A novel mathematical ECG signal analysis approach for features extraction using LabVIEW

Chandan Tamrakar*, Chinmay Chandrakar**, Monisha Sharma***
* Assistant Professor, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.
** Sr. Associate Professor, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.
*** Professor, Shri Shankaracharya College of Engineering and Technology, Bhilai, India.
Periodicity:July - September'2014
DOI : https://doi.org/10.26634/jdp.2.3.3011

Abstract

ECG feature extraction stage is a significant job in diagnosing most of the cardiac diseases after the preprocessing of the ECG signal. Features extracted from ECG are extremely useful in diagnosis. In the previous work to detect the QRS complex wavelet multi-resolution analysis, threshold consideration is used. There has been proposed a structure for detection of the QRS complexes of the ECG signals with the help of Virtual Instruments (VI) of LabVIEW for the standard MIT- BIH arrhythmia database. This structure detects the various features of QRS complex. This paper deals with a resourceful composite method which has been proposed for detrending, denoising and feature extraction of the ECG signals. The proposed structure first employed a wavelet-based detrending and denoising of the ECG signal. Then execute a novel ECG feature extractor. The proposed feature extractor consists of virtual instrument of LabVIEW like Read Biosignal VI, Extraction Portion of signal express VI, Waveform Max-Min VI etc. The Waveform Max-Min VI and Extraction Portion of signal express VI is the alternative of the Peak detector VI without any threshold calculation. Various features like QR level, RS level, QR slope and RS slope etc has been detected by proposed structure. LabVIEW 2013 version has been used here to design the feature extractor.

Keywords

Biomedical Signal, Detrending, Denoising, ECG, Feature extraction, LabVIEW, MIT-BIH arrhythmia database, Wavelet Analysis

How to Cite this Article?

Tamrakar,C., Chandrakar,C., and Sharma,M. (2014). A novel mathematical ECG signal analysis approach for features extraction using LabVIEW. i-manager’s Journal on Digital Signal Processing, 2(3), 14-21. https://doi.org/10.26634/jdp.2.3.3011

References

[1]. Jiapu Pan and Willis J. Tompkins, (1985). “A Real-Time QRS Detection Algorithm” IEEE transactions on biomedical engineering, Vol. BME-32, No. 3, pp. 230-236.
[2]. Qibin Zhao, and Liqing Zhan, (2005). “ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines”, International Conference on Neural Networks and Brain, ICNN&B '05, Vol. 2, pp. 1089- 1092, 2005.
[3]. S. Z. Mahmoodabadi, A. Ahmadian, and M. D. Abolhasani, (2005). “ECG Feature Extraction using Daubechies Wavelets”, Proceedings of the fifth IASTED International conference on Visualization, Imaging and Image Processing, pp. 343-348.
[4]. F. Sufi, S. Mahmoud, I. Khalil, (2008). “A new ECG obfuscation method: A joint feature extraction & corruption approach”, International Conference on Information Technology and Applications in Biomedicine, 2008, ITAB 2008, pp. 334-337.
[5]. Yun-Chi Yeh, Wen-June Wang, Che Wun Chiou, (2009). “Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals”, Elsevier, Measurement ,Vol. 42, pp. 778–789.
[6]. Channappa Bhyri, Kalpana.V, S.T.Hamde, and L.M.Waghmare (2009). “Estimation of ECG features using LabVIEW”, TECHNIA – International Journal of Computing Science and Communication Technologies, Vol. 2, No. 1, pp. 320-324.
[7]. Adam Szczepa´nski, Khalid Saeed, and Alois Ferscha, (2010). “A New Method for ECG Signal Feature Extraction”, Springer-Verlag Berlin Heidelberg 2010 ICCVG 2010, Part II, LNCS 6375, pp. 334–341.
[8]. Faezipour, Student, Adnan Saeed, Suma Chandrika Bulusu,Mehrdad Nourani, Hlaing Minn, and Lakshman Tamil, (2010). “A Patient-Adaptive Profiling Schemefor ECG Beat Classification”, Transactions on Information Technology in Biomedicine, Vol. 14, No. 5, pp. 1153-1165.
[9]. P. Sasikala, Dr. R.S.D. WahidaBanu, (2011). “Extraction of P wave and T wave in Electrocardiogram using Wavelet Transform”, International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 2 (1) , pp.489- 493
[10]. M. K. Islam, A. N. M. M. Haque, G. Tangim, T. Ahammad, and M. R. H. Khondokar, (2012). “Study and Analysis of ECG Signal Using MATLAB & LABVIEW as Effective Tools”, International Journal of Computer and Electrical Engineering, Vol. 4, No. 3, pp. 404-408.
[11]. Swati Banerjee and M. Mitra, (2013). “ECG beat classification based on discrete wavelet transformation and nearest neighbour classifier”, J Med Eng Technol, Vol. 37(4): 264–272.

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