Discrete wavelet transform based seizure detection in newborns EEG signals

This paper proposes a novel method for detecting newborns seizure events from electroencephalogram (EEG) data. The detection scheme is based on the discrete wavelet transform (DWT) of the EEG signals. The number of zero-crossings, the average distance between adjacent zero-crossings, the number of extrema, and the average distance between adjacent extrema of the wavelet coefficients (WCs) of certain scales are extracted to form a feature set. The extracted feature set is then fed to an artificial neural network (ANN) classifier to organize the EEG signals into seizure and non- seizure activities. In this study, the training and test sets were obtained from EEG data acquired from 1 and 5 other neonates, respectively, with ages ranging from 2 days to 2 weeks. The obtained results show that on the average 95% of the EEG seizures were detected by the proposed scheme.

[1]  Metin Akay,et al.  Detection and Estimation Methods for Biomedical Signals , 1996 .

[2]  Mostefa Mesbah,et al.  Detection of seizures in newborns using time-frequency analysis of EEG signals , 2000, Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No.00TH8496).

[3]  J. Gotman,et al.  Automatic seizure detection in the newborn: methods and initial evaluation. , 1997, Electroencephalography and clinical neurophysiology.

[4]  S. V. Mehta,et al.  Wavelet analysis as a potential tool for seizure detection , 1994, Proceedings of IEEE-SP International Symposium on Time- Frequency and Time-Scale Analysis.

[5]  Steven J. Schiff,et al.  Wavelet transforms for epileptic spike and seizure detection , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Y. H. Lee,et al.  Detection of epileptiform activity using wavelet and neural network , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[7]  Mostefa Mesbah,et al.  Detection of newborn EEG seizure using optimal features based on discrete wavelet transform , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[8]  A. Liu,et al.  Detection of neonatal seizures through computerized EEG analysis. , 1992, Electroencephalography and clinical neurophysiology.

[9]  Nitish V. Thakor,et al.  Biomedical problems in time-frequency-scale analysis-new challenges , 1994, Proceedings of IEEE-SP International Symposium on Time- Frequency and Time-Scale Analysis.

[10]  B. Onaral,et al.  On-line neonatal seizure detection based on multi-scale analysis of EEG using wavelets as a tool , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[11]  Li Yong,et al.  Apply wavelet transform to analyse EEG signal , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.