Review on HRV based Prediction and Detection of Heart Disease

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Santosh K. Maher, Sumegh Tharewal, Abdul Hannan, Suvarnsing G. Bhable, K. V. Kale
10.5120/ijca2018917083

Santosh K Maher, Sumegh Tharewal, Abdul Hannan, Suvarnsing G Bhable and K V Kale. Review on HRV based Prediction and Detection of Heart Disease. International Journal of Computer Applications 179(46):7-12, June 2018. BibTeX

@article{10.5120/ijca2018917083,
	author = {Santosh K. Maher and Sumegh Tharewal and Abdul Hannan and Suvarnsing G. Bhable and K. V. Kale},
	title = {Review on HRV based Prediction and Detection of Heart Disease},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2018},
	volume = {179},
	number = {46},
	month = {Jun},
	year = {2018},
	issn = {0975-8887},
	pages = {7-12},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume179/number46/29482-2018917083},
	doi = {10.5120/ijca2018917083},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Heart disease is the major cause of death today. Cholesterol, blood pressure and CVD cardiovascular disease. Pulse rate are the main reason for the heart disease. Measurement of heart rate variability (HRV) its shows information on the functional state of the autonomic nervous system (sympathetic and parasympathetic). HR analysis based on measure of heart rate signal per unit of time of the number of heartbeats (identified as RR interval, as it is the time interval between successive R points of the QRS complex of the electrocardiogram and measured by the variation in the beat-to-beat interval).Heart rate variability (HRV) is a relatively new method for assessing the effects of stress on your body. It is measured as the time gap between your heart beats that varies as you breathe in and out. The heart is a key factor of the human body, acting as a pump that transfers oxygenated and deoxygenated blood around the body. Like all other organs, it is susceptible to diseases and age. Heart rate variability is a reliable indication of the many physiological factors modulating the normal rhythm of the heart. In fact, they provide a powerful means of observing the relationship between the sympathetic and parasympathetic nervous systems. It is also significantly associated with average heart rate (HR), therefore, HRV actually provides information on two quantities, that is, on HR and its variability.

References

  1. Mohammad Nasim (2013) A low cost optical sensor based heart rate monitoring system Informatics, Electronics & Vision (ICIEV), 2013 International Conference on 10.1109/ICIEV.2013.6572660
  2. Saul JP (1990) Beat-to-beat variations of heart rate reflect modulation of cardiac autonomic outflow. News Physiol Sci 5:32–37
  3. Schwartz PJ, Priori SG (1990) Sympathetic nervous system and cardiac arrythmias. In: Zipes DP, Jalife J (eds) Cardiac electrophysiology. From cell to bedside. W.B. Saunders, Philadelphia, pp 330–343
  4. Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger MA, Cohen RJ (1981) Power spectrum analysis of heart rate fluctuation. Science 213:220–222
  5. Tulen JH, Boomsma F, Man in t’veld AJ (1999) Cardiovascular control and plasma catecholamines during restand mental stress: effects of posture. Clin Sc 96:567–576
  6. Viktor A, Jurij-MAtija K, Roman T et al (2003) Breathing rates and heart rate spectrograms regarding body position in normal subjects. Compute Biol Med 33:259–266
  7. Cysarz D, Bettermann H, van Leeuwen P (2000) Entropies of short binary sequences in heart period dynamics. Am J Physiol Heart Circ Physiol 278:H2163–H2172
  8. Gamero LG, Vila J, Palacios F (2002) Wavelet transform analysis of heart rate variability during myocardial ischaemia. Med Biol Eng Comput 40:72–78
  9. G.Ranganathan 2v.Bindhu 3dr.R.Rangarajan, “Signal Processing Of Heart Rate Variability Using Wavelet Transform For Mental Stress Measurement “Journal of Theoretical and Applied Information Technology , 2005 – 2010
  10. Nazneen Akhter, Jinan Fadhil Mahdi, Ganesh R.Manza “Microcontroller based data acquisition system for Heart Rate Variability (HRV) measurement” Int. Journal of Applied Sciences and Engineering Research, Vol. 1, Issue 4, 2012.
  11. George E. Billman 1*, Heikki V. Huikuri 2, Jerzy Sacha 3 and Karin Trimmel 4 “An introduction to heart rate variability: methodological considerations and clinical applications” Department of Physiology and Cell Biology, The Ohio State University, Columbus, OH, USA published: 25 February 2015.
  12. Billman, G. E. (2011). Heart rate variability – a historical perspective. Front. Physiol.2:86. doi: 10.3389/fphys.2011.00086.
  13. Billman, G. E. (2013a). The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance. Front. Physiol. 4:26. doi: 10.3389/fphys.2013.00026.
  14. Heathers, J. A. (2014). Everything Hertz: methodological issues in short-term frequency-domain HRV. Front. Physiol. 5:177. doi: 10.3389/fphys.201400177
  15. Peltola, M. A. (2012). Role of editing of R-R intervals in the analysis of heart rate variability. Front. Physiol. 3:148. doi: 10.3389/fphys.2012.00148
  16. Heart rate variability Standards of measurement, physiological interpretation, and clinical use Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology European Heart Journal (1996) 17, 354–381
  17. Nasim Karim , Jahan Ara Hasan and Syed Sanowar Ali “Heart Rate Variability – A Review” Journal of Basic and Applied Sciences Vol. 7, No. 1, 71-77, 2011. ISSN: 1814-8085
  18. Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society. Euro pace (2015) 17, 1341–1353 doi:10.1093/euro pace/euv015, online publish-ahead-of-print 15 July 2015
  19. F. Buccelletti, E. Gilardi, E. Scaini, L. Galiuto, R. Persiani, A. Biondi, F. Basile, N. Gentiloni Silveri,Heart rate variability and myocardial infarction: systematic literature review and met analysis, European Review for Medical and Pharmacological Sciences, 2009; 13: 299-307
  20. A. Günther, O.W. Witte and D. Hoyer “Autonomic Dysfunction and Risk Stratification Assessed from Heart Rate Pattern” The Open Neurology Journal, 2010, 4, 39-49
  21. Cysarz D, Bettermann H, van Leeuwen P (2000) Entropies of short binary sequences in heart period dynamics. Am J Physiol Heart Circ Physiol 278:H2163–H2172
  22. Verlinde D, Beckers F, Ramaekers D, Aubert AE (2001) Wavelet decomposition analysis of heart rate variability in aerobic athletes. Auton Neurosci 90(1–2):138– 141
  23. Elsenbrunch S, Harnish MJ, Orr WC (1999) Heart rate variability during waking and sleep in healthy males and females. Sleep 22(8):1067–1071
  24. Bracic M, Stefanovska A (1998) Wavelet-based analysis of human blood-flow dynamics. Bull Math Biol 60:919–935
  25. Andr´e E. Aubert, Bert Seps and Frank Beckers, “Heart Rate Variability in Athletes”, Laboratory of Experimental Cardiology, School of Medicine, K.U. Leuven, Leuven, Belgium, Sports Med 2003; 33 (12): 889-919.
  26. Barutcu I, Esen AM, Kaya D, Turkmen M, Karakaya O, Melek M, Esen OB, Basaran Y (2005) Cigarette smoking and heart rate variability: dynamic influence of parasympathetic and sympathetic maneuvers. Ann Noninvas Electrocardiol 10(3):324–329
  27. Pfeifer MA, Cook D, Brodsky J, Tice D, Reenan A, Swedine et al (1982) Quantitative evaluation of cardiac parasympathetic activity in normal and diabetic man. Diabetes 3:339–45
  28. Ho KK, Moody GB, Peng CK, Meitus JE, Larson MG, Levy D, Goldberger AL (1997) Predicting survival in heart failure case and control subjects by use of full automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation 96:842– 848
  29. Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger MA, Cohen RJ (1981) Power spectrum analysis of heart rate fluctuation. Science 213:220–222
  30. Nazneen Akhter, Sumegh Tharewal, Vijay Kale, Ashish Bhalerao and K.V. Kale,” Heart-Based Biometrics and Possible Use of Heart Rate Variability in Biometric Recognition Systems”
  31. Hoang ChuDuc* , Kien NguyenPhan, Dung NguyenVietA ,”Review of Heart Rate Variability and its Applications” ICBET 2013: May 19-20, 2013, Copenhagen, Denmark,
  32. Warsuzarina Mat Jubadi, Siti Faridatul Aisyah Mohd Sahak, “Heartbeat Monitoring Alert via SMS” 2009 IEEE Symposium on Industrial Electronics and Applications (ISIEA 2009), October 4-6, 2009, Kuala Lumpur, Malaysia.
  33. Purnima, Puneet Singh, “Zigbee and GSM Based Patient Health Monitoring System”, Interntional Conference on Electronics and Communication System (IECS -2014)
  34. D.J.R.Kiran Kumar, Nalini Kotnana, “Design and Implementation of Portable health monitoring system using PSOC mixed signal Array chip”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Volume-1, Issue-3, august 2012.
  35. Nazneen Akhter, Sumegh Tharewal, Vijay Kale, Ashish Bhalerao and K.V. Kale “Heart-Based Biometrics and Possible Useof Heart Rate Variability in Biometric Recognition Systems” © Springer India 2016 R. Chaki et al. (eds.), Advanced Computing and Systems for Security,Advances in Intelligent Systems and Computing 395, DOI 10.1007/978-81-322-2650-5_2
  36. Amit S. Wale “ Signal Analysis And Prediction Of Heart Attack With The Help Of Optimized Neural Network Using Genetic Algorithm” international conference on emerging trade in engineering technology, science and management IIMT college of Engineering, greater noida, India 12 April 2017 ISBN 978-93-8617-38-2
  37. Shelley, K. H., R. G. Stout, et al. (1999). "The Use of Joint Time Frequency Analysis of The Pulse Oximeter Waveform to Measure The Respiratory Rate of Ventilated Patients." Anesthesiology 91(3A): A583.

Keywords

ECG (Electrocardiogram), HR, RR interval, Heart Rate Variability, KNN,GA.