Sourya Bhattacharyya, Jayanta Mukhopadhyay, Arun Kumar Majumdar, Bandana Majumdar, Arun Kumar Singh, Chanchal Saha


Presence of burst suppression pattern in neonate EEG is a sign of epilepsy. Detection of burst patterns is normally done by visual inspection of recorded raw EEG or amplitude integrated EEG signal. Existing automatic burst detection approaches consist of either supervised learning mechanism or static energy threshold based comparison. Both approaches can produce inconsistent results for babies with different ages (for example, a neonate EEG and a six month old baby EEG). That is because, EEG signal amplitude or energy increases according to baby’s age. Training based classifiers or static thresholds cannot adapt with this amplitude variation. Here we propose an automatic burst detection method, which first computes signal parameters such as energy, variance and power spectral density. From generated signal data, so called low level amplitude or energy output is used as a ground reference for indication of signal suppression level. Burst is identified according to high deviation of parameter values from those in suppression pattern. It does not need any static threshold based comparison. Results show that our algorithm exhibits greater sensitivity and equal specificity than existing methods. Due to adaptive thresholding for burst detection, our method is applicable for analyzing EEG signals of babies with different ages.


  1. Connell, J. & Oozeer, R. & De Vries, L. S. & Dubowitz, L. M. S. & Dubowitz, V. (1989). “Continuous EEG monitoring of neonatal seizures: diagnostic and prognostic considerations”, in Archives of Disease in Childhood, 1989, 64, pp. 452-458.
  2. Greene, B. R. & Faul, S. & Marnane, W. P. & Lightbody, G. & Korotchikova, I. & Boylan, G. B. (2008). A comparison of quantitative EEG features for neonatal seizure detection. in J Clin Neurophysiol 2008, doi:10.1016/j.clinph.2008.02.001
  3. Hellström-Westas, L. & De Vries, L. S. & Rosén, I. (2008). Atlas of Amplitude Integrated EEGs in the newborn, informa healthcare. UK, 2nd edition, ISBN13: 978 1 84184 649 1
  4. Löfhede, J. & Löfgren, N. & Thordstein, M. & Flisberg, A. & Kjellmer, I. & Lindecrantz, K. (2008). Classification of burst and suppression in the neonatal electroencephalogram. in J Neural Eng 2008;5:402- 10.
  5. Löfhede, J. & Thordstein, M. & Löfgren, N. & Flisberg, A. & Rosa-Zurera, M. & Kjellmer, I. & Lindecrantz, K. (2010). Automatic classification of background EEG activity in healthy and sick neonates. in J. Neural Eng. 7 (2010) 016007
  6. Maynard, D. E. & Prior, P. F. & Scott, D. F. (1969), Device for continuous monitoring of cerebral activity in resuscitated patients. in Br Med J 1969;4:545-6.
  7. Palmu, K. et al. (2010) Detection of 'EEG bursts' in the early preterm EEG: Visual vs. automated detection. in J Clin Neurophysiol 2010, doi:10.1016/j.clinph .2010.02.010
  8. Prior, P. F. & Maynard, D. E. & Sheaff, P. et al. (1971). Monitoring cerebral function: Clinical experience with new device of continuous recording of electrical activity of brain. in Br Med J 1971;2:736-8.
  9. Rennie, J. M. & Hagmann, C. F. & Robertson, N. J. (2008). Neonatal Cerebral Investigation, Cambridge University Press. New York, 1st edition, ISBN-13 978-0-511-41368-1
  10. Sanei, S. & Chambers, J. A. (2007). EEG Signal Processing, John Wiley & Sons. England, ISBN-13 978-0-470-02581-9
  11. Särkelä, M. & Mustola, S. & Seppänen, T. & Koskinen, M. & Lepola, P. & Suominen, K. & Juvonen, T. & Tolvanen-Laakso, H. & Jäntti, V. (2002). Automatic Analysis and Monitoring of Burst Suppression in Anesthesia. in J Clin Monit Comput 2002;17:125-34.
  12. Wang, Y. & Agarwal, R. (2007). Automatic Detection of Burst Suppression. in IEEE EMBS INTL Conf Aug 2007.
  13. Welch, P. D. (1967). The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms. in IEEE Transactions on Audio Electroacoustics, Volume AU-15 (1967), pages 70-73.

Paper Citation

in Harvard Style

Bhattacharyya S., Mukhopadhyay J., Majumdar A., Majumdar B., Singh A. and Saha C. (2011). AUTOMATED BURST DETECTION IN NEONATAL EEG . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011) ISBN 978-989-8425-35-5, pages 15-21. DOI: 10.5220/0003123900150021

in Bibtex Style

author={Sourya Bhattacharyya and Jayanta Mukhopadhyay and Arun Kumar Majumdar and Bandana Majumdar and Arun Kumar Singh and Chanchal Saha},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)},

in EndNote Style

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)
SN - 978-989-8425-35-5
AU - Bhattacharyya S.
AU - Mukhopadhyay J.
AU - Majumdar A.
AU - Majumdar B.
AU - Singh A.
AU - Saha C.
PY - 2011
SP - 15
EP - 21
DO - 10.5220/0003123900150021