Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions

Md. Mashiur Rahman; A. H. M. Zadidul Karim; Abdullah Al Mahmud; Salma Nazia Rahman

doi:doi:10.11648/j.cbb.20160404.11

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Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions

Md. Mashiur Rahman, A. H. M. Zadidul Karim, Abdullah Al Mahmud, Salma Nazia Rahman

Published in Computational Biology and Bioinformatics (Volume 4, Issue 4)

Received: 28 July 2016 Accepted: 9 August 2016 Published: 5 September 2016

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Abstract

Analysis process of electrocardiogram (ECG) is a major research interests in bio-medical signal processing. The reasons of this interest is the growth of cardiac health care activities all over the world and the rapid progress in digital computer technology which play an essential role to the detection of diseases at various stages from bio medical signals. The assessment process of diagnostic results for these bio medical signals heavily depends upon quantity, accuracy and speed. Computer based analysis is very useful in clinical therapy. In this Paper a method of analysis (ECG) signals using fractal features have been proposed and practical experiments have done to show that this method provides a good electronic diagnosis pattern for cardiac abnormality because it has been used by some specialist doctors to diagnose various types of diseases with accuracy. By the fact that ECG signals show a fractal patterns, it has been tried to find out a comparison between Katz’s and Higuchi’s method under fractal dimension (FD) of the ECG time series in a feature extraction phase. All ECG signals have been acquired from the Massachusetts Institute of Technology (MITBIH) arrhythmia database. The obtained results confirm the superiority of the Katz’s and Higuchi’s method to identify cardiac abnormality as compared to traditional one which is analyses of ECG signals based on morphology features and three ECG temporal features.(i.e. the QRS complex duration, the RR interval and the RR interval averaged over the ten last beats).

Published in	Computational Biology and Bioinformatics (Volume 4, Issue 4)
DOI	10.11648/j.cbb.20160404.11
Page(s)	27-36
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

ECG, Katz’s Method, Higuchi’s Method, Fractal Features, Heart

References

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[2]	http://www.bartleby.com/107/136.html
[3]	http://www.vhlab.umn.edu/atlas/physiology-tutorial
[4]	http://www.livescience.com/34655-human-heart.html
[5]	http://www.j-circ.or.jp/english/sessions/reports/64th-ss/nerbonne-l1.htm
[6]	http://study.com/academy/lesson/electrocardiogram-ecg-definition-wave-types.html
[7]	"ECG-simplified. Aswini Kumar M. D.". LifeHugger. Retrieved 11 February 2010.
[8]	Walraven, G. (2011). Basic arrhythmias (7th ed.), pp. 1–11
[9]	Takemi Program in International Health Dr. Taro Takemi
[10]	Mark, Jonathan B. (1998). Atlas of cardiovascular monitoring. New York: Churchill Livingstone. ISBN 0-443-08891-8.
[11]	Falconer, Kenneth (2003). Fractal Geometry. New York: Wiley. p. 308. ISBN 978-0-470-84862-3.
[12]	http://library.thinkquest.org/3493/ frames/- fractal.html.
[13]	J. M. Blackledge, (2006), “Digital Signal Processing: Mathematical and Computation Methods: Software Development and Applications”, 2nd Edition, London: Horwood Publishing Limited.
[14]	F. Melgani and L. Bruzzone, (Aug. 2004), “Classification of Hyperspectral Remote Sensing Images with Support Vector Machine”, IEEE Trans. Geosci, Remote Sens, vol. 42, no. 8: pp. 1778–1790.
[15]	MIT-BIH Arrhythmia Database from PhysioBank- Physiologic Signal Archives for Biomedical Research. Retrieved 10 March, 2013, from http://www.physionet.org/ -physiobank/database.
[16]	Accardo A., Affinito M., Carrozzi M, Bouquet F, (1997) “Use of the Fractal Dimension for the Analysis of Electroencephalographic Time Series”, Biol Cyber, 77: 339-350.

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Md. Mashiur Rahman, A. H. M. Zadidul Karim, Abdullah Al Mahmud, Salma Nazia Rahman. (2016). Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions. Computational Biology and Bioinformatics, 4(4), 27-36. https://doi.org/10.11648/j.cbb.20160404.11

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ACS Style

Md. Mashiur Rahman; A. H. M. Zadidul Karim; Abdullah Al Mahmud; Salma Nazia Rahman. Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions. Comput. Biol. Bioinform. 2016, 4(4), 27-36. doi: 10.11648/j.cbb.20160404.11

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AMA Style

Md. Mashiur Rahman, A. H. M. Zadidul Karim, Abdullah Al Mahmud, Salma Nazia Rahman. Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions. Comput Biol Bioinform. 2016;4(4):27-36. doi: 10.11648/j.cbb.20160404.11

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@article{10.11648/j.cbb.20160404.11,
  author = {Md. Mashiur Rahman and A. H. M. Zadidul Karim and Abdullah Al Mahmud and Salma Nazia Rahman},
  title = {Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions},
  journal = {Computational Biology and Bioinformatics},
  volume = {4},
  number = {4},
  pages = {27-36},
  doi = {10.11648/j.cbb.20160404.11},
  url = {https://doi.org/10.11648/j.cbb.20160404.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20160404.11},
  abstract = {Analysis process of electrocardiogram (ECG) is a major research interests in bio-medical signal processing. The reasons of this interest is the growth of cardiac health care activities all over the world and the rapid progress in digital computer technology which play an essential role to the detection of diseases at various stages from bio medical signals. The assessment process of diagnostic results for these bio medical signals heavily depends upon quantity, accuracy and speed. Computer based analysis is very useful in clinical therapy. In this Paper a method of analysis (ECG) signals using fractal features have been proposed and practical experiments have done to show that this method provides a good electronic diagnosis pattern for cardiac abnormality because it has been used by some specialist doctors to diagnose various types of diseases with accuracy. By the fact that ECG signals show a fractal patterns, it has been tried to find out a comparison between Katz’s and Higuchi’s method under fractal dimension (FD) of the ECG time series in a feature extraction phase. All ECG signals have been acquired from the Massachusetts Institute of Technology (MITBIH) arrhythmia database. The obtained results confirm the superiority of the Katz’s and Higuchi’s method to identify cardiac abnormality as compared to traditional one which is analyses of ECG signals based on morphology features and three ECG temporal features.(i.e. the QRS complex duration, the RR interval and the RR interval averaged over the ten last beats).},
 year = {2016}
}

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TY - JOUR
T1 - Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions
AU - Md. Mashiur Rahman
AU - A. H. M. Zadidul Karim
AU - Abdullah Al Mahmud
AU - Salma Nazia Rahman
Y1 - 2016/09/05
PY - 2016
N1 - https://doi.org/10.11648/j.cbb.20160404.11
DO - 10.11648/j.cbb.20160404.11
T2 - Computational Biology and Bioinformatics
JF - Computational Biology and Bioinformatics
JO - Computational Biology and Bioinformatics
SP - 27
EP - 36
PB - Science Publishing Group
SN - 2330-8281
UR - https://doi.org/10.11648/j.cbb.20160404.11
AB - Analysis process of electrocardiogram (ECG) is a major research interests in bio-medical signal processing. The reasons of this interest is the growth of cardiac health care activities all over the world and the rapid progress in digital computer technology which play an essential role to the detection of diseases at various stages from bio medical signals. The assessment process of diagnostic results for these bio medical signals heavily depends upon quantity, accuracy and speed. Computer based analysis is very useful in clinical therapy. In this Paper a method of analysis (ECG) signals using fractal features have been proposed and practical experiments have done to show that this method provides a good electronic diagnosis pattern for cardiac abnormality because it has been used by some specialist doctors to diagnose various types of diseases with accuracy. By the fact that ECG signals show a fractal patterns, it has been tried to find out a comparison between Katz’s and Higuchi’s method under fractal dimension (FD) of the ECG time series in a feature extraction phase. All ECG signals have been acquired from the Massachusetts Institute of Technology (MITBIH) arrhythmia database. The obtained results confirm the superiority of the Katz’s and Higuchi’s method to identify cardiac abnormality as compared to traditional one which is analyses of ECG signals based on morphology features and three ECG temporal features.(i.e. the QRS complex duration, the RR interval and the RR interval averaged over the ten last beats).
VL - 4
IS - 4
ER -

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Author Information

Md. Mashiur Rahman

Department of EEE, Ahsanullah University of Science and Technolo
A. H. M. Zadidul Karim

Department of EEE, University of Asia Pacific, Dhaka, Bangladesh
Abdullah Al Mahmud

Department of EEE, University of Asia Pacific, Dhaka, Bangladesh
Salma Nazia Rahman

Department of EEE, University of Asia Pacific, Dhaka, Bangladesh

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APA Style

Md. Mashiur Rahman, A. H. M. Zadidul Karim, Abdullah Al Mahmud, Salma Nazia Rahman. (2016). Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions. Computational Biology and Bioinformatics, 4(4), 27-36. https://doi.org/10.11648/j.cbb.20160404.11

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ACS Style

Md. Mashiur Rahman; A. H. M. Zadidul Karim; Abdullah Al Mahmud; Salma Nazia Rahman. Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions. Comput. Biol. Bioinform. 2016, 4(4), 27-36. doi: 10.11648/j.cbb.20160404.11

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AMA Style

Md. Mashiur Rahman, A. H. M. Zadidul Karim, Abdullah Al Mahmud, Salma Nazia Rahman. Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions. Comput Biol Bioinform. 2016;4(4):27-36. doi: 10.11648/j.cbb.20160404.11

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@article{10.11648/j.cbb.20160404.11,
  author = {Md. Mashiur Rahman and A. H. M. Zadidul Karim and Abdullah Al Mahmud and Salma Nazia Rahman},
  title = {Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions},
  journal = {Computational Biology and Bioinformatics},
  volume = {4},
  number = {4},
  pages = {27-36},
  doi = {10.11648/j.cbb.20160404.11},
  url = {https://doi.org/10.11648/j.cbb.20160404.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20160404.11},
  abstract = {Analysis process of electrocardiogram (ECG) is a major research interests in bio-medical signal processing. The reasons of this interest is the growth of cardiac health care activities all over the world and the rapid progress in digital computer technology which play an essential role to the detection of diseases at various stages from bio medical signals. The assessment process of diagnostic results for these bio medical signals heavily depends upon quantity, accuracy and speed. Computer based analysis is very useful in clinical therapy. In this Paper a method of analysis (ECG) signals using fractal features have been proposed and practical experiments have done to show that this method provides a good electronic diagnosis pattern for cardiac abnormality because it has been used by some specialist doctors to diagnose various types of diseases with accuracy. By the fact that ECG signals show a fractal patterns, it has been tried to find out a comparison between Katz’s and Higuchi’s method under fractal dimension (FD) of the ECG time series in a feature extraction phase. All ECG signals have been acquired from the Massachusetts Institute of Technology (MITBIH) arrhythmia database. The obtained results confirm the superiority of the Katz’s and Higuchi’s method to identify cardiac abnormality as compared to traditional one which is analyses of ECG signals based on morphology features and three ECG temporal features.(i.e. the QRS complex duration, the RR interval and the RR interval averaged over the ten last beats).},
 year = {2016}
}

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