Details

Title

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

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2021

Volume

69

Issue

3

Authors

Keywords

epilepsy ; seizure detection ; seizure prediction ; convolutional neural network ; deep learning ; ECG ; HRV ; cardiac sympathetic index

Divisions of PAS

Nauki Techniczne

Coverage

e136921

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.

Date

22.03.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2021.136921

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; Early Access; e136921
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