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10004326
Heart Rate Variability Analysis for Early Stage Prediction of Sudden Cardiac Death
Abstract:
In present scenario, cardiovascular problems are growing challenge for researchers and physiologists. As heart disease have no geographic, gender or socioeconomic specific reasons; detecting cardiac irregularities at early stage followed by quick and correct treatment is very important. Electrocardiogram is the finest tool for continuous monitoring of heart activity. Heart rate variability (HRV) is used to measure naturally occurring oscillations between consecutive cardiac cycles. Analysis of this variability is carried out using time domain, frequency domain and non-linear parameters. This paper presents HRV analysis of the online dataset for normal sinus rhythm (taken as healthy subject) and sudden cardiac death (SCD subject) using all three methods computing values for parameters like standard deviation of node to node intervals (SDNN), square root of mean of the sequences of difference between adjacent RR intervals (RMSSD), mean of R to R intervals (mean RR) in time domain, very low-frequency (VLF), low-frequency (LF), high frequency (HF) and ratio of low to high frequency (LF/HF ratio) in frequency domain and Poincare plot for non linear analysis. To differentiate HRV of healthy subject from subject died with SCD, k –nearest neighbor (k-NN) classifier has been used because of its high accuracy. Results show highly reduced values for all stated parameters for SCD subjects as compared to healthy ones. As the dataset used for SCD patients is recording of their ECG signal one hour prior to their death, it is therefore, verified with an accuracy of 95% that proposed algorithm can identify mortality risk of a patient one hour before its death. The identification of a patient’s mortality risk at such an early stage may prevent him/her meeting sudden death if in-time and right treatment is given by the doctor.
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References:

[1] Ebrahimzadeh E, Pooyan M, Bijar A, “A Novel Approach to Predict Sudden Cardiac Death (SCD) Using Nonlinear and Time-Frequency Analyses from HRV Signals,” PLoS ONE,9(2),2014.
[2] http://emedicine.medscape.com/article/151907-overview, April, 29, 2014.
[3] Bianca S Honnekeri, Disha Lokhandwala, Gopi Krishna Panicker, Yash Lokhandwala, “Sudden Cardiac Death in India: A Growing Concern,” Journal of the association of Physicians of India, vol 62, December, 2014.
[4] Ciprian Rezuş, Victor Dan Moga, Anca Ouatu, Mariana Floria, “QT interval variations and mortality risk: Is there any relationship?” Anatol. J. Cardiol. vol. 15, pp. 255-258, 2015.
[5] H. J.J.Wellens et.al, “Risk stratification for sudden cardiac death: current status and challenges for the future,” European Heart Journal Advance Access, May 5, 2014.
[6] Sabine M.J.M. Straus et. al, “Prolonged QTc Interval and Risk of Sudden Cardiac Death in a Population of Older Adults,” Journal of the American College of Cardiology, vol. 47, issue 2, pp. 362-367,17 January 2006.
[7] Nishibe, T. Sato, K., Yoshino, K., Seki, R., Yana, K, Ono, T., “RR-QT interval trend covariability for sudden cardiac death risk stratification,” Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE San Diego, CA pp:4287 - 4290, Aug. 28, 2012-Sept. 1, 2012.
[8] Timo H. Makikallio, Heikki V. Huikuri, Anne Makikallio, Leif B. Sourander, Raul D. Mitrani, Agustin Castellanos, Robert J. Myerburg, “Prediction of Sudden Cardiac Death by Fractal Analysis of Heart Rate Variability in Elderly Subjects,” Journal of the American College of Cardiology, published by Elsevier Science Inc. vol. 37, no. 5, 2001.
[9] https://physionet.org.
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