beats viz. Non-ectopic (N), Ventricular ectopic (V), Supraventricular ectopic (S), Fusion (F) and Unknown (U) are classified with class-specific accuracy of 98.75%, 89.38%, 82.2%, 47.04% and 90.57%, respectively and an overall accuracy of 95.98%. The developed methodology is an efficient tool, which has intensive applications in early diagnosis, mass screening of cardiac health and in cardiac theoretic devices such as pacemaker systems.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5-8
Number of pages4
ISBN (Electronic)9781509007745
DOIs
Publication statusPublished - 05-01-2017
Event1st IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Bangalore, India
Duration: 20-05-201621-05-2016

Conference

Conference1st IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016
CountryIndia
CityBangalore
Period20-05-1621-05-16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Electrical and Electronic Engineering

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  • Cite this

    Desai, U., Nayak, C. G., & Seshikala, G. (2017). An efficient technique for automated diagnosis of cardiac rhythms using electrocardiogram. In 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings (pp. 5-8). [7807770] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RTEICT.2016.7807770