Time-Frequency Feature Extraction of Newborn EEG Seizure Using SVD-Based Techniques

The nonstationary and multicomponent nature of newborn EEG seizures tends to increase the complexity of the seizure detection problem. In dealing with this type of problems, time-frequency-based techniques were shown to outperform classical techniques. This paper presents a new time-frequency-based EEG seizure detection technique. The technique uses an estimate of the distribution function of the singular vectors associated with the time-frequency distribution of an EEG epoch to characterise the patterns embedded in the signal. The estimated distribution functions related to seizure and nonseizure epochs were used to train a neural network to discriminate between seizure and nonseizure patterns.

[1]  Paul B. Colditz,et al.  Nonlinear nonstationary Wiener model of infant EEG seizures , 2002, IEEE Transactions on Biomedical Engineering.

[2]  J. Aravena,et al.  Nonstationary signal classification using pseudo power signatures: The matrix SVD approach , 1999 .

[3]  Zhiyue Lin,et al.  An introduction to time‐frequency signal analysis , 1997 .

[4]  William J. Williams,et al.  Reduced Interference Time-Frequency Distributions , 1992 .

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

[6]  S. Partap,et al.  NEUROLOGIC DISORDERS , 1992, Clinical Signs in Small Animal Medicine.

[7]  Eli M. Mizrahi,et al.  Diagnosis and Management of Neonatal Seizures , 1998 .

[8]  Mostefa Mesbah,et al.  Neonatal EEG seizure detection using spike signatures in the time-frequency domain , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

[9]  B Boashash,et al.  A time-frequency approach for newborn seizure detection. , 2001, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[10]  Jun Ni,et al.  Non-stationary signal analysis and transient machining process condition monitoring , 2002 .

[11]  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).

[12]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  L. Debnath.  Wavelet Transforms and Time-Frequency Signal Analysis , 2001 .

[14]  Jean Gotman,et al.  Automatic seizure detection in newborns and infants , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[15]  Boualem Boashash,et al.  Methods of signal classification using the images produced by the Wigner-Ville distribution , 1991, Pattern Recognit. Lett..

[16]  John W. Auer,et al.  Linear algebra with applications , 1996 .

[17]  Mostefa Mesbah,et al.  Time-frequency extraction of EEG spike events for seizure detection in neonate , 2001, Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467).

[18]  Miguel Pinzolas,et al.  Neighborhood based Levenberg-Marquardt algorithm for neural network training , 2002, IEEE Trans. Neural Networks.

[19]  Dale Groutage,et al.  Feature sets for nonstationary signals derived from moments of the singular value decomposition of Cohen-Posch (positive time-frequency) distributions , 2000, IEEE Trans. Signal Process..

[20]  Paul B. Colditz,et al.  A computer-aided detection of EEG seizures in infants: a singular-spectrum approach and performance comparison , 2002, IEEE Transactions on Biomedical Engineering.

[21]  Braham Barkat,et al.  A high-resolution quadratic time-frequency distribution for multicomponent signals analysis , 2001, IEEE Trans. Signal Process..

[22]  N. M. Marinovic,et al.  Feature Extraction And Pattern Classification In Space - Spatial Frequency Domain , 1985, Other Conferences.

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

[24]  D. Bennink,et al.  A new matrix decomposition based on optimum transformation of the singular value decomposition basis sets yields principal features of time-frequency distributions , 2000, Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No.00TH8496).

[25]  Dionisio Bernal.  DAMAGE LOCALIZATION USING LOAD VECTORS , 2001 .