The week's pick
Reseach Article
Arrhythmia Classification by Heart Rate Variability Analysis using Symlets based on Time-Frequency Features
| International Conference on Computing, Communication and Sensor Network |
| Foundation of Computer Science USA |
| CCSN2014 - Number 1 |
| June 2015 |
| Authors: Narottam Das, Alok Chakrabarty |
| b18bc8ab-b680-4111-8083-5a9c436459c0 |
Narottam Das, Alok Chakrabarty . Arrhythmia Classification by Heart Rate Variability Analysis using Symlets based on Time-Frequency Features. International Conference on Computing, Communication and Sensor Network. CCSN2014, 1 (June 2015), 16-20.
Abstract
The activities of autonomic nervous system can be accessed using information on heart rate modulation mechanism. HRV analysis is a well-known non-invasive tool that gives information on heart rate modulation mechanism. This paper presents a work on HRV analysis to distinguish normal sinus rhythm from atrial fibrillation, supra-ventricular arrhythmia and premature ventricular contraction. Basically a technique for detection of the heart disease Arrhythmia grounding on HRV signal data analysis is presented in this paper. The R-Peak detection is done using wavelet Symlet7 at second level decomposition. The time-frequency parameters such as SD Ratio, LF/HF Ratio and pNN50 are used for HRV analysis. The ratio LF/HF of HRV spectra represents a measure of sympatho-vagal balance. As this parameter shows better results for only short term recordings hence other parameters such as SD Ratio and pNN50 are considered for HRV analysis for both long-term and short-term recordings.
References
- Malik, M. , Bigger, J. T. , Camm, A. J. , Kleiger, R. E. , Malliani, A. , Moss, A. J. , and Schwartz, P. J. , 1996. Heart rate variability standards of measurement, physiological interpretation, and clinical use. European Heart Journal. 17(3)(March 1996), 354-381.
- Pan, J. , and Tompkins, W. J. , 1985. A Real-Time QRS Detection Algorithm. IEEE Transactions on Biomedical Engineering. BME-32(3)(March 1985), 230-236.
- Sifuzzaman, M. , Islam, M. R. , and Ali, M. Z. , 2009. Aplication of Wavelet Transform and its Advantage Compared to Fourier Transform. Journal of Physical Sciences. 13, 121-134.
- D'Addio, G. , Pinna, G. , Maestri, R. , Corbi, G. , Ferrara, N. , and Rengo, F. 2004. Quantitative Poincare plots analysis contains relevant information related to heart rate variability dynamics of normal and pathological subjects. Computers in Cardiology 2004, September 19-22. IEEE. 457-460.
- Milicevi?, G. , 2005. Low to High Frequency Ratio of Heart Rate Variability Spectra Fails to Describe Sympatho-Vagal Balance in Cardiac Patients. Coll Antropol. . 29(1)(June 2005), 295-300.
- Moody, G. B. , Mark, R. G. , and Glodberger, A. L. 2000. PhysioNet: a research resource for studies of complex physiologic and biomedical signals. Computers in Cardiology 2000, September 24-27. IEEE. 179-182.
- Chavan, M. S. , Mastorakis, N. , Chavan, M. N. , and Gaikwad, M. S. 2011. Implementation of SYMLET wavelets to removal of Gaussian additive noise from speech signal. In Proceedings of the 10th WSEAS international conference on electronics, hardware, wireless and optical communications, and 10th WSEAS international conference on signal processing, robotics and automation, and 3rd WSEAS international conference on nanotechnology, and 2nd WSEAS international conference on Plasma-fusion-nuclear physics (NEHIPISIC'11). Bojkovic, Z. ; Kacprzyk, J. ; Mastorakis, N. ; Mladenov, V. ; Revetria, R. (Eds. ). World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, USA. 37-41.
- Piskorski, J. , and Guzik, P. , 2005. Filtering poincare plots. Computational methods in Science and Technology. 11(1), 39-48.
- Mainardi, L. T. , 2009. On the quantification of heart rate variability spectral parameters using time-frequency and time-varying methods. NCBI Philosophical transactions: Series A - Mathematical, physical, and engineering sciences. 367(1887)(January 2009), 255-275.
Index Terms
Keywords