Model-Based Seizure Detection for Intracranial EEG Recordings

This paper presents a novel model-based patient-specific method for automatic detection of seizures in the intracranial EEG recordings. The proposed method overcomes the complexities in the practical implementation of the patient-specific approach of seizure detection. The method builds a seizure model (set of basis functions) for a priori known seizure (the template seizure pattern), and uses the statistically optimal null filters as a building block for the detection of similar seizures. The process of modeling the template seizure is fully automatic. Overall, the detection method involves the segmentation of the template seizure pattern, rejection of the redundant and noisy segments, extraction of features from the segments to generate a set of models, selection of the best seizure model, and training of the classifier. The trained classifier is used to detect similar seizures in the remaining data. The resulting seizure detection method was evaluated on a total of 304 h of single-channel depth EEG recordings from 14 patients. The system performance is further compared to the Qu-Gotman patient-specific system using the same data. A significant improvement in the proposed system, in terms of specificity, is observed over the compared method.

[1]  Richard J. Davidson,et al.  Electromyogenic artifacts and electroencephalographic inferences revisited , 2011, NeuroImage.

[2]  M.N.S. Swamy,et al.  Statistically optimal null filters for processing short record length signals , 1995, Proceedings of ISCAS'95 - International Symposium on Circuits and Systems.

[3]  Shekhar Saxena,et al.  Epilepsy Care in the World: Results of an ILAE/IBE/WHO Global Campaign Against Epilepsy Survey , 2006, Epilepsia.

[4]  J. Gotman,et al.  A system to detect the onset of epileptic seizures in scalp EEG , 2005, Clinical Neurophysiology.

[5]  M. N. Shanmukha Swamy,et al.  Statistically optimal null filters: Kalman equivalence , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[6]  J. S. Barlow,et al.  Methods of Analysis of Nonstationary EEGs, with Emphasis on Segmentation Techniques: A Comparative Review , 1985, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[7]  Scott B. Wilson,et al.  Seizure detection: correlation of human experts , 2003, Clinical Neurophysiology.

[8]  I. Gath,et al.  Segmentation of EEG during sleep using time-varying autoregressive modeling , 2006, Biological Cybernetics.

[9]  A. Aertsen,et al.  Detecting Epileptic Seizures in Long-term Human EEG: A New Approach to Automatic Online and Real-Time Detection and Classification of Polymorphic Seizure Patterns , 2008, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[10]  J. Gotman,et al.  An automatic warning system for epileptic seizures recorded on intracerebral EEGs , 2005, Clinical Neurophysiology.

[11]  J. Gotman,et al.  A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: possible use as a warning device , 1997, IEEE Transactions on Biomedical Engineering.

[12]  Scott B. Wilson,et al.  Seizure detection: evaluation of the Reveal algorithm , 2004, Clinical Neurophysiology.

[13]  Timothy A. Pedley,et al.  Epilepsy : a comprehensive textbook , 2008 .

[14]  J Gotman,et al.  Automatic EEG analysis during long-term monitoring in the ICU. , 1998, Electroencephalography and clinical neurophysiology.

[15]  F. Wendling,et al.  Time-frequency matching of warped depth-EEG seizure observations , 1999, IEEE Transactions on Biomedical Engineering.

[16]  Ali H. Shoeb,et al.  Patient-specific seizure onset detection , 2004, Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  J. Gotman,et al.  A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification , 2006, Clinical Neurophysiology.

[18]  M.N.S. Swamy,et al.  Model-based seizure detection method using statistically optimal null filters , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Fabrice Wendling,et al.  A Physiologically Plausible Spatio-Temporal Model for EEG Signals Recorded With Intracerebral Electrodes in Human Partial Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.

[20]  M.N.S. Swamy,et al.  Recursive implementation of statistically-optimal null filters , 1996, 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96.

[21]  Reza Tafreshi,et al.  Automated Real-Time Epileptic Seizure Detection in Scalp EEG Recordings Using an Algorithm Based on Wavelet Packet Transform , 2010, IEEE Transactions on Biomedical Engineering.

[22]  Ivan Osorio,et al.  Strategies for adapting automated seizure detection algorithms. , 2007, Medical engineering & physics.

[23]  J Gotman,et al.  Automatic detection of seizures and spikes. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[24]  Rajeev Agarwal,et al.  Model-based seizure detection method using statistically optimal filters. , 2004, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference.

[25]  F. Wendling,et al.  Extraction of spatio-temporal signatures from depth EEG seizure signals based on objective matching in warped vectorial observations , 1996, IEEE Transactions on Biomedical Engineering.

[26]  M. Swamy,et al.  Statistically optimal null filter based on instantaneous matched processing , 2001 .

[27]  M.N.S. Swamy,et al.  STFT-Based Segmentation in Model-Based Seizure Detection , 2007, 2007 Canadian Conference on Electrical and Computer Engineering.

[28]  F Wendling,et al.  A method to quantify invariant information in depth-recorded epileptic seizures. , 1997, Electroencephalography and clinical neurophysiology.

[29]  A Värri,et al.  Evaluation of a computerized system for recognition of epileptic activity during long-term EEG recording. , 1994, Electroencephalography and clinical neurophysiology.

[30]  J. Frost,et al.  A Multistage System for the Automated Detection of Epileptic Seizures in Neonatal Electroencephalography , 2009, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[31]  R. Harner,et al.  Patient-Specific Early Seizure Detection From Scalp Electroencephalogram , 2010, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[32]  Scott B. Wilson.  A neural network method for automatic and incremental learning applied to patient-dependent seizure detection , 2005, Clinical Neurophysiology.

[33]  T. Ferryman,et al.  Data outlier detection using the Chebyshev theorem , 2005, 2005 IEEE Aerospace Conference.

[34]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[35]  Rajeev Yadav,et al.  A new improved model-based seizure detection using statistically optimal null filter , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.