News Updates Thursday 19th Oct 2017 :
  • Welcome to International Press Corporation, world's leading publishers, We have served more than 10000+ authors
  • Articles are invited in engineering, science, technology, management, industrial engg, biotechnology etc.
  • Paper submission last date of Sept/Oct 2017 extended to 20 Oct 2017, Submit online or at editor.ijcet@inpressco.com
  • Our journals are indexed in University of Regensburg Germany, Google Scholar, Cross Reference data bases
  • Applications for reviewers are invited and can be sent directly to concerned editor's mail

Analyzing Electrocardiography (ECG) Signal using Fractal Method


Author : Pardis Nayyeri

Pages : 498-505
Download PDF
Abstract

Background: Today, the process of ECG signal analysis is one of the most important issues in processing biosignals, which has gained attention of lots of scholars. Increasing enhancement of heart care activities across the world and vast advancements of technology that play key role in diagnosis of diseases by biological signals could be the main cause gained attention of majority of scholars. These signals can have different types. As ECG signals have same morphological features, they are more important than other biological signals. Through processing these morphological changes, lots of heart diseases could be diagnosed visually. Therefore, this study has been conducted with the aim of analyzing ECG signal using fractal method.
Materials and Methods: In this study, to analyze ECG signals, fractal features are used. Using fractal features, ECG signals are analyzed and different scale behavior are observed for people with healthy and unhealthy heart. Moreover, fractal features are proved for ECG series. As the method provides a proper diagnosis method for heart diseases, the method is used by specialists to diagnose types of different heart diseases with the accuracy coefficient of 89.33%.
Results: The results obtained from the study showed that the proposed method has higher accuracy and speed to diagnose heart diseases compared to old methods including analysis of ECG signals based on morphological features. With the increase in normal rhythms, the correlation dimension is increased. This shows more complex nature of normal rhythms. Hence, it could be used as a criterion to separate normal and abnormal ECG signals.
Conclusion: According to obtained results for arrhythmia, in addition to significant correlations, Hurst index, correlation dimension and fractal dimension (FD) could be used as diagnosis instruments for arrhythmia.

Keywords: Electrocardiography (ECG) signal, fractal method, heart rate time series

Article published in International Journal of Current Engineering and Technology, Vol.7, No.2 (April-2017)

 

Call for Papers
  1. IJCET- Sept/Oct-2017 Issue

    Submission Last Date extended to
    20 Oct 2017
  2. IJTT-Sept-2017
  3. IJAIE-Sept-2017
  4. IJCSB-Sept-2017
  • Inpressco Google Scholar
  • Inpressco Science Central
  • Inpressco Global impact factor
  • Inpressco aap

International Press corporation is licensed under a Creative Commons Attribution-Non Commercial NoDerivs 3.0 Unported License
©2010-2017 INPRESSCO® All Rights Reserved