Heart rate variability dynamics for the prognosis of cardiovascular risk
Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients....
- Autores:
-
Ramírez Moreno, David Fernando
Calvo Echeverry, Paulo César
Agredo Rodríguez, Wilfredo
Ramírez Villegas, Juan Felipe
Lam Espinosa, Eric
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2011
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/11994
- Acceso en línea:
- http://red.uao.edu.co//handle/10614/11994
- Palabra clave:
- Inteligencia computacional
Cardiología
Neurociencia computaciona
Computational neuroscience
Cardiology
Artificial intelligence
- Rights
- openAccess
- License
- Derechos Reservados - Universidad Autónoma de Occidente
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Heart rate variability dynamics for the prognosis of cardiovascular risk |
| title |
Heart rate variability dynamics for the prognosis of cardiovascular risk |
| spellingShingle |
Heart rate variability dynamics for the prognosis of cardiovascular risk Inteligencia computacional Cardiología Neurociencia computaciona Computational neuroscience Cardiology Artificial intelligence |
| title_short |
Heart rate variability dynamics for the prognosis of cardiovascular risk |
| title_full |
Heart rate variability dynamics for the prognosis of cardiovascular risk |
| title_fullStr |
Heart rate variability dynamics for the prognosis of cardiovascular risk |
| title_full_unstemmed |
Heart rate variability dynamics for the prognosis of cardiovascular risk |
| title_sort |
Heart rate variability dynamics for the prognosis of cardiovascular risk |
| dc.creator.fl_str_mv |
Ramírez Moreno, David Fernando Calvo Echeverry, Paulo César Agredo Rodríguez, Wilfredo Ramírez Villegas, Juan Felipe Lam Espinosa, Eric |
| dc.contributor.author.none.fl_str_mv |
Ramírez Moreno, David Fernando Calvo Echeverry, Paulo César Agredo Rodríguez, Wilfredo Ramírez Villegas, Juan Felipe Lam Espinosa, Eric |
| dc.subject.armarc.spa.fl_str_mv |
Inteligencia computacional Cardiología Neurociencia computaciona |
| topic |
Inteligencia computacional Cardiología Neurociencia computaciona Computational neuroscience Cardiology Artificial intelligence |
| dc.subject.armarc.eng.fl_str_mv |
Computational neuroscience Cardiology Artificial intelligence |
| description |
Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis |
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2011 |
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2011-02 |
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2020-02-26T20:41:07Z |
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2020-02-26T20:41:07Z |
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Artículo de revista |
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http://red.uao.edu.co//handle/10614/11994 |
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eng |
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eng |
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PLoS ONE. Volumen 6, (febrero 2011) |
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2 |
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6 |
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Ramírez-Villegas J.F., Lam-Espinosa E., Ramírez-Moreno D.F., Calvo-Echeverry P .C., Agredo-Rodríguez W (2011). Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk. PLoS ONE 6(2): e17060. http://red.uao.edu.co//handle/10614/11994 |
| dc.relation.ispartofjournal.eng.fl_str_mv |
Plos One |
| dc.relation.references.none.fl_str_mv |
Mohammadzadeh Asl B, Kamaledin Setarehdan S, Mohebbi M (2008) Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artificial Intelligence in Medicine 44: 51–64. Rajendra Acharya U, Paul Joseph K, Kannathal N, Min Lim C, Suri JS (2006) Heart rate variability: a review. Med Bio Eng Comput 44: 1031–1051. Bezerianos A, Papadimitriou S, Alexopoulos D (1999) Radial basis function neural networks for the characterization of heart rate variability dynamics. Artificial Intelligence in Medicine 15: 215–234. Belova NY, Mihaylov SV, Piryova BG (2007) Wavelet transform: A better approach for the evalation of instantaneous changes in heart rate variability. Autonomic Neuroscience: Basic and Clinical 131: 107–122. Lopes P, White J (2006) Heart rate variability: Measurement methods and practical implications. In: Maud PJ, Foster C, editors. Physiological Assessment of Human Fitness. pp. 39–61. García-González MA (1998) Estudio de la Variabilidad del ritmo cardíaco mediante técnicas estadísticas, espectrales y no-lineales. PhD Thesis, Universitat Politècnica De Catalunya. Montano N, Porta A, Cogliati C, Costantino G, Tobaldini E, et al. (2008) Heart rate variability explored in the frequency domain: A tool to investigate the link between heart and behavior. Neuroscience and Biobehavioral Reviews. In Press. Acharyaa UR, Bhatb PS, Iyengarc SS, Raod A, Dua S (2003) Classification of heart rate data using artificial neural network and fuzzy equivalence relation. Pattern Recognition 36: 61–68. Kuss O, Schumann B, Kluttig A, Greiser KH, Haerting J (2008) Time domain parameters can be estimated with less statistical error than frequency domain parameters in the analysis of heart rate variability. Journal of Electrocardiology 41: 287–291 Urbanowicza K, Zebrowski J, Baranowskic JR, Holysta JA (2007) How random is your heart beat?. Physica A 384: 439–447. Bilgin S, Çolak OH, Koklukaya E, Ari N (2008) Efficient solution for frequency band decomposition problem using wavelet packet in HRV. Digital Signal Processing 18: 892–899. Task force of the European society of cardiology and the North American society of pacing and electrophysiology (1996) Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use. Circulation 93: 1043–1065 D'Addio G, Acanfora D, Pinna GD, Maestri R, Furgi G, et al. (1998) Reproducibility of Short -and Long-Term Poincaré Plot Parameters Compared with Frequency-Domain HRV Indexes in Congestive Heart Failure. Computers in Cardiology 25: 381–384 D'Addio G, Pinna GD, Maestri R, Acanfora D, Picone C, et al. (1999) Correlation Between Power-law Behavior and Poincaré Plots of Heart Rate Variability in Congestive Heart Failure Patients. Computers in Caridiology 26: 611–614. Thong T (2007) Geometric Measures of Poincaré Plots for the Detection of Small Sympathovagal Shift. Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale 4641–4644. Woo MA, Stevenson WG, Moser DK, Trelease RB, Harper RM (1992) Patterns of beat-to-beat heart rate variability in advanced heart failure. Am Heart J 123: 704–710. Brennan M, Planiswami M, Kamen P (2001) Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability?. IEEE Trans Biomed Eng 48: 1342–1347 Rueda OL, López EJ, Vargas CA, Delgado MB, Murillo CA (2007) La variabilidad de la frecuencia cardíaca como factor pronóstico de mortalidad del infarto del miocardio: Revisión sistemática de estudios observacionales. Médica Sanitas 22 Malik M, Farrell T, Cripps TR, Camm AJ (1989) Heart rate variability in relation to prognosis after myocardial infarction: selection of optimal processing techniques. Eur Heart J 10: 1060–1074. Chesnokov YV (2008) Complexity and spectral análisis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods. Artificial Intelligence in Medicine 43: 151–165. Ísler Y, Kuntalp M (2007) Combining classical HRV indexes with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Computers in Biology and Medicine 37: 1502–1510. Wolf MM, Varigos GA, Hunt D, Sloman JG (1978) Sinus arrhythmia in acute myocardial infarction. Med J Aust 52–53. Penaz J, Roukenz J, Van Der Waal HJ (1968) Spectral Analysis of Some Spontaneous Rhythms in the Circulation.. Leipzig. Germany: Biokybernetik, Karl Marx University. pp. 223–241. Pomeranz M, Macaulay R, Caudill M, Ma K, Kutz I, et al. (1985) Assessment of autonomic function in humans by heart rate spectral analysis. Am J Physiol 248: H151–H153 Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger AC, et al. (1981) Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat to beat cardiovascular control. Science 220–222. Akselrod S, Lishner M, Oz O, Bernheim J, Ravid M (1987) Spectral analysis of fluctuations in heart rate: an objective evaluation of autonomic nervous control in chronic renal failure. Nephron 45: 202–206. Berntson GG, Bigger JT Jr, Eckberg DL, Grossman P, Kaufmann PG, et al. (1997) Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiol 34: 623–648. Broadman A, Schlindwein FS, Rocha AP, Leite A (2002) A study on the optimum order of autoregressive models for heart rate variability. Physiol Meas 23: 324–336. Pagani M, Lombardi F, Guzzetti S, Rimoldi O, Furlan R, et al. (1986) Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympathovagal interaction in man and conscious dog. Circ Res 58: 178–193. Pal GK, Pal P, Nanda N, Amudharaj D, Karthik S (2009) Spectral analysis of heart rate variability (HRV) may predict the future development of essential hypertension. Medical Hypotheses 72: 183–185 Ravichandran IT, Ramasuhha Reddy I, Avudainayagam A (2003) Estimation and Power Spectral Analysis of Heart Instantaneous Frequency (HIF) - A Wavelet Approach. Bio-Signal Processing TENCON 223–226. Fukuda O, Nagata Y, Homma K, Tsuji T (2001) Evaluation of heart rate variability by using wavelet transform and a recurrent neural network. Proceedings of the 23rd Annual EMBS International Conference 1769–1772. Lerma C, Infante O, Perez-Grovas H, Jose MV (2003) Poincaré plot indexes of heart rate variability capture dynamic adaptations after haemodialysis in chronic renal failure patients. Clin Physiol & Func Im 23: 72–80. Marciano F, Migaux ML, Acanfora D, Furgi G, Rengdf F (1994) Quantification of Poincaré Maps for the Evaluation of Heart Rate Variability. Computers in Cardiology 577–580. Singh D, Vinod K (2005) Effect of RR Segment Duration on Short-Term HRV Assessment Using Poincaré Plot. Proceedings of ICISIP 430–434 Brennan M, Palaniswami M, Kamen P (2002) Poincaré plot interpretation using a physiological model of HRV based on a network of oscillators. Am J Physiol Heart Circ Physiol 283: 1873–1886 Hnatkova K, Copie X, Staunton A, Malik M (1995) Numeric processing of Lorenz plots of R–R intervals from long-term ECGs. Comparison with time-domain measures of heart rate variability for risk stratification after myocardial infarction. J Electrocardiol 28: 74–80. Wolf A, Swift JB, Swinney HL, Vastano JA (1985) Determining Lyapunov exponents from a time series. Physica 16D: 285–317. Kobayashi M, Musha T (1982) 1/f fluctuation of heartbeat period. IEEE Trans Biomed Eng 29: 456–257 Goldberger AL (1996) Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside, Lancet 347: 1312–1314 Peng CK, Havlin S, Hausdorf JM, Mietus JE, Stanley HE, et al. (1995) Fractal mechanisms and heart rate dynamics. Long-range correlations and their breakdown with disease. J Electrocardiol 28: 59–65. Hirsh JA, Bishop B (1981) Respiratory sinus arrhythmia in humans: how breathing pattern modulates heart rate. Am J Physiol Heart Circ Physiol 241: H620–H629. Piepoli M, Sleight P, Leuzzi S, Valle F, Spadacini G, et al. (1997) Origin of Respiratory Sinus Arrhythmia in Conscious Humans. Circulation 95: 1813–1821. Katona PG, Jih F (1975) Respiratory sinus arrhythmia: noninvasive measure of parasympathetic cardiac control. J Appl Physiol 39: 801–805. Lloyd-Jones D, Adams R, Carnethon M, de Simone G, Ferguson TB, et al. (2009) Heart disease and stroke statistics – 2009 update: A report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 119: e21–e181. Beltrán-Bohórquez JR (2010) Guías colombianas de cardiología. Síndrome coronario agudo con elevación del ST. Revista Colombiana de Cardiología 17: 111–275. Gallagher D, Terenzi T, de Meersman R (1992) Heart rate variability in smokers, sedentary and aerobically fit individuals. Clinical Autonomic Research 2: 383–387 Liao D, Carnethon M, Evans GW, Cascio WE, Heiss G (2002) Lower heart rate variability is associated with the development of coronary heart disease in individuals with diabetes: the atherosclerosis risk in communities (ARIC) study. Diabetes 51: 3524–3531 Williams JS, Brown SM, Conlin PR (2009) Blood-pressure Measurement. N Engl J Med 360: e6. Motivala SJ, Hurwitz BE, Lagreca AM, Llabre MM, Marks JB, et al. (1999) Aberrant parasympathetic and Hemodynamic function distinguishes a subgroup of psyologically distressed individuals with asymptomatic type-I diabetes mellitus, International Journal of Behavioral Medicine 6: 78–94. Gutierrez A, Lara M, Hernandez PR (2005) Evaluación de un detector de complejo QRS basado en la wavelet de Haar usando las bases de datos MIT-BIH de arritmias y Europea del segmento ST y de la onda T. Computación y Sistemas 8: 293–302 Lippman N, Stein KM, Lerman BB (1994) Comparison of methods for removal of ectopy in measurement of heart rate variability. Am J Physiol Heart Circ Physiol 267: H411–H418. Rosner B (1983) Percentage Points for a Generalized ESD Many-Outlier Procedure. Technometrics 25: 165–172 Ramirez-Villegas JF, Lam-Espinosa E (2009) Análisis de la variabilidad de la frecuencia cardíaca integrando la señal de la frecuencia respiratoria. B. Eng. Thesis, Universidad Autonoma de Occidente, Cali, Colombia. Niskanen JP, Tarvainen MP, Ranta-Aho PO, Karjalainen PA (2002) Software for advanced HRV analysis. Computer Methods and Programs in Biomedicine. In Press Rosso OA, Blanco S, Yordanova J, Kolev V, Figliola A, et al. (2001) Wavelet entropy: a new tool for analysis of short duration brain electrical signals. J Neurosci Methods 105: 65–75. Blanco S, Figliola A, Quian QR, Rosso OA, Serrano E (1998) Time-Frequency analysis of electroencephalogram series (iii): Information transfer function and wavelet packets. Phys Rev E 57: 932–940 Lombardi F (2000) Chaos Theory, Heart Rate Variability, and Arrhythmic Mortality. Circulation 101: 8–10. Meiss JD (2007) Differential dynamical systems. Society for Industrial and Applied Mathematics (SIAM) 434. Sano M, Sawada Y (1985) Measurement of the Lyapunov spectrum from a chaotic time series. Physical Review Letters 55: 1082–1085. Korpelainen JT, Sotaniemi KA, Mäkikallio A, Huikuri HV, Myllylä VV (1999) Dynamic Behavior of Heart Rate in Ischemic Stroke. Stroke 30: 1008–1013. Pincus SM (2001) Assessing Serial Irregularity and its Implications for Health. Annals of the New York Academy of Sciences 954: 245–267 Rencher AC (2002) Methods of Multivariate Analysis. Wiley Series in Probability and Statistics 738. Haykin S (1999) Neural Networks: A Comprehensive Foundation. Prentice Hall, inc. 823 Wang L, Liu B, Wan C (2005) Classification using support vector machines with graded resolution. Proceedings of the international conference on granular computing 666–670 Woo MA, Stevenson WG, Moser DK, Trelease RB, Harper RM (1992) Patterns of beat-to-beat heart rate variability in advanced heart failure. Am Heart J 123: 704–710. Fawcett T (2006) An introduction to ROC analysis. Pat Rec Lett 27: 861–874. Chattipakorn N, Incharoen T, Kanlop N, Chattipakorn S (2007) Heart rate variability in myocardial infarction and heart failure. International Journal of Cardiology 120: 289–296 |
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Ramírez Moreno, David Fernandovirtual::4314-1Calvo Echeverry, Paulo Césarvirtual::982-1Agredo Rodríguez, Wilfredovirtual::335-1Ramírez Villegas, Juan Felipede4a5d2f855a047341c6903be500d787Lam Espinosa, Ericdde3358206b4819ed0f9c2411c9d50492020-02-26T20:41:07Z2020-02-26T20:41:07Z2011-02http://red.uao.edu.co//handle/10614/11994Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysisapplication/pdf15 páginasengPublic Library of SciencePLoS ONE. Volumen 6, (febrero 2011)26Ramírez-Villegas J.F., Lam-Espinosa E., Ramírez-Moreno D.F., Calvo-Echeverry P .C., Agredo-Rodríguez W (2011). Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk. PLoS ONE 6(2): e17060. http://red.uao.edu.co//handle/10614/11994Plos OneMohammadzadeh Asl B, Kamaledin Setarehdan S, Mohebbi M (2008) Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artificial Intelligence in Medicine 44: 51–64.Rajendra Acharya U, Paul Joseph K, Kannathal N, Min Lim C, Suri JS (2006) Heart rate variability: a review. Med Bio Eng Comput 44: 1031–1051.Bezerianos A, Papadimitriou S, Alexopoulos D (1999) Radial basis function neural networks for the characterization of heart rate variability dynamics. Artificial Intelligence in Medicine 15: 215–234.Belova NY, Mihaylov SV, Piryova BG (2007) Wavelet transform: A better approach for the evalation of instantaneous changes in heart rate variability. Autonomic Neuroscience: Basic and Clinical 131: 107–122.Lopes P, White J (2006) Heart rate variability: Measurement methods and practical implications. In: Maud PJ, Foster C, editors. Physiological Assessment of Human Fitness. pp. 39–61.García-González MA (1998) Estudio de la Variabilidad del ritmo cardíaco mediante técnicas estadísticas, espectrales y no-lineales. PhD Thesis, Universitat Politècnica De Catalunya.Montano N, Porta A, Cogliati C, Costantino G, Tobaldini E, et al. (2008) Heart rate variability explored in the frequency domain: A tool to investigate the link between heart and behavior. Neuroscience and Biobehavioral Reviews. In Press.Acharyaa UR, Bhatb PS, Iyengarc SS, Raod A, Dua S (2003) Classification of heart rate data using artificial neural network and fuzzy equivalence relation. Pattern Recognition 36: 61–68.Kuss O, Schumann B, Kluttig A, Greiser KH, Haerting J (2008) Time domain parameters can be estimated with less statistical error than frequency domain parameters in the analysis of heart rate variability. Journal of Electrocardiology 41: 287–291Urbanowicza K, Zebrowski J, Baranowskic JR, Holysta JA (2007) How random is your heart beat?. Physica A 384: 439–447.Bilgin S, Çolak OH, Koklukaya E, Ari N (2008) Efficient solution for frequency band decomposition problem using wavelet packet in HRV. Digital Signal Processing 18: 892–899.Task force of the European society of cardiology and the North American society of pacing and electrophysiology (1996) Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use. Circulation 93: 1043–1065D'Addio G, Acanfora D, Pinna GD, Maestri R, Furgi G, et al. (1998) Reproducibility of Short -and Long-Term Poincaré Plot Parameters Compared with Frequency-Domain HRV Indexes in Congestive Heart Failure. Computers in Cardiology 25: 381–384D'Addio G, Pinna GD, Maestri R, Acanfora D, Picone C, et al. (1999) Correlation Between Power-law Behavior and Poincaré Plots of Heart Rate Variability in Congestive Heart Failure Patients. Computers in Caridiology 26: 611–614.Thong T (2007) Geometric Measures of Poincaré Plots for the Detection of Small Sympathovagal Shift. Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale 4641–4644.Woo MA, Stevenson WG, Moser DK, Trelease RB, Harper RM (1992) Patterns of beat-to-beat heart rate variability in advanced heart failure. Am Heart J 123: 704–710.Brennan M, Planiswami M, Kamen P (2001) Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability?. IEEE Trans Biomed Eng 48: 1342–1347Rueda OL, López EJ, Vargas CA, Delgado MB, Murillo CA (2007) La variabilidad de la frecuencia cardíaca como factor pronóstico de mortalidad del infarto del miocardio: Revisión sistemática de estudios observacionales. Médica Sanitas 22Malik M, Farrell T, Cripps TR, Camm AJ (1989) Heart rate variability in relation to prognosis after myocardial infarction: selection of optimal processing techniques. Eur Heart J 10: 1060–1074.Chesnokov YV (2008) Complexity and spectral análisis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods. Artificial Intelligence in Medicine 43: 151–165.Ísler Y, Kuntalp M (2007) Combining classical HRV indexes with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Computers in Biology and Medicine 37: 1502–1510.Wolf MM, Varigos GA, Hunt D, Sloman JG (1978) Sinus arrhythmia in acute myocardial infarction. Med J Aust 52–53.Penaz J, Roukenz J, Van Der Waal HJ (1968) Spectral Analysis of Some Spontaneous Rhythms in the Circulation.. Leipzig. Germany: Biokybernetik, Karl Marx University. pp. 223–241.Pomeranz M, Macaulay R, Caudill M, Ma K, Kutz I, et al. (1985) Assessment of autonomic function in humans by heart rate spectral analysis. Am J Physiol 248: H151–H153Akselrod S, Gordon D, Ubel FA, Shannon DC, Barger AC, et al. (1981) Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat to beat cardiovascular control. Science 220–222.Akselrod S, Lishner M, Oz O, Bernheim J, Ravid M (1987) Spectral analysis of fluctuations in heart rate: an objective evaluation of autonomic nervous control in chronic renal failure. Nephron 45: 202–206.Berntson GG, Bigger JT Jr, Eckberg DL, Grossman P, Kaufmann PG, et al. (1997) Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiol 34: 623–648.Broadman A, Schlindwein FS, Rocha AP, Leite A (2002) A study on the optimum order of autoregressive models for heart rate variability. Physiol Meas 23: 324–336.Pagani M, Lombardi F, Guzzetti S, Rimoldi O, Furlan R, et al. (1986) Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympathovagal interaction in man and conscious dog. Circ Res 58: 178–193.Pal GK, Pal P, Nanda N, Amudharaj D, Karthik S (2009) Spectral analysis of heart rate variability (HRV) may predict the future development of essential hypertension. Medical Hypotheses 72: 183–185Ravichandran IT, Ramasuhha Reddy I, Avudainayagam A (2003) Estimation and Power Spectral Analysis of Heart Instantaneous Frequency (HIF) - A Wavelet Approach. Bio-Signal Processing TENCON 223–226.Fukuda O, Nagata Y, Homma K, Tsuji T (2001) Evaluation of heart rate variability by using wavelet transform and a recurrent neural network. 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International Journal of Cardiology 120: 289–296Derechos Reservados - Universidad Autónoma de Occidentehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Heart rate variability dynamics for the prognosis of cardiovascular riskArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTREFinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Inteligencia computacionalCardiologíaNeurociencia computacionaComputational neuroscienceCardiologyArtificial intelligencePublication61e20236-82c5-4dcc-b05c-0eaa9ac06b11virtual::4314-1767bff32-1019-4cc1-a2d8-a8baf8b48240virtual::982-17ba463c5-0d63-46ba-b8b4-667138a06638virtual::335-17ba463c5-0d63-46ba-b8b4-667138a06638virtual::335-1767bff32-1019-4cc1-a2d8-a8baf8b48240virtual::982-161e20236-82c5-4dcc-b05c-0eaa9ac06b11virtual::4314-1https://scholar.google.com/citations?user=RTce1fkAAAAJ&hl=esvirtual::4314-10000-0003-2372-3554virtual::4314-10000-0001-5353-6368virtual::982-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000353744virtual::4314-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000785075virtual::982-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000210978virtual::335-1CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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