The new methods of statistical analysis of heart rhythm were developed based on its generalized mathematical model in a form of random rhythm function, that allows to increase the informativeness and detailed analysis of heart rhythm in cardiovascular information systems. Three information criteria (BIC, AIC and AICc) were used to determine the cumulative distribution functions that best describe the sample and to assess the unknown parameters of distributions. The usage of the rhythm function to analyse heart rhythm allows to consider much better its time structure that is the basis to improve the accuracy of diagnosis of cardiac rhythm.
If the inline PDF is not rendering correctly, you can download the PDF file here.
1. Akaike H. (1974) A new look at the statistical model identification IEEE Transactions on Automatic Control 19(4) 716–723.
2. Berkaya S.K. Uysal A.K. Gunal E.S Ergin S. Gunal S. Gulmezoglu M.B. (2018) A survey on ECG analysis Biomedical Signal Processing and Control 43 216-235.
3. Bozhokin S.V. Suslova I.B. (2014) Wavelet Analysis of Non-stationary Signals in Medical Cyber-Physical Systems (MCPS). B S. Balandin S. Andreev & Y. Koucheryavy (Eds) Internet of Things Smart Spaces and Next Generation Networks and Systems Springer International Publishing.
4. Brandão G.S. Sampaio A.A.C. Brandão G.S. Urbano J.J. Fonsêca N.T. Apostólico N. Oliveira E.F. Perez E.A. Almeida R.G. Dias I.S. Santos I.R. Nacif S.R. Oliveira L.V.F. (2014) Analysis of heart rate variability in the measurement of the activity of the autonomic nervous system: technical note Manual Therapy Posturology & Rehabilitation Journal 12 243-251.
5. Ciucurel C. Georgescu L. Iconaru E.I. (2018) ECG response to submaximal exercise from the perspective of Golden Ratio harmonic rhythm Biomedical Signal Processing and Control 40 156-162.
6. Coles S. (2001) Extreme values regular variation and point processes Springer London.
7. Evaristo R.M. Batista A.M. Viana R.L. Iarosz K.C. Szezech J.D. Jr. Godoy M.F. (2018). Mathematical model with autoregressive process for electrocardiogram signals Communications in Nonlinear Science and Numerical Simulation 57 415-421.
8. Foster F.G. Stuart A. (1954) Distribution-Free Tests in Time-Series Based on the Breaking of Records Journal of the Royal Statistical Society. Series B (Methodological) 16(1) 1-22.
10. Fumagalli F. Silver A.E. Tan Q. Zaidi N. Ristagno G. (2018) Cardiac rhythm analysis during ongoing cardiopulmonary resuscitation using the Analysis During Compressions with Fast Reconfirmation technology Heart Rhythm 15(2) 248-255.
11. Gadhoumi K. Do D. Badilini F. Pelter M.M. Hu X. (2018) Wavelet leader multifractal analysis of heart rate variability in atrial fibrillation Journal of Electrocardiology 51(6) S83-S87.
12. Galeotti L. Scully C.G. (2018) A method to extract realistic artifacts from electrocardiogram recordings for robust algorithm testing Journal of Electrocardiology 51(6) S56-S60.
13. Hammad M. Maher A. Wang K. Jiang F. Amrani M. (2018) Detection of abnormal heart conditions based on characteristics of ECG signals Measurement 125 634-644.
14. Isler Y. Narin A. Ozer M. Perc M. (2019) Multi-stage classification of congestive heart failure based on short-term heart rate variability Chaos Solitons & Fractals 118 145-151.
15. Koichubekov B.K. Sorokina M.A. Laryushina Y.M. Turgunova L.G. Korshukov I.V. (2018) Nonlinear analyses of heart rate variability in hypertension Annales de Cardiologie et d'Angéiologie 67(3) 174-179.
16. Kotel’nikov S.A. Nozdrachev A.D. Odinak M.M. Shustov E.B. Kovalenko I.Yu. Davydenko V.Yu. (2002) Cardiac Rhythm Variability: Approaches to Mechanisms Human Physiology 28(1) 114-127.
17. Li J. Chen Ch. Yao Q. Zhang P. Wang J. Hu J. Feng F. (2018) The effect of circadian rhythm on the correlation and multifractality of heart rate signals during exercise Physica A: Statistical Mechanics and its Applications 509 1207-1213.
18. Liddle A.R. (2007) Information criteria for astrophysical model selection Monthly Notices of the Royal Astronomical Society: Letters 377(1) 74-78.
19. Lupenko S. N. Lutsyk Y. Lapusta. (2015) Cyclic linear random process as a mathematical model of cyclic signals Acta Mechanica et Automatica 9(4) 219-224.
20. Lytvynenko I. Maruschak P. Lupenko S. Panin S. (2015) Segmentation and Statistical Processing of Geometric and Spatial Data on Self-Organized Surface Relief of Statically Deformed Aluminum Alloy Applied Mechanics and Materials 770 288-293.
21. Mustaqeem A. Anwar SM Khan AR. Majid M. (2017) A statistical analysis based recommender model for heart disease patients International Journal of Medical Informatics 108 134-145.
22. Napoli N.J. Demas M.W. Mendu S. Stephens C.L. Kennedy K.D Harrivel A.R Bailey R.E. Barnes L.E. (2018) Uncertainty in heart rate complexity metrics caused by R-peak perturbations Computers in Biology and Medicine 103 198-207.
23. Schwarz G. (1978). Estimating the Dimension of a Model. The Annals of Statistics 6(2) 461-464.
24. Serrano E. Figliola A. (2009) Wavelet Leaders: A new method to estimate the multifractal singularity spectra Physica A: Statistical Mechanics and its Applications 388(14) 2793-2805.
25. Sharma L.D. Sunkaria R.K. (2018) Stationary wavelet transform based technique for automated external defibrillator using optimally selected classifiers Measurement 125 29-36.
26. Shen C. Yu Z. Liu Z. (2015) The use of statistics in heart rhythm research: a review Heart Rhythm 12(6) 1376-1386.
27. Sugiura N. (1978). Further analysts of the data by akaike’ s information criterion and the finite corrections. Communications in Statistics - Theory and Methods 7(1) 13-26.
28. Wang Y. Wei S. Zhang S. Zhang Y. Zhao L. Liu C. Murray A. (2018) Comparison of time-domain frequency-domain and nonlinear analysis for distinguishing congestive heart failure patients from normal sinus rhythm subjects Biomedical Signal Processing and Control 42 30-36.