Forecasting epilepsy from the heart rate signal

Article

Abstract

Information contained in the R-R interval series, specific to the pre-ictal period, was sought by applying an unsupervised fuzzy clustering algorithm to the N-dimensional phase space of N consecutive interval durations or the absolute value of duration differences. Data sources were individual, complex partial seizures of temporal-lobe epileptics and generalised seizures of rats rendered epileptic with hyperbaric oxygen. Forecasting success was 86% and 82% (zero false positives in resistant rats), respectively, at times ranging from 10 min to 30s prior to seizure onset. Although certain forecasting clusters predominated in the patient group and different ones predominated in the animal group, forecasting on the whole was seizure-specific. The high prediction sensitivity of this method, which matches that of EEG-based methods, seems promising. It is believed that an on-line version of the algorithm, trained on each patient's peri-ictal ECG, could serve as a basis for a simple seizure alarm system.

Keywords

ECG Seizure Ictal tachycardia Forecasting Heart rate variability Unsupervised fuzzy clustering 

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References

  1. Akselrod, S., Gordon, D., Ubel, F. A., Shanon, D. C., Berger, A. C., andCohen, R. J. (1981) ‘Power spectrum of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control’,Science,213, pp. 220–222Google Scholar
  2. Baumert, M., Baier, V., Haueisen, J., Wessel, N., Meyerfeldt, U., Schirdewan, A., andVoss, A. (2004): ‘Forecasting of life threatening arrhythmias using the compression entropy of heart rate’,Methods Inform. Med.,43, pp. 202–206Google Scholar
  3. Baumgartner, C., Serles, W., Leutmezer, F., Pataraia, E., Aull, S., Czech, T., Pietrzyk, U., Relic, A., andPodreka, I. (1998): ‘Preictal SPECT in temporal lobe epilepsy: regional cerebral blood flow is increased prior to electroencepzhalographyseizure onset’,J. Nucl. Med.,39, pp. 978–982Google Scholar
  4. Brown, L., Beightol, A., Koh, J., andEcckberg, D. L. (1993): ‘Important influence of respiration on human R-R interval power spectra is largely ignored’,J. Appl. Physiol.,75, pp. 2310–2317Google Scholar
  5. Cerutti, S., Bianchi, A. M., andMainardi, L. T. (1995): ‘Spectral analysis of the heart rate variability signal’ inMalik, M., andCamm, A. J. (Eds). ‘Heart rate variability’ (Futura Publishing, Armonk, NY, 1995), pp. 63–74Google Scholar
  6. Frysinger, R. C., Engel, J., andHarper, R. M. (1993): ‘Interictal heart rate patterns in partial seizure disorders’,Neurology,43, pp. 2136–2139Google Scholar
  7. Gath, I., andGeva, A. B. (1989): ‘Unsupervised optimal fuzzy clustering’IEEE Trans. Pattern Anal. Mach. Intell.,7, pp. 773–781Google Scholar
  8. Geva, A. B., andKerem, D. H. (1998): ‘Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering’,IEEE Trans. Biomed. Eng.,45, pp. 1205–1216CrossRefGoogle Scholar
  9. Geva, A. B., andKerem, D. H. (1999): ‘Brain state identification and forecasting of acute pathology using unsupervised fuzzy clustering of EEG temporal patterns’ inTeodorescu, H. N., Kandel, A., andJain, L. C. (Eds), ‘Fuzzy and neuro-fuzzy systems in medicine’ (CRC International Series on Computational Intelligence, CRC Press, Boca Raton, Florida, 1999), pp. 57–93Google Scholar
  10. Geva, A. B., andKerem, D. H. (2002): ‘Fuzzy clustering in medicine: applications to electrophysiological signal processing’ inBarro, S., andMarín, R. (Eds): ‘Fuzzy logic in medicine’ (Physica-Verlag, Heidelberg, Germany, 2002), pp. 137–176Google Scholar
  11. Goldberger, A. L., andWest, B. J. (1987): ‘Applications of nonlinear dynamics to clinical cardiology’,Ann. NY Acad. Sci.,504, pp. 195–213Google Scholar
  12. Harel, T., Gath, I., andBen-Haim, S. (1997): ‘High resolution estimation of the heart rate variability signal’,Med. Biol. Eng. Comput.,35, pp. 1–5Google Scholar
  13. Iasemidis, L. D., andSackellares, J. C. (1991): ‘The evolution with time of the spatial distribution of the largest Lyapunov exponent on the human epileptic cortex’ inDuke, D., andPritchard, W. (Eds): ‘Measuring Chaos in the Human Brain’ (World Scientific, Singapore, 1991), pp. 49–82Google Scholar
  14. Keilson, M. J., Hauser, W. A., andKagrill, J. P. (1989): ‘Electrocardiographic changes during electrographic seizures’,Arch. Neurol.,46, pp. 1169–1170Google Scholar
  15. Lathers, C. M., Schraeder, P. L., andWeiner, F. L. (1987): ‘Synchronization of cardiac autonomic neural discharge with epileptogenic activity: the lockstep phenomenon’,Electroencephalogr. Clin. Neurophysiol.,67, pp. 247–259Google Scholar
  16. Lehnertz, K., Andrzejak, R. G., Arnhold, J., Kreuz, T., Mormann, F., Rieke, C., Widman, G., andElger, C. E. (1998): ‘Seizure prediction by non-linear time series analysis of brain electrical activity’,Eur. J. Neurosci,10, pp. 786–789Google Scholar
  17. Le Van Quyen, M., Martinerie, J., Navarro, V., Boon, P., D'Have, M., Adam, C., Renault, B., Varela, F., andBaulac, M. (2001): ‘Anticipation of epileptic seizures from standard EEG recordings’,Lancet,357, pp. 183–188Google Scholar
  18. Litt, B., andLehnertz, K. (2002): ‘Seizure prediction and the preseizure period’,Cur. Opin. Neurol.,15, pp. 173–177Google Scholar
  19. Long, T. J., Robinson, S. E., andQuinlivan, L. S. (1999): ‘Effectiveness of heart rate seizure detection compared to EEG in an epilepsy monitoring unit (EMU)’,Epilepsia,40, p. 174Google Scholar
  20. Malik, M., andCamm, A. J. (Eds) (1995): ‘Heart rate variability’ (Futura Publishing, Armonk, NY, 1995)Google Scholar
  21. Malik, M. (1995): ‘Geometrical methods for heart rate variability assessment’ inMalik, M., andCamm, A. J. (Eds): ‘Heart rate variability’ (Futura Publishing, Armonk, NY, 1995), pp. 47–62Google Scholar
  22. Malik, M. (Chairman),Task Force of the European Society of Cardiology and North American Society of Pacing & Electrophysiology (1996): ‘Heart rate variability: standards of measurements, physiological interpretation and clinical use’,Circulation,93, pp. 1043–1065Google Scholar
  23. Marshall, D. W., Westmoreland, B. F., andSharbrough, F. W. (1983): ‘Ictal tachycardia during temporal lobe seizures’,Mayo Clin. Proc.,58, pp. 443–446Google Scholar
  24. Mayanagi, Y., Watanabe, E., andKameka, Y. (1996): ‘Mesial temporal lobe epilepsy: clinical features and seizure mechanism’,Epilepsia,37, pp. 57–60CrossRefGoogle Scholar
  25. Nei, M., Ho, R. T., andSperling, M. R. (2000): ‘EKG abnormalities during partial seizures in refractory epilepsy’,Epilepsia,41, pp. 542–548CrossRefGoogle Scholar
  26. Novak, P., andNovak, V. (1993): ‘Time/frequency mapping of the heart rate, blood pressure and respiratory signals’,Med. Biol. Eng. Comput.,31, pp. 103–110Google Scholar
  27. Novak, V., Reeves, A. L., Novak, P., Low, P. A., andSharbrough, F. W. (1999): ‘Time-frequency mapping of R-R interval during complex partial seizures of temporal lobe origin’,J. Auton. Nerv. Syst.,77, pp. 195–202CrossRefGoogle Scholar
  28. Pahlm, O., andSörnmo, L. (1984): ‘Software for QRS detection in ambulatory monitoring—a review’,Med. Biol. Eng. Comput.,22, pp. 289–297Google Scholar
  29. Scherthaner, C., Lindinger, G., Potzelberger, R., Zeiler, K., andBaumgartner, C. (1999): ‘Autonomic epilepsy—the influence of epileptic discharges on heart rate and rhythm’,Wien. Klin. Wochenschr.,111, pp. 392–401Google Scholar
  30. Schmidt, G., andMorfill, G. E. (1995) ‘Nonlinear methods for heart rate variability assessment’ inMalik, M., andCamm, A. J. (Eds): ‘Heart rate variability’ (Futura Publishing, Armonk, NY, 1995), pp. 87–98Google Scholar
  31. Shusterman, V., Aysin, B., Anderson, K. P., andBeigel, A. (2001): ‘Multidimensional rhythm disturbances as a precursor of sustained ventricular tachyarrhythmias’,Circ. Res.,88, pp. 705–712Google Scholar
  32. Skinner, J. E., Pratt, C. M., andVybiral, T. (1993): ‘A reduction in the correlation dimension of heartbeat intervals precedes imminent ventricular fibrillation in human subjects’Am. Heart J.,125, pp. 731–743CrossRefGoogle Scholar
  33. Vila, J., Palacios, F., Presedo, J., Fernandez-Delgado, M., Felix, P., andBarro, S. (1997): ‘Time-frequency analysis of heart-rate variability: an improved method for monitoring and diagnosing myocardial ischemia’,IEEE Eng. Med. Biol.,16, pp. 119–126Google Scholar
  34. Weinand, M. E., Carter, L. P., El-Saadany, W. F., Sioutos, P. J., Labiner, D. M., andCommen, K. J. (1997): ‘Cerebral blood flow and temporal lobe epileptogenicity’,J. Neurosurg.,86, pp. 226–232Google Scholar
  35. Wilkund, U., Akay, M., andNiklasson, U. (1997): ‘Short-term analysis of heart-rate variability by adapted wavelet transforms’,IEEE Eng. Med. Biol.,16, pp. 113–118Google Scholar

Copyright information

© IFMBE 2005

Authors and Affiliations

  1. 1.Recanati Institute for Maritime StudiesUniversity of HaifaHaifaIsrael
  2. 2.Department of Electrical EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael

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