Skip to main content

Time-Frequency Analysis of Heart Rate Variability for Neonatal Seizure Detection


There are a number of automatic techniques available for detecting epileptic seizures using solely electroencephalogram (EEG), which has been the primary diagnosis tool in newborns. The electrocardiogram (ECG) has been much neglected in automatic seizure detection. Changes in heart rate and ECG rhythm were previously linked to seizure in case of adult humans and animals. However, little is known about heart rate variability (HRV) changes in human neonate during seizure. In this paper, we assess the suitability of HRV as a tool for seizure detection in newborns. The features of HRV in the low-frequency band (LF: 0.03–0.07 Hz), mid-frequency band (MF: 0.07–0.15 Hz), and high-frequency band (HF: 0.15–0.6 Hz) have been obtained by means of the time-frequency distribution (TFD). Results of ongoing time-frequency (TF) research are presented. Based on our preliminary results, the first conditional moment of HRV which is the mean/central frequency in the LF band and the variance in the HF band can be used as a good feature to discriminate the newborn seizure from the nonseizure.


  1. 1.

    Rennie JM: Neonatal seizures. European Journal of Pediatrics 1997,156(2):83-87. 10.1007/s004310050559

    Article  Google Scholar 

  2. 2.

    Liu A, Hahn JS, Heldt GP, Coen RW: Detection of neonatal seizures through computerized EEG analysis. Electroencephalography and Clinical Neurophysiology 1992,82(1):30-37. 10.1016/0013-4694(92)90179-L

    Article  Google Scholar 

  3. 3.

    Gotman J, Flanagan D, Rosenblatt B, Bye A, Mizrahi EM: Evaluation of an automatic seizure detection method for the newborn EEG. Electroencephalography and Clinical Neurophysiology 1997,103(3):363-369. 10.1016/S0013-4694(97)00005-2

    Article  Google Scholar 

  4. 4.

    Boashash B, Mesbah M: A time-frequency approach for newborn seizure detection. IEEE Engineering in Medicine and Biology Magazine 2001,20(5):54-64.

    Article  Google Scholar 

  5. 5.

    Faul S, Boylan G, Connolly S, Marnane L, Lightbody G: An evaluation of automated neonatal seizure detection methods. Clinical Neurophysiology 2005,116(7):1533-1541. 10.1016/j.clinph.2005.03.006

    Article  Google Scholar 

  6. 6.

    Quint SR, Messenheimer JA, Tennison MB, Nagle HT: Assessing autonomic activity from the EKG related to seizure onset detection and localization. Proceedings of the 2nd Annual IEEE Symposium on Computer-Based Medical Systems, June 1989, Minneapolis, Minn, USA 2–9.

    Google Scholar 

  7. 7.

    Tavernor SJ, Brown SW, Tavernor RM, Gifford C: Electrocardiograph QT lengthening associated with epileptiform EEG discharges—a role in sudden unexplained death in epilepsy? Seizure 1996,5(1):79-83. 10.1016/S1059-1311(96)80067-7

    Article  Google Scholar 

  8. 8.

    Leutmezer F, Schernthaner C, Lurger S, Pötzelberger K, Baumgartner C: Electrocardiographic changes at the onset of epileptic seizures. Epilepsia 2003,44(3):348-354. 10.1046/j.1528-1157.2003.34702.x

    Article  Google Scholar 

  9. 9.

    Zijlmans M, Flanagan D, Gotman J: Heart rate changes and ECG abnormalities during epileptic seizures: prevalence and definition of an objective clinical sign. Epilepsia 2002,43(8):847-854. 10.1046/j.1528-1157.2002.37801.x

    Article  Google Scholar 

  10. 10.

    Tinuper P, Bisulli F, Cerullo A, et al.: Ictal bradycardia in partial epileptic seizures: autonomic investigation in three cases and literature review. Brain 2001,124(12):2361-2371. 10.1093/brain/124.12.2361

    Article  Google Scholar 

  11. 11.

    Goldberg RN, Goldman SL, Ramsay RE, Feller R: Detection of seizure activity in the paralyzed neonate using continuous monitoring. Pediatrics 1982,69(5):583-586.

    Google Scholar 

  12. 12.

    O'Regan ME, Brown JK: Abnormalities in cardiac and respiratory function observed during seizures in childhood. Developmental Medicine and Child Neurology 2005,47(1):4-9.

    Article  Google Scholar 

  13. 13.

    Kamath MV, Bentley T, Spaziani R, et al.: Time-frequency analysis of heart rate variability signals in patients with autonomic dysfunction. Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, June 1996, Paris, France 373–376.

    Google Scholar 

  14. 14.

    Finley JP, Nugent ST: Heart rate variability in infants, children and young adults. Journal of the Autonomic Nervous System 1995,51(2):103-108. 10.1016/0165-1838(94)00117-3

    Article  Google Scholar 

  15. 15.

    Abeysekera RMSS: Time-frequency domain features of electrocardiographic signals: an interpretation and their application in computer aided diagnosis, Ph.D. thesis. University of Queensland, Brisbane, Australia; 1989.

    Google Scholar 

  16. 16.

    Tacer B, Loughlin PJ: Non-stationary signal classification using the joint moments of time-frequency distributions. Pattern Recognition 1998,31(11):1635-1641. 10.1016/S0031-3203(98)00031-4

    Article  Google Scholar 

  17. 17.

    Boashash B: Time Frequency Signal Analysis and Processing: A Comprehensive Reference. Elsevier, Oxford, UK; 2003.

    Google Scholar 

  18. 18.

    Novak P, Novak V: Time/frequency mapping of the heart rate, blood pressure and respiratory signals. Medical and Biological Engineering and Computing 1993,31(2):103-110. 10.1007/BF02446667

    Article  Google Scholar 

  19. 19.

    Rankine L, Mesbah M, Boashash B: Resolution analysis of the T-class time-frequency distributions. Proceedings of the International Symposium on Signal Processing and Its Applications (ISSPA '07), February 2007, Sharjah, United Arab Emirates

    Google Scholar 

  20. 20.

    Boashash B: Time-Frequency Signal Analysis. In Advances in Spectrum Estimation and Array Processing. Edited by: Haykin S. Prentice-Hall, Englewood Cliffs, NJ, USA; 1990:418-517. chapter 9

    Google Scholar 

  21. 21.

    Srikanth T, Napper SA, Gu H: Bottom-up approach to uniform feature extraction in time and frequency domains for single lead ECG signal. International Journal of BioElectromagnetism 2002.,4(1):

  22. 22.

    Mukhopadhyay S, Ray GC: A new interpretation of nonlinear energy operator and its efficacy in spike detection. IEEE Transactions on Biomedical Engineering 1998,45(2):180-187. 10.1109/10.661266

    Article  Google Scholar 

  23. 23.

    Hassanpour H, Mesbah M: Neonatal EEG seizure detection using spike signatures in the time-frequency domain. Proceedings of the 7th International Symposium on Signal Processing and Its Applications (ISSPA '03), July 2003, Paris, France 2: 41–44.

    Google Scholar 

  24. 24.

    Theodoridis S, Koutroumbas K: Pattern Recognition. Academic Press, San Diego, Calif, USA; 2006.

    Google Scholar 

  25. 25.

    Macgillivray HL: Data Analysis: Introductory Methods in Context. Queensland University of Technology, Brisbane, Australia; 2004.

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to M. B. Malarvili.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Malarvili, M.B., Mesbah, M. & Boashash, B. Time-Frequency Analysis of Heart Rate Variability for Neonatal Seizure Detection. EURASIP J. Adv. Signal Process. 2007, 050396 (2007).

Download citation


  • Heart Rate Variability
  • Epileptic Seizure
  • Adult Human
  • Automatic Technique
  • Conditional Moment