Comparative Study of Tachyarrhythmia ECG and Normal ECG

• Autorzy: Nainwal Ashish , Kumar Yatindra , Jha Bhola

Identyfikatory

DOI

10.15439/2017R62

• Konferencja: The Second International Conference on Research in Intelligent and Computing in Engineering
• Języki publikacji: EN

Abstrakty

EN

ECG is the electrical activity of heart functioning which is used to diagnosis the heart related diseases. ECG helps to decide whether human is healthy or not. Today most of death happened in the world due to the heart diseases. It is very important to know the accurate information about the heart activity to diagnosis the actual diseases. The data base is taken from the MIT-BIH physionet bank. In this paper the features of tachyarrhythmia ECG and normal ECG are extracted using wavelet transform. After that t-test is used for statically analysis. This study shows that the most of morphological features of tachyarrhythmia ECG has strongly significant changes.

Słowa kluczowe

EN

ECG; tachyarrhythmia; wavelet transform; hypothesis test; MITBIH.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2017
• Tom: Vol. 10
• Strony: 63--65
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Nainwal Ashish

• Department of Electronics and Communication Engineering Faculty of Engineering and Technology, Gurukul Kangri Vishwavidhalaya, Haridwar Uttarakhand-246104

autor

Kumar Yatindra

• Department of Electrical Engineering G. B. Pant Engineering College Pauri-Garhwal Uttarakhand-246194

autor

Jha Bhola

• Department of Electrical Engineering G. B. Pant Engineering College Pauri-Garhwal Uttarakhand-246194

Bibliografia

• [1] M. Rangayyan, Biomedical signal analysis. John Wiley & Sons, 2015, vol. 33.
• [2] L. Cromwell and F. Wecbell, “Biomedical instruments and measurements.”
• [3] P. P. Brendan, “Advanced ecg : Boards and beyond,” Saunders Elsevier, 2006.
• [4] Y. N. Singh, “Human recognition using fisher s discriminant analysis of heartbeat interval features and ecg morphology,” Neurocomputing, vol. 167, pp. 322-335, 2015.
• [5] “Physiobank archive index, mit-bih normal sinus rhythm database.” http://www.physionet.org/physiobank/database.
• [6] “Physiobank archive index, creighton university ventricular tachyarrhythmia database.” [Online]. Available: http://www.physionet.org/physiobank/database.
• [7] A. Nainwal, Y. Kumar, and B. Jha, “Morphological changes in congestive heart failure ecg,” in Advances in Computing, Communication, & Automation (ICACCA)(Fall), International Conference on. IEEE, 2016, pp. 1-4.
• [8] M. Kor¨urek and B. Do˘gan, “Ecg beat classification using particle swarm optimization and radial basis function neural network,” Expert systems with Applications, vol. 37, no. 12, pp. 7563-7569, 2010.
• [9] D. Zhang, “Wavelet approach for ecg baseline wander correction and noise reduction,” in 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005, pp. 1212-1215.
• [10] S. Kadambe, R. Murray, and G. F. Boudreaux-Bartels, “Wavelet transform-based qrs complex detector,” IEEE Transactions on biomedical Engineering, vol. 46, no. 7, pp. 838-848, 1999.
• [11] G. K. Prasad and J. Sahambi, “Classification of ecg arrhythmias using multi-resolution analysis and neural networks,” in TENCON 2003. Conference on Convergent Technologies for the Asia-Pacific Region, vol. 1. IEEE, 2003, pp. 227-231.
• [12] O. S. Faragallah, “Efficient video watermarking based on singular value decomposition in the discrete wavelet transform domain,” AEU International Journal of Electronics and Communications, vol. 67, no. 3, pp. 189-196, 2013.