Comparison of Normal and Abnormal Conditions in ECG Using RR Variablity and Spectral Density

  • Authors

    • T R. Thamizhvani
    • Josline Elsa Joseph
    • Bincy Babu
    • U Rithikka
    • D Rohini
    • A Josephin Arockia Dhivya
    2018-05-03
    https://doi.org/10.14419/ijet.v7i2.25.12362
  • Atrial Fibrillation, Congestive Heart Failure, Heart rate Variability, Power Spectral Density.

  • Abstract

    Abnormality of the heart is monitored by Electrocardiograph (ECG). The ECG waveform is formed of PQRS pattern. Differentiation of the abnormalities based on the ECG signal is simple algorithm for diagnosis. ECG data of normal, atrial fibrillation and congestive heart failure is obtained from a authorized database. R peak from the QRS complex is detected using Pan-Tompkins algorithm for analysis. Mean RR and heart rate variability parameters are extracted from the QRS complex detected. With these results, the difference in the three ECG signals can be determined. For further detailed comparison, frequency component variation is analysed using power spectral density. Based on density spectrum, the differentiation of normal and abnormal ECG signals can be determined.

     

  • References

    1. [1] F. Sufi, Q. Fang, I. Cosic, “ECG R-R Peak Detection on Mobile Phonesâ€, Proceedings of 29th Annual International Conference of the IEEE EMBS, (2007), pp:3797-3700. http://dx.doi.org/10.1109/IEMBS.2007.4353134

      [2] Shreya Das, Dr. Monisha Chakraborty, “Comparison of Power Spectral Density (PSD) of Normal and Abnormal ECGsâ€, Proceedings of the 2nd National Conference on Computing, Communication and Sensor Network, Vol. 2, (2011), pp:10-14.

      [3] P. Tirumala Rao1, S. Koteswarao Rao2, G. Manikanta1 and S. Ravi Kumar, “Distinguishing Normal and Abnormal ECG Signalâ€, Indian Journal of Science and Technology, Vol. 9, No.10, (2016), pp:1-5, 10.17485/ijst/2016/v9i10/85449.

      [4] Sachin singh ,Netaji Gandhi.N, “Pattern analysis of different ECG signal using Pan-Tompkins algorithmâ€, International journal on computer science and engineering, Vol. 2, No. 7, (2010), pp:2502-2505.

      [5] Kritika Bawa , Pooja Sabherwal, “R-peak detection by modified Pan-Tompkins algorithmâ€, International journal of advancement in research & technology, Vol. 3, No. 5, (2014), pp:30-33, 10.1109/ICACCCT.2014.7019490.

      [6] John J.V.Mcmurray, Stamatis Adamopoulos, Stefan D.Anker, Angelo Auricchio, Michael Bohm, Kenneth Dickstein,Volkmar Falk, Gerasimos Filippatos, Candida Fonseca, “The task force for the diagnosis and treatment of and chronic heart failureâ€, European Heart Journal, Vol. 33, No. 14, (2012), pp:1787-1847, 10.1093/eurheartj/ehw128.

      [7] Thomas J. Wang, Martin G. Larson, Daniel Levy, Ramachandran S. Vasan, Eric P. Leip, Philip A. Wolf,Ralph B. D’Agostino, Joanne M. Murabito, William B. Kannel, Emelia J. Benjamin, “Temporal Relations of Atrial Fibrillation and Congestive Heart Failure and Their Joint Influence on Mortalityâ€, American heart association, Vol. 107, No. 23, (2003), pp:2920-2925, 10.1161/01.CIR.0000072767.89944.6E.

      [8] E.S. Thanuja, L. Suganthi, “Effect of integrated yoga on cardiovascular parameters during pregnancyâ€, Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS), (2018), pp:804-809, 10.1109/ICCONS.2017.8250574.

      [9] KenUmetani, Donald HSinger, BRollin, McCraty Mike, Atkinson C, “Twenty-Four Hour Time Domain Heart Rate Variability and Heart Rate: Relations to Age and Gender Over Nine Decadesâ€, Journal of the American College of Cardiology, Vol. 31, No. 3, (1998), pp:593 -601.

      [10] Diana P. Tobón, Srinivasan Jayaraman, Tiago H. Falk, “Spectro-Temporal Electrocardiogram Analysis for Noise-Robust Heart Rate and Heart Rate Variability Measurementâ€, IEEE Journal of Translational Engineering in Health and Medicine, Vol. 5, (2017), pp:1-11, 10.1109/JTEHM.2017.2767603.

      [11] Friedman HS, “Heart rate variability in atrial fibrillation related to left atrial sizeâ€, American Journal of Cardiology, Vol. 93, No. 6, (2004), pp:705-709, 10.1016/j.amjcard.2003.11.052.

      [12] YanWang, ShoushuiWei, ShuaiZhang, YataoZhang, LinaZhao, ChengyuLiu, AlanMurray, “Comparison of time-domain, frequency-domain and non-linear analysis for distinguishing congestive heart failure patients from normal sinus rhythm subjectsâ€, Biomedical Signal Processing and control, Vol. 42, (2018), pp:30-36, https://doi.org/10.1016/j.bspc.2018.01.001.

      [13] B.G. Celler, P. deChazal, N.H. Lovell, “Power spectral density esti mates of populations of normal and abnormal 12 lead and Frank lead ECGsâ€, Proceedings of the 18th Annual International Confer ence of the IEEE in Engineering in Medicine and Biology Society, Vol. 4, (1996), pp:1407-1409, 10.1109/IEMBS.1996.647478.

  • Downloads

  • How to Cite

    R. Thamizhvani, T., Elsa Joseph, J., Babu, B., Rithikka, U., Rohini, D., & Josephin Arockia Dhivya, A. (2018). Comparison of Normal and Abnormal Conditions in ECG Using RR Variablity and Spectral Density. International Journal of Engineering & Technology, 7(2.25), 28-31. https://doi.org/10.14419/ijet.v7i2.25.12362

    Received date: 2018-05-03

    Accepted date: 2018-05-03

    Published date: 2018-05-03