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Automatic Diagnostic System for Long-Term ECG Data from Holter Monitor

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 47 - Number 20
Year of Publication: 2012
Authors:
Ghousia Begum S.
Vipula Singh
10.5120/7303-0502

Ghousia Begum S. and Vipula Singh. Article: Automatic Diagnostic System for Long-Term ECG Data from Holter Monitor. International Journal of Computer Applications 47(20):16-21, June 2012. Full text available. BibTeX

@article{key:article,
	author = {Ghousia Begum S. and Vipula Singh},
	title = {Article: Automatic Diagnostic System for Long-Term ECG Data from Holter Monitor},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {47},
	number = {20},
	pages = {16-21},
	month = {June},
	note = {Full text available}
}

Abstract

An Electrocardiogram (ECG) gives significant information for the cardiologist to detect cardiac diseases. . Automation algorithm is essential to analyse long ECG data. In this paper, we have proposed fully automated, high efficiency, accurate and fast algorithm to detect abnormalities in ECG based on wavelet transform. The algorithm consists of pre-processing, feature extraction and diagnosis. Number of heart beats and Premature Ventricular Contraction (PVC), Premature Atrial Contractions (PACs), Supraventricular tachyarrhythmia and Bradycardia are diagnosed accurately and result matches with doctors opinion. The average sensitivity of algorithm is 99. 70%.

References

  • Naregalkar Akshay et al. 2010. ECG Noise Removal and QRS Complex Detection Using UWT. International Conference on Electronics and Information Engineering (ICEIE).
  • P. Sasikala et al. 2010. Robust R Peak and QRS detection in Electrocardiogram using Wavelet Transform. International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 1, No. 6.
  • MujeebRahman et al. 2011. An Algorithm for Detection of Arrhythmia. IEEE Ajman University of Science & Technology.
  • S. Karpagachelvi et al. 2010. ECG Feature Extraction Techniques - A Survey Approach. International Journal of Computer Science and Information Security (IJCSIS), Vol. 8, No. 1.
  • A. K. M. FazlulHaque et al. 2009. Detection Of Small Variations Of ECG Features Using Wavelet. ARPN Journal of Engineering and Applied Sciences, Vol. 4, No. 6.
  • BachirBoucheham. 2011. Abnormality Detection in Electrocardiograms by Time Series Alignment. Communications in Information Science and Management Engineering (CISME), Vol. 1 No. 3.
  • Mohammad Niknazar et al. 2009. Detection of Characteristic Points of ECG using Quadratic Spline Wavelet Transfrom. International Conference on Signals, Circuits and Systems.
  • S. Z. Mahmoodabadi et al. 2005. ECG Feature Extraction Using Daubechies Wavelets. Proceedings of the Fifth IASTED International Conference ,Benidorm, Spain.
  • C. Saritha et al. 2008. ECG Signal Analysis Using WaveletTransforms. Department of Physics and Electronics, S. S. B. N. College Anantapur, Andhrapradesh, India,16.
  • Qibin Zhao, and Liqing Zhan. 2005. ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines. International Conference on Neural Networks and Brain, ICNN&B '05, vol. 2, pp. 1089-1092.
  • Massachusetts Institute of Technology. 1997. Cambrige. MITBIH, database distribution.
  • M. Kania et al. 2007. Wavelet Denoising for Multi-lead High Resolution ECG Signals. Measurement Science Review, Volume 7, Section 2, No. 4,
  • E. Hostalkova et al. Wavelet Signal And Image Denoising. Institute of Chemical Technology Department of Computing and Control Engineering
  • Digvijay Ghosh. 2005. Wavelet Aided SVM Analysis of ECG Signals for Cardiac Abnormality Detection. IEEE Indicon Conference, Chennai, India.
  • K. V. L. Narayana et al. 2011. Wavelet based QRS detection in ECG using Matlab. Innovative Systems Design and Engineering, Vol 2, No7.
  • Gordan Cornelia et al, "ECG Signals Processing Using Wavelets" University of Oradea , Electronics Department, Faculty of Electrical Engineering and Information Technology Oradea, Romania.
  • Veena N. Hegde et al. 2011. Comparison of Characterizing and Data Analysis Methods for Detecting Abnormalities in ECG. IEEE.
  • R. Sudirman et. al. 2010. Modeling Of EEG Signal Sound Frequency Characteristic Using Time Frequency Analysis", Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation
  • Md. Abdullah Arafat et al. 2009. Automatic Detection Of ECG wave Boundaries Using Empirical Mode Decomposition. IEEE
  • Sonia Rezk et. al. 2011. An Algebraic Derivative-Based Method For R Wave Detection. 19th European Signal Processing Conference (EUSIPCO)
  • C. Li, C. Zheng, and C. Tai. 1995. Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng, 42:21–28
  • I. Christov. 2004. Real time electrocardiogram QRS detection using combined adaptive threshold. Biomed. Eng. Online, 3(1):p28
  • Awadhesh Pachauri et. al. 2009. Robust Detection of R-Wave Using Wavelet Technique. World Academy of Science, Engineering and Technology 56.