Experimental analysis of heart rate variability of long-recording electrocardiograms in normal subjects and patients with coronary artery disease and normal left ventricular function

The heart rate signal contains valuable information about cardiac health, which cannot be extracted without the use of appropriate computerized methods. This paper presents an analysis of various electrocardiograms, the aim of which is to categorize them into two distinct groups. Group A represents young male subjects with no prior occurrence of coronary disease events and Group B represents middle-aged male subjects who have symptomatic coronary artery disease without myocardial infarction and whose 12-lead ECGs do not contain any abnormalities, thus wrongly indicating a normal subject. Electrocardiographic recordings are approximately 2h in length and acquired under conditions that favor the stationarity of collected data. Linear and nonlinear characteristics are studied by applying several techniques including Fourier analysis, Correlation Dimension Estimation, Approximate Entropy, and the Discrete Wavelet Transform. The small variations of the diagnostic information given by each one of the methods as well as the slightly different conclusions among similar studies indicate the necessity of further investigation, combined use, and complementary application of different approaches.

[1]  Bruce J. West,et al.  Applications of Nonlinear Dynamics to Clinical Cardiology a , 1987, Annals of the New York Academy of Sciences.

[2]  P. Atten,et al.  Determination of Attractor Dimension and Entropy for Various Flows: An Experimentalist’s Viewpoint , 1986 .

[3]  T. Seppänen,et al.  Analysis of nonlinear heart rate dynamics in cardiac arrhythmias , 2000, Herzschrittmachertherapie und Elektrophysiologie.

[4]  G Manis,et al.  Hardware design for the computation of heart rate variability , 2002, Journal of medical engineering & technology.

[5]  D. T. Kaplan,et al.  Techniques for analyzing complexity in heart rate and beat-to-beat blood pressure signals , 1990, [1990] Proceedings Computers in Cardiology.

[6]  H. Brooks,et al.  Medical physiology , 1961 .

[7]  Madalena Costa,et al.  No Evidence of Chaos in the Heart Rate Variability of Normal and Cardiac Transplant Human Subjects , 1999, Journal of cardiovascular electrophysiology.

[8]  L. Ohno-Machado Journal of Biomedical Informatics , 2001 .

[9]  D. Kugiumtzis State space reconstruction parameters in the analysis of chaotic time series—the role of the time window length , 1996, comp-gas/9602002.

[10]  J. Miller,et al.  Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. , 1987, The American journal of cardiology.

[11]  A. Aubert,et al.  Analysis of heart rate variability with correlation dimension method in a normal population and in heart transplant patients , 2001, Autonomic Neuroscience.

[12]  L. Nolan,et al.  Biological psychology , 2019, An Introduction to the Psychology of Humor.

[13]  R. Cohen,et al.  Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. , 1981, Science.

[14]  Michel Loève,et al.  Probability Theory I , 1977 .

[15]  K. Karhunen Zur Spektraltheorie stochastischer prozesse , 1946 .

[16]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[17]  Schwartz,et al.  Singular-value decomposition and the Grassberger-Procaccia algorithm. , 1988, Physical review. A, General physics.

[18]  Malvin Carl Teich,et al.  Analysis of spectral and wavelet-based measures used to assess cardiac pathology , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[19]  R. Mccraty,et al.  The effects of emotions on short-term power spectrum analysis of heart rate variability . , 1995, The American journal of cardiology.

[20]  B. Nevitt,et al.  Coping With Chaos , 1991, Proceedings of the 1991 International Symposium on Technology and Society - ISTAS `91.

[21]  D. T. Kaplan,et al.  Aging and the complexity of cardiovascular dynamics. , 1991, Biophysical journal.

[22]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[23]  G. PORENTA,et al.  Prognostic Value of Heart Rate Variability in Patients Awaiting Cardiac Transplantation , 1992, Pacing and clinical electrophysiology : PACE.

[24]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[25]  B. Sayers,et al.  Analysis of heart rate variability. , 1973, Ergonomics.

[26]  M. Doroslovački,et al.  An Integrated System for the Diagnosis of Cardiac Pathology through the Analysis of Heartbeat Interval Variability , 2000 .

[27]  F. Takens Detecting strange attractors in turbulence , 1981 .

[28]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[29]  秦 浩起,et al.  Characterization of Strange Attractor (カオスとその周辺(基研長期研究会報告)) , 1987 .

[30]  A. Babloyantz,et al.  Is the normal heart a periodic oscillator? , 1988, Biological Cybernetics.

[31]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[32]  Steven M. Pincus,et al.  Approximate entropy: a complexity measure for biological time series data , 1991, Proceedings of the 1991 IEEE Seventeenth Annual Northeast Bioengineering Conference.

[33]  A. N. Sharkovskiĭ Dynamic systems and turbulence , 1989 .

[34]  F R Calaresu,et al.  Influence of cardiac neural inputs on rhythmic variations of heart period in the cat. , 1975, The American journal of physiology.

[35]  Gottfried Mayer-Kress,et al.  Dimensions and Entropies in Chaotic Systems , 1986 .