CFP last date
20 May 2024
Reseach Article

Stress Quantification using Fuzzy Analysis of ECG Parameters

by Sneha Mittal, Nirmal Singh Grewal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 99 - Number 9
Year of Publication: 2014
Authors: Sneha Mittal, Nirmal Singh Grewal
10.5120/17403-7968

Sneha Mittal, Nirmal Singh Grewal . Stress Quantification using Fuzzy Analysis of ECG Parameters. International Journal of Computer Applications. 99, 9 ( August 2014), 24-27. DOI=10.5120/17403-7968

@article{ 10.5120/17403-7968,
author = { Sneha Mittal, Nirmal Singh Grewal },
title = { Stress Quantification using Fuzzy Analysis of ECG Parameters },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 99 },
number = { 9 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 24-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume99/number9/17403-7968/ },
doi = { 10.5120/17403-7968 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:27:46.857863+05:30
%A Sneha Mittal
%A Nirmal Singh Grewal
%T Stress Quantification using Fuzzy Analysis of ECG Parameters
%J International Journal of Computer Applications
%@ 0975-8887
%V 99
%N 9
%P 24-27
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Mental stress quantification using fuzzy analysis of ecg parameters is presented here. ECG signal is decomposed using the BIOR-3. 9 wavelet family upto three levels. The approximates signals are used for computation ecg parameters like energy, entropy, power, standard deviation, mean and covariance. A fuzzy classifier is designed using trimf function as associate membership in fuzzy analysis. The ecg data base is taken from MIT data base web site.

References
  1. Zhilin Zhang_, Student Member, IEEE, Tzyy-Ping Jung, "Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Non-Invasive Fetal ECG via Block Sparse Bayesian Learning", ACCEPTED BY IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2012
  2. Fahimeh Ansari-Ram, Saied Hosseini-Khayat, "ECG Signal Compression Using Compressed Sensing with Nonuniform Binary Matrices", 978-1-4673-1479-4/12/$31. 00 ©2012 IEEE
  3. M. Sharafat Hossain , "ECG Signal Compression using Energy Compaction Based Thresholding of the Wavelet Coefficients", DUET Journal Vol. 1, Issue 2, June 2011.
  4. Bong Siao Zheng, M Murugappan and Sazali Yaacob, "FCM Clustering of Emotional Stress using ECG Features", International conference on Communication and Signal Processing, April 3-5, 2013, India.
  5. Mohammad Reza Homaeinezhad1, 2 , Ehsan Tavakkoli1,2 , Ali Ghaffari, "Discrete Wavelet-based Fuzzy Network Architecture for ECG Rhythm-Type Recognition: Feature Extraction and Clustering- Oriented Tuning of Fuzzy Inference System" , International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 4, No. 3, September, 2011.
  6. Mohit Kumar, Matthias Weippert, Reinhard Vilbrandt, Steffi Kreuzfeld, and Regina Stoll, "Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment" , IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 15, NO. 5, OCTOBER 2007.
  7. G. Ranganathan, V. Bindhu, Dr. R. Rangarajan, s "ECG Signal Processing using Dyadic wavelet for Mental Stress Assessment", 978-1-4244-4713-8/10/$25. 00 ©2010 IEEE.
  8. C. Saritha, V. Sukanya, Y. Narasimha Murthy, "ECG Signal Analysis Using Wavelet Transforms", Bulg. J. Phys. 35 (2008) 68–77. Prof, Shamla Mantri, Dr. Pankaj Agrawal, Prof. Dipti Patil, Dr. V. M. Wadhai
  9. "Depression Analysis using ECG Signal", ISSN 2277-3061, N o v 1 0 , 2 0 1 3.
Index Terms

Computer Science
Information Sciences

Keywords

ECG BIOR-3. 9 wavelet Entropy Energy Power Standard Deviation Covariance Fuzzy Logic Mental Stress