Open Access Open Access  Restricted Access Subscription or Fee Access

Pattern Extraction and Classification of ECG Signals using Neuro-Fuzzy Techniques

rajesh dhurtatej wagh, Shaila P. Kharde

Abstract


Electrocardiography is a technique which provides the interpretation of the electrical activity of the heart over a period of time. The condition of cardiac health is given by Electrocardiogram (ECG) and heart rate. This paper represents the techniques, which extracts features from the ECG signals and classify them. ECG signal were taken from preselected data segment of the MIT‐BIH Arrhythmia Database. Pattern extraction provides the PQRST-wave values, heart rate and the intervals of the ECG signals, which are useful to calculate the cardiac health of the patient. In the result section, it was shown that two different techniques were applied for comparative study of Artificial Neural Network (ANN) as well as Fuzzy Interface System (FIS) on the same ECG signal. ANN and FIS classifier was used for the classification and an accuracy of 95.69% and 97.87% was achieved, respectively.

Keywords: Electrocardiography, cardiac, ECG, PQRST-wave, ANN


Full Text:

PDF

References


Anuradha B, Veera Reddy VC. ANN for Classification of Cardiac Arrhythmias. ARPN Journal of Engineering and Applied Sciences. 2008; 3 (3): 1–6p.

Ghongade R, Ghatol AA. A Robust and Reliable ECG Pattern Classification using QRS Morphological Features and ANN. Proceedings of TENCON 2008 - IEEE Region 10 Conference; 2008 Nov 19–21; Hyderabad, India.

Patil D, Wadhai VM, Sharma A, et al. A Comparative Study of Traditional and

Mobile based ECG System Algorithms. IJCA. 2012; 41(3): 1–5p.

Saxena SC, Sharma A, Chaudhary SC. Data compression and feature extraction of ECG signals. Int J Sys Sci. 1997; 28 (5): 483–98p.

Rehman MZ, Nawi N. M. Improving the Accuracy of Gradient Descent Back Propagation Algorithm (GDAM) on Classification Problems. IJNCAA. 2011; 1(4): 838–47p.

Tawfiq LNM. Improving Gradient Descent Method for Training Feed Forward Neural Networks. IJMCSE. 2013; 2(1): 12–24p.

Bataineh KM, Naji M, Saqer M. A Comparison Study between Various Fuzzy Clustering Algorithms. JJMIE. 2011; 5 (4): 335–43p.

Hammouda K, Karray F. A Comparative Study of Data Clustering Techniques. University of Waterloo, Ontario, Canada. 1997; 13, (2-3):149–59p.


Refbacks

  • There are currently no refbacks.


This site has been shifted to https://stmcomputers.stmjournals.com/