RESEARCH PAPER
Statistical Analysis of Human Heart Rhythm with Increased Informativeness
1
Faculty of Computer Information Systems and Software Engineering, Ternopil Ivan Pul’uj National Technical University, 46001, Ruska str. 56, Ternopil, Ukraine
2
Institute of Technological Systems of Information, Mechanical Engineering Faculty, Lublin University of Technology, ul. Nadbystrzycka 38 D, 20–618 , Lublin
Submission date: 2018-07-10
Acceptance date: 2018-12-20
Online publication date: 2019-01-03
Publication date: 2018-12-01
Acta Mechanica et Automatica 2018;12(4):311-315
KEYWORDS
ABSTRACT
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.
REFERENCES (27)
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.
9.
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.
10.
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.
11.
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.
12.
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.
13.
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.
14.
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.
15.
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.
16.
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.
17.
Liddle A.R. (2007), Information criteria for astrophysical model selection, Monthly Notices of the Royal Astronomical Society: Letters, 377(1), 74-78.
18.
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.
19.
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.
20.
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.
21.
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.
22.
Schwarz G. (1978). Estimating the Dimension of a Model. The Annals of Statistics, 6(2), 461-464.
23.
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.
24.
Sharma L.D., Sunkaria R.K. (2018), Stationary wavelet transform based technique for automated external defibrillator using optimally selected classifiers, Measurement, 125, 29-36.
25.
Shen C., Yu Z., Liu Z. (2015), The use of statistics in heart rhythm research: a review, Heart Rhythm, 12(6), 1376-1386.
26.
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.
27.
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.
Share
RELATED ARTICLE
| eISSN: | 2300-5319 |
| ISSN: | 1898-4088 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
