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Study of autonomic modulation by non-linear analysis of heart rate variability in different age groups and analysis of health status, disease and risk of death in dogs

L. Martinello F.G. Romão M.F. Godoy L.H.A. Machado M.H. Tsunemi M.L.G. Lourenço
Polish Journal of Veterinary Sciences | 2023 | vol. 26 | No 4 | 581-590 | DOI: 10.24425/pjvs.2023.148278
Keywords dogs sympathetic system parasympathetic system and heart rate variability
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Abstract

The symbolic analysis of heart rate variability (biomarker of cardiac autonomic homeostasis) is a nonlinear and effective tool for pattern extraction and classification in a series analysis, which implies the transformation of an original time series into symbols, represented by numbers. Autonomic heart rate control is influenced by different factors, and better indicators of heart rate variability are found in healthy young individuals than in older and sicker individuals. The aim of this study was to compare the indicators of heart rate variability among healthy dogs in different age groups and in health status using the nonlinear method of symbolic analysis to evaluate the diagnostic accuracy of this method for the risk of death in dogs. An increase in cardiac sympathetic modulation was observed in puppies and dogs at risk of death, which was evidenced by a marked increase of 0 V% (without variation – associated with sympathetic modulation) and a decrease in patterns of 2 V% (two variations – associated with parasympathetic modulation), while the opposite was observed in young adult dogs with increased parasympathetic modulation. Elderly dogs showed a gradual decrease in parasympathetic activity, which tended to worsen with loss of health. It is concluded that the variables of symbolic analysis may be useful to evaluate autonomic modulation in dogs and assist in the differentiation between health states, advanced disease and death throughout the life cycle and have been shown to be indices with high specificity, sensitivity and diagnostic accuracy to help identify dogs at risk of death.
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Bibliography

1. Baisan RA, Vulpe V, Ohad DG (2021) Short-term heart rate variability in healthy dogs and dogs in various stages of degenerative mitral valve disease evaluated before pharmacotherapy. J Vet Cardiol 274: 1-6.
2. Barbosa MPCR, Silva NT, Azevedo FM, Pastre CM, Vanderlei CM (2016) Comparison of Polar RS800G3TM heart rate monitor with Polar S810iTM and electrocardiogram to obtain the series of RR intervals and analysis of heart rate variability at rest. Clin Physiol Funct Imaging 36: 112-117.
3. Bogucki S, Noszczyk-Nowak A (2017) Short-term heart rate variability in dogs with sick sinus syndrome or chronic mitral valve disease as compared to healthy Controls. Pol J Vet Sci 20: 167-172.
4. Corrêa MS, Catai AM, Milan-Mattos J, Porta A, Driusso P (2019) Cardiovascular autonomic modulation and baroreflex control in the second trimester of pregnancy: A cross sectional study. Plos One 14: 1-16. 5. Costa MD, Davis RB, Goldberger AL (2017) Heart Rate Fragmentation: A New Approach the Analysis of Cardiac Interbeat Interval Dynamics. Front in Physiol 8: 1-13.
6. Cysarz D, Leeuwen PV, Edelhäuser F, Montano N, Somers KV, Porta A (2015) Symbolic Transformations of Heart Rate Variability Preserve Information About cardiac autonomic control. Physiol Meas 36: 643-658.
7. Cysarz D, Porta A, Montano N, Leeuwen VP, Kurths J, Wessel N (2013) Differentapproaches of symbolic dynamics to quantify heart rate complexity. 35th Annual International Conference of the IEEE EMBS: Osaka, Japão, Japan, 3-7 July 2013.
8. Essner A, Sjostrom R, Ahlgrn E, Gustas P, Edge-hughes L, Zetterberg L, Hellstrom K (2015) Comparison of polar RS800CX heart rate monitor and electrocardiogram for measuring interbeat intervals in healthy dogs. Physiol Behav 138: 247-253.
9. Essner A, Sjostrom R, Ahlgrn E, Lindmark B (2013) Validity and Reability of Polar rs800cx Hear Rate Monitor, Measuring Heart Rate in Dogs During Standing Position and at Trot on a Treadmill. Physiol Behav115: 1-5.
10. Godoy MF, Gregório ML (2019) Diagnostic Relevance of Recurrence Plots for the Characterization of Health, Disease or Death in Hu-mans. J Hum Growth Dev 29: 39-47.
11. Godoy MF, Gregório ML. Heart Rate Variability as a Marker of Homeostatic Level. In: Aslanidis T., Nouris Ch (eds) Autonomic Nervous System – Special Interest Topics. London, United Kingdom: Intechopen Limited; 2022, pp 25-35.
12. Goldberger JJ, Arora A, Buckley Una, Shivkumar K (2019) Autonomic Nervous System Dysfunction, J Am Coll Cardiol 73: 1189-1206.
13. Guzzetti S, Borroni E, Garbelli PE, Ceriani E, Bella P, Montano N, Cogliati C, Somers VK, Mallani A, Porta A (2005) Symbolic Dy-namics of Heart Rate Variability: A Probe to Investigate Cardiac Autonomic Modulation. Circulation 12: 465-470.
14. Heitmann A, Huebner T, Schroeder R, Perz S, Voss A (2011) Multivariate short-term heart rate variability: a prediagnostic tool for screening heart disease. Med Biol Eng Comput 49: 41-50.
15. Izhaki N, Perek S, Agbaria M, Raz-Pasteur A (2022) Ultrashortheart rate variability for early risk stratification in pneumonia patients: Preliminary analysis. Isr Med Assoc J. 24: 741-746.
16. Jonckheer-Sheehy VSM, Vinke CM, Ortolani A (2012) Validation of a Polar® human heart rate monitor for measuring heart rate and heart rate variability in adult dogs under stationary conditions. J Vet Behav 7: 205-212.
17. Keene BW, Atkins CE, Bonagura JD, Fox PR, Häggström J, Fuentes VL, Oyama MA, Rush JE Stepien R, Uechi M (2019) ACVIM consensus guidelines for the diagnosis and treatment of myxomatous mitral valve disease in dogs. J Vet Intern Med. 33: 1127-1140.
18. Kleiger RE, Stein PK, Bigger JT (2005) Heart rate variability: measurement and clinical utility. ANE 10: 88-101.
19. Lucia C, Eguchi A, Koch, WJ (2018) New Insights in Cardiac β-Adrenergic Signaling During Heart Failure and Aging. Front Pharmacol 9: 1-14.
20. Lymperopoulos A, Rengo J, Koch WJ (2013) The Adrenergic Nervous System in Heart Failure: Pathophysiology and Therapy. Circ Res 30: 113-116.
21. Martinello L, Cruz-aleixo AS, Romão FG, Lima MCF, Tsunemi MH, Chiacchio SB, Godoy MF, Lourenço MLG (2022) Short-term Heart Rate Variability Analysis in Healthy Dogs of Different Ages. Acta Sci. Vet 50: 1-7.
22. Min K B, Min JY, Paek D, Coh S, Son M (2008) Is 5-Minute Heart Rate Variability a Useful Measure for Monitoring the Autonomic Nervous System of Workers? I nt Heart J 49: 175-181.
23. Moïse SN, Flanders WH, Pariaut R (2020). Beat-to-Beat Patterning of Sinus Rhythm Reveals Non-linear Rhythm in the Dog Compared to the Human. Front in Physiol 10: 1-22.
24. Moura-Tonello SC, Carvalho VO, Godoy MF, Porta A, Leal AMO, Bocchi AE, Catai AM (2019) Evaluation of Cardiac Autonomic Modulation Using Symbolic Dynamics After Cardiac Braz. J. Cardiovasc. Surg 34: 572-580.
25. Nascimento LS, Santos AC, Lima AHRA, Dias RMR, Santos MSB (2013) Symbolic analysis comparison of heart rate variability in middle-aged and older physically active women. Rev Bras Ativ Fís Saúde 19: 253-259.
26. Perseguini NM, Takahashi ACM, Rebelatto JR, Silva E, Borghi-Silva A, Porta A, Montano, N, Catai AM (2011) Spectral and symbolic analysis of the effect of gender and postural change on cardiac autonomic modulation in healthy elderly subjects. Braz J Med Biol 44: 29-37.
27. Peripherr P, Chansaisakorn W, Trisiriroj M, Kalandakanond-Thongsong S, Buranakarlc (2012) Heart rate variability and plasma norepi-nephrine concentration in diabetic dogs at rest. Vet Res Commun 36: 207-214.
28. Porta A, Guzzetti S, Montano N, Furlan R, Pagani M, Malliani A, Cerutti S (2001) Entropy, Entropy Rate, and Pattern Classification as Tools to Typify Complexity in Short Heart Period Variability Series. IEEE Trans Biomed Eng 48: 282-1291.
29. Silva LEV, Geraldini VR, Oliveira BP, Silva CAA, Porta A, Fazan R (2017) Comparison between spectral analysis and symbolic dy-namics for heart rate variability analysis in the rat. Scientific Reports 7: 1-8.
30. Takakura IT, HoshI RA, Santos MA, Pivatelli FC, Nóbregao JH, Guedes DL, Nogueira VF, Frota TQ, Castelo GC, Godoy MF (2017) Recurrence Plots: a New Tool for Quantification of Cardiac Autonomic Nervous System Recovery after Transplant. Braz J Cardiovasc Surg 32: 245-52.
31. Tebaldi M, Machado LHA, Lourenço MLG (2015) Pressão arterial em cães: Uma revisão. Vet. e Zootec. 22(2): 198-208.
32. Tobaldini E, Montano N, Wei SG, Zhang ZH, Francis J, Weiss RM, Casali KR, Felder RB, Porta A (2009) Symbolic Analysis of the Effects of Central Mineralocorticoid Receptor Antagonist in Heart Failure Rats. IEEE Trans Biomed Eng 150: 21-26.
33. Vanderlei LCM, Silva RA, Pastre CM, Azevedo FM, Godoy MF (2009) Comparison of the Polar S810i monitor and the ECG for the analysis of heart rate variability in the time and frequency domains. Braz J Med Biol Res 41: 205-217.
34. Valencia JF, Vallverdu M, Rivero I, Voss A, Luna AB, Porta A, Caminal P (2015) Symbolic dynamics to discriminate healthy and is-chemic dilated cardiomyopathy populations: an application to the variability of heart period and QT interval. Philos Trans A Math Phys Eng Sci 373: 1-20.
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Authors and Affiliations

L. Martinello
1
F.G. Romão
1
M.F. Godoy
2
L.H.A. Machado
1
M.H. Tsunemi
3
M.L.G. Lourenço
1
  1. São Paulo State University (Unesp), School of Veterinary Medicine and Animal Science
  2. Department of Cardiology and Cardiovascular Surgery – São José do Rio Preto Medical School (FAMERP)
  3. São Paulo State University (Unesp), Institute of Biosciences, Botucatu, São Paulo, Brazil

A new method of cardiac sympathetic index estimation using a 1D-convolutional neural network

Marcin Kołodziej Andrzej Majkowski Paweł Tarnowski Remigiusz Jan Rak Andrzej Rysz
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | 3 | e136921 | DOI: 10.24425/bpasts.2021.136921
Keywords epilepsy seizure detection seizure prediction convolutional neural network deep learning ECG HRV cardiac sympathetic index
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Abstract

Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal.
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Bibliography

  1.  World Health Organization (WHO), “Epilepsy”, 2019. Accessed: Jul. 10, 2020. [Online]. Available: https://www.who.int/news-room/ fact-sheets/detail/epilepsy
  2.  B. Sommer et al., “Resection of cerebral gangliogliomas causing drug-resistant epilepsy: short- and long-term outcomes using intraoperative MRI and neuronavigation”, Neurosurg. Focus 38(1), E5 (2015), doi: 10.3171/2014.10.FOCUS14616.
  3.  T. Harnod, C.C.H. Yang, Y.-L. Hsin, P.-J. Wang, K.-R. Shieh, and T.B.J. Kuo, “Heart rate variability in patients with frontal lobe epilepsy”, Seizure 18(1), 21–25 (2009), doi: 10.1016/j.seizure.2008.05.013.
  4.  K. Jansen and L. Lagae, “Cardiac changes in epilepsy”, Seizure 19(8),455–460 (2010), doi: 10.1016/j.seizure.2010.07.008.
  5.  R. Brotherstone and A. McLellan, “Parasympathetic alteration during sub-clinical seizures”, Seizure 21(5), 391–398 (2012), doi: 10.1016/j. seizure.2012.03.011.
  6.  A. Van de Vel et al., “Non-EEG seizure detection systems and potential SUDEP prevention: State of the art: Review and update”, Seizure 41, 141–153 (2016), doi: 10.1016/j.seizure.2016.07.012.
  7.  U.R. Acharya, Y. Hagiwara, and H. Adeli, “Automated seizure prediction”, Epilepsy Behav. 88, 251–261 (2018), doi: 10.1016/j. yebeh.2018.09.030.
  8.  G. Giannakakis, V. Sakkalis, M. Pediaditis, and M. Tsiknakis, “Methods for Seizure Detection and Prediction: An Overview”, in Modern Electroencephalographic Assessment Techniques: Theory and Applications, pp. 131–157, V. Sakkalis, Ed. New York, NY: Springer, 2015.
  9.  E. Bou Assi, D.K. Nguyen, S. Rihana, and M. Sawan, “Towards accurate prediction of epileptic seizures: A review”, Biomed. Signal Process. Control 34, 144–157 (2017), doi: 10.1016/j.bspc.2017.02.001.
  10.  G. Giannakakis, M. Tsiknakis, and P. Vorgia, “Focal epileptic seizures anticipation based on patterns of heart rate variability parameters”, Computer Methods and Programs in Biomedicine 178, 123–133 (2019), doi: 10.1016/j.cmpb.2019.05.032.
  11.  M. Kotas, “Projective filtering of time-aligned beats for foetal ECG extraction”, Bull. Pol. Acad. Sci. Tech. Sci. 55(4), 331‒339 2007.
  12.  K. Lewenstein, M. Jamroży, and T. Leyko, “The use of recurrence plots and beat recordings in chronic heart failure detection”, Bull. Pol. Acad. Sci. Tech. Sci. 64(2), 339–345 (2016).
  13.  J. Jarczewski, A. Furgała, A. Winiarska, M. Kaczmarczyk, and A. Poniatowski, “Cardiovascular response to different types of acute stress stimulations”, Folia Medica Cracoviensia 59(4), 95–110 (2019).
  14.  J. Jeppesen, S. Beniczky, P. Johansen, P. Sidenius, and A. Fuglsang-Frederiksen, “Comparing maximum autonomic activity of psychogenic non-epileptic seizures and epileptic seizures using heart rate variability”, Seizure 37, 13–19 (2016), doi: 10.1016/j.seizure.2016.02.005.
  15.  J. Jeppesen, S. Beniczky, P. Johansen, P. Sidenius, and A. Fuglsang-Frederiksen, “Detection of epileptic seizures with a modified heart rate variability algorithm based on Lorenz plot”, Seizure 24, 1–7 (2015), doi: 10.1016/j.seizure.2014.11.004.
  16.  J. Jeppesen, S. Beniczky, P. Johansen, P. Sidenius, and A. Fuglsang-Frederiksen, “Using Lorenz plot and Cardiac Sympathetic Index of heart rate variability for detecting seizures for patients with epilepsy”, in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014, pp. 4563–4566, doi: 10.1109/EMBC.2014.6944639.
  17.  F. Fürbass, S. Kampusch, E. Kaniusas, J. Koren, S. Pirker, R. Hopfengärtner, H. Stefan, T. Kluge, C. Baumgartner, “Automatic multimodal detection for long-term seizure documentation in epilepsy”, Clinical Neurophysiology 128(8), 1466–1472 (2017), doi: 10.1016/j. clinph.2017.05.013.
  18.  M. Toichi, T. Sugiura, T. Murai, and A. Sengoku, “A new method of assessing cardiac autonomic function and its comparison with spectral analysis and coefficient of variation of R-R interval”, J. Auton. Nerv. Syst. 62(1–2), 79–84 (1997), doi: 10.1016/s0165-1838(96)00112-9.
  19.  J. Pan and W.J. Tompkins, “A Real-Time QRS Detection Algorithm”, IEEE Transactions on Biomedical Engineering BME-32(3), 230–236 (1985), doi: 10.1109/TBME.1985.325532.
  20.  S. Rezaei, S. Moharreri, S. Ghiasi, and S. Parvaneh, “Diagnosis of sleep apnea by evaluating points distribution in poincare plot of RR intervals”, in 2017 Computing in Cardiology (CinC), 2017, pp. 1–4, doi: 10.22489/CinC.2017.158-398.
  21.  S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. J. Inman, “1D Convolutional Neural Networks and Applications: A Survey”, arXiv:1905.03554 [cs, eess], May 2019, Accessed: Jul. 10, 2020. [Online]. Available: http://arxiv.org/abs/1905.03554.
  22.  T. Poggio and Q. Liao, “Theory I: Deep networks and the curse of dimensionality”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 761–773 (2018).
  23.  J. Kurek, B. Świderski, S. Osowski, M. Kruk, and W. Barhoumi, “Deep learning versus classical neural approach to mammogram recognition”, Bull. Pol. Acad. Sci. Tech. Sci. Vol. 66(6), 831‒840 2018, doi: 10.24425/bpas.2018.125930.
  24.  Y. Zhang and Z. Wang, “Research on intelligent algorithm for detecting ECG R waves”, in 2015 IEEE 5th International Conference on Electronics Information and Emergency Communication, 2015, pp. 47–50, doi: 10.1109/ICEIEC.2015.7284484.
  25.  M. Kołodziej, A. Majkowski, P. Tarnowski, R.J. Rak, and A. Rysz, “Implementation of 1DConvolutional Neural Network for Cardiac Sympathetic Index Estimation”, presented at the 2020 IEEE 21st International Conference on Computational Problems of Electrical Engineering (CPEE), 2020.
  26.  K.M. Gaikwad and M.S. Chavan, “Removal of high frequency noise from ECG signal using digital IIR butterworth filter”, in 2014 IEEE Global Conference on Wireless Computing Networking (GCWCN), 2014, pp. 121–124, doi: 10.1109/GCWCN.2014.7030861.
  27.  M. Kołodziej, A. Majkowski, and R.J. Rak, “A new method of feature extraction from EEG signal for brain-computer interface design”, Prz. Elektrotechniczny 9, 35–38 (2010).
  28.  K. Hayase, K. Hayashi, and T. Sawa, “Hierarchical Poincaré analysis for anaesthesia monitoring”, J. Clin. Monit. Comput. 34, 1321–1330 (2020), doi: 10.1007/s10877-019-00447-0.
  29.  J. Niehoff, M. Matzkies, F. Nguemo, J. Hescheler, and M. Reppel, “The Effect of Antiarrhythmic Drugs on the Beat Rate Variability of Human Embryonic and Human Induced Pluripotent Stem Cell Derived Cardiomyocytes”, Sci. Rep. 9(1), 14106 (2019), doi: 10.1038/ s41598-019-50557-7.
  30.  M.M. Platiša, T. Bojić, S. Mazić, and A. Kalauzi, “Generalized Poincaré plots analysis of heart period dynamics in different physiological conditions: Trained vs. untrained men”, PLoS ONE 14(7), e0219281 (2019), doi: 10.1371/journal.pone.0219281.
  31.  P. Fontana, N.R.A. Martins, M. Camenzind, M. Boesch, F. Baty, O.D. Schoch, M.H. Brutsche, R.M. Rossi, and S. Annaheim, “Applicability of a Textile ECG-Belt for Unattended Sleep Apnoea Monitoring in a Home Setting”, Sensors (Basel) 19(15), 3367 (2019), doi: 10.3390/ s19153367.
  32.  T. Schmidt, S. Wulff, K.-M. Braumann, and R. Reer, “Determination of the Maximal Lactate Steady State by HRV in Overweight and Obese Subjects”, Sports Med. Int. Open 3(2), E58–E64 (2019), doi: 10.1055/a-0883-5473.
  33.  J. Piskorski and P. Guzik, “Geometry of the Poincaré plot of RR intervals and its asymmetry in healthy adults”, Physiol. Meas. 28(3), 287–300 (2007), doi: 10.1088/0967-3334/28/3/005.
  34.  Ö. Yıldırım, P. Pławiak, R.-S. Tan, and U. R. Acharya, “Arrhythmia detection using deep convolutional neural network with long duration ECG signals”, Computers in Biology and Medicine 102, 411–420, (2018), doi: 10.1016/j.compbiomed.2018.09.009.
  35.  B. Zhao, H. Lu, S. Chen, J. Liu, and D. Wu, “Convolutional neural networks for time series classification”, Journal of Systems Engineering and Electronics 28(1), 162–169 (2017), doi: 10.21629/JSEE.2017.01.18.
  36.  V. Lebedev and V. Lempitsky, “Speeding-up convolutional neural networks: A survey”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 799‒810 (2018), doi: 10.24425/BPAS.2018.125927.
  37.  M. Grochowski, A. Kwasigroch, and A. Mikołajczyk, “Selected technical issues of deep neural networks for image classification purposes”, Bull. Pol. Acad. Sci. Tech. Sci. 67(2), 363–376 (2019).
  38.  X. Glorot, A. Bordes, and Y. Bengio, “Deep Sparse Rectifier Neural Networks”, in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011, 315–323, [Online]. Available: http://proceedings.mlr.press/v15/glorot11a.html.
  39.  A. Krizhevsky, I. Sutskever, and G.E. Hinton, “ImageNet classification with deep convolutional neural networks”, Commun. ACM 60(6), 84–90 (2017), doi: 10.1145/3065386.
  40.  Y. Gal and Z. Ghahramani, “A Theoretically Grounded Application of Dropout in Recurrent Neural Networks”, in Advances in Neural Information Processing Systems 29, pp.1019–1027, Eds. D.D. Lee, M. Sugiyama, U.V. Luxburg, I. Guyon, and R. Garnett, Curran Associates, Inc., 2016.
  41.  S. Albawi, T.A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network”, in 2017 International Conference on Engineering and Technology (ICET), 2017, pp. 1–6, doi: 10.1109/ICEngTechnol.2017.8308186.
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Authors and Affiliations

Marcin Kołodziej
ORCID: ORCID
Andrzej Majkowski
ORCID: ORCID
Paweł Tarnowski
ORCID: ORCID
Remigiusz Jan Rak
ORCID: ORCID
Andrzej Rysz
ORCID: ORCID

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