The week's pick
Reseach Article
Fuzzy Classifier for Mental Stress Estimation using ECG Statistical Parameters
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 95 - Number 15 |
| Year of Publication: 2014 |
| Authors: Sneha Mittal, Nirmal Singh Grewal |
10.5120/16670-6665
|
Sneha Mittal, Nirmal Singh Grewal . Fuzzy Classifier for Mental Stress Estimation using ECG Statistical Parameters. International Journal of Computer Applications. 95, 15 ( June 2014), 18-21. DOI=10.5120/16670-6665
Abstract
Mental Stress estimation is an important feature to be derived in health related diagnostic activity. It has been observed that the stress has a major effect on heart functioning. And therefore, ecg should be the major source of stress variation and can be analyzed in various ways in order to extract the effect of mental stress. In the presented work, the ecg is analyzed using the statistical parameters set (energy, entropy, power, standard deviation and covariance). The parameters are not directly computed form the ecg itself. The ecg is first decomposed to level-2 using BIOR-3. 9 wavelet transform to reduce the dimensionality of the ecg sample size. The level-1 and level-2 parameters are used to derive the mental stress levels as normal (N), hyper-1 (H-1), hyper-2 (H-2), depression-1 (D-1) and depression-2 (D-2). On parameter analysis, it has been observed that the energy and entropy are the two parameters that show an effective variation in values when normal to depression or normal to hyper case is observed. Therefore, the energy and entropy values are used for rule making and learning of the system in order to derive the mental stress levels
References
- 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
- 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
- M. Sharafat Hossain , "ECG Signal Compression using Energy Compaction Based Thresholding of the Wavelet Coefficients", DUET Journal Vol. 1, Issue 2, June 2011.
- 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.
- 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.
- 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.
- 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.
- 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
- "Depression Analysis using ECG Signal", ISSN 2277-3061, N o v 1 0 , 2 0 1 3.
Index Terms
Keywords