CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Real Time ECG Feature Extraction and Arrhythmia Detection on a Mobile Platform

by Abhilasha M. Patel, Pankaj K. Gakare, A. N. Cheeran
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 23
Year of Publication: 2012
Authors: Abhilasha M. Patel, Pankaj K. Gakare, A. N. Cheeran
10.5120/6432-8840

Abhilasha M. Patel, Pankaj K. Gakare, A. N. Cheeran . Real Time ECG Feature Extraction and Arrhythmia Detection on a Mobile Platform. International Journal of Computer Applications. 44, 23 ( April 2012), 40-45. DOI=10.5120/6432-8840

@article{ 10.5120/6432-8840,
author = { Abhilasha M. Patel, Pankaj K. Gakare, A. N. Cheeran },
title = { Real Time ECG Feature Extraction and Arrhythmia Detection on a Mobile Platform },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 23 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 40-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number23/6432-8840/ },
doi = { 10.5120/6432-8840 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:36:22.479379+05:30
%A Abhilasha M. Patel
%A Pankaj K. Gakare
%A A. N. Cheeran
%T Real Time ECG Feature Extraction and Arrhythmia Detection on a Mobile Platform
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 23
%P 40-45
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Arrhythmia means abnormal rate of heart contraction which is dangerous as it may also cause death. The work proposed in this paper mainly deals with the development of an efficient arrhythmia detection algorithm using ECG signal so that detection of arrhythmia at initial stages is possible using a smart-phone which is readily available anywhere which makes complete system mobile. Subjects for experiments included normal patients, patients with Bradycardia, Tachycardia, atrial premature contraction (APC), patients with ventricular premature contraction (PVC) and patients with Sleep Apnea. Pan-Tompkins algorithm was used to find the locations of QRS complexes and R Peaks. The algorithm to detect different arrhythmia is based on position of P wave, QRS complex, R Peak and T wave and on interval between these waves on android smart-phone. The algorithm was tested using MIT-BIH arrhythmia database. Results revealed that the system is accurate and efficient to classify arrhythmias as high overall performance (97. 3%) for the classification of the different categories of arrhythmic beats was achieved. The proposed arrhythmia detection algorithm may therefore be helpful to the clinical diagnosis.

References
  1. American Heart Association, "Heart Disease and Stroke Statistics", Dallas, Texas, 2007.
  2. Kumar VK : A novel approach to pattern recognition in real-time arrhythmia detection. Engineering in medicine and biology society, Proceedings of the annual international conference of the IEEE 1988; 1: 7-8.
  3. Giraldo BF, Marrugat J, and Carninalti P : Design of an expert system for arrhythmia diagnosis. Engineering in medicine and biology society, Proceedings of the annual international conference of the IEEE 1992; 3, 1255-1256.
  4. Giraldo BF, Binia M, Marrugat J and Caminal P : Arrhythmia diagnosis system: validation methodology. Engineering in medicine and biology society. IEEE 17th annual conference 1995; 1: 737- 738.
  5. Dickhaus H, Gittinger J and Maier C : Classification of QRS morphology in Holter monitoring. Engineering in medicine and biology. 21st annual conference and the annual fall meeting of the biomedical engineering society 1999; 1: 270
  6. Bluetooth [Online] Available: http://www. bluetooth. org
  7. ZigBee Alliance,[Online] Available: http://www. zigbee. org
  8. RFID [Online] Available: http://www. zigbee. org
  9. Wi-Fi Alliance [Online] Available: http://www. wifi. org
  10. D. Dubin, and Rapid Interpretation of EKG's, Cover Pub Co; 6th edition, ISBN: 0912912065, 2000.
  11. B. U. Kohler, C. Hennig, R. Orglmeister, "The principles of software QRS detection", IEEE Eng. Biol. Mag. , 21(1), 2002, pp. 42-57.
  12. Z. Dokur, T. Olmez, E. Yazgan, O. K. Ersoy, "Detection of ECG waveforms by neural networks" Med. Eng. Phys. , vol. 19, no. 8, 1997, pp. 738-741.
  13. V. X. Afonso, W. J. Tompkins, T. Q. Nguyen, S. Luo, "ECG beat detection using filter banks", IEEE Trans. Biomed. Eng. , vol. 46, 1999, pp. 192-202.
  14. C. Li, C. Zheng, C. Tai, "Detection of ECG characteristic points using wavelet transform", IEEE Trans. Biomed. Eng. , (1), vol. 42, 1995, pp. 21-28.
  15. S. Suppappola, Y. Sun, "Nonlinear transforms of ECG signals for digital QRS detection: A quantitative analysis", IEEE Trans. Biomed. Eng. , vol. 41, 1994, pp. 397-400.
  16. J. Pan and W. J. Tompkins, "A real-time QRS detection algorithm," IEEE Trans. Biomed. Eng. , vol. BME-32, pp. 230-236, 1985.
  17. Penzel T, Kantelhardt JW, Grote L, Peter JH, and Bunde A: Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea. IEEE Trans. Biomed. Eng. 2003; 50: 1143-1151
  18. MIT Database [Online] Available: http://www. physionet. org/physiobank/database/mitdb
Index Terms

Computer Science
Information Sciences

Keywords

Ecg Android Smart-phone Mhealth Ehealth Telemedicine Tachycardia Pvc

Powered by PhDFocusTM