A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis

Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 ± 22% at a specificity of 86 ± 7% (mean ± SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.

[1]  J. R. Hughes.  EEG in Clinical Practice , 1982 .

[2]  Tzyy-Ping Jung,et al.  Imaging brain dynamics using independent component analysis , 2001, Proc. IEEE.

[3]  J R Ives,et al.  Automatic recognition of inter-ictal epileptic activity in prolonged EEG recordings. , 1979, Electroencephalography and clinical neurophysiology.

[4]  Ricardo Nuno Vig.  Extraction of' ocular artefacts from EEG using independent component analysis , 1997 .

[5]  Dinh Tuan Pham,et al.  Separation of a mixture of independent sources through a maximum likelihood approach , 1992 .

[6]  V D Calhoun,et al.  Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms , 2001, Human brain mapping.

[7]  J. Gotman,et al.  Systematic source estimation of spikes by a combination of independent component analysis and RAP-MUSIC I: Principles and simulation study , 2002, Clinical Neurophysiology.

[8]  Bruce J. West,et al.  Wavelet analysis of epileptic spikes. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Saeid Sanei,et al.  Removal of eye blinking artifact from the electro-encephalogram, incorporating a new constrained blind source separation algorithm , 2005, Medical and Biological Engineering and Computing.

[10]  Aapo Hyvärinen,et al.  Survey on Independent Component Analysis , 1999 .

[11]  O Ozdamar,et al.  Detection of spikes with artificial neural networks using raw EEG. , 1998, Computers and biomedical research, an international journal.

[12]  Václav Hlavác,et al.  Ten Lectures on Statistical and Structural Pattern Recognition , 2002, Computational Imaging and Vision.

[13]  Peter Dayan,et al.  The Classification of Spikes in EEG Recordings using Features Derived from ICA , 2006 .

[14]  Nurettin Acir,et al.  Automatic spike detection in EEG by a two-stage procedure based on support vector machines , 2004, Comput. Biol. Medicine.

[15]  Scott B. Wilson,et al.  Spike detection: a review and comparison of algorithms , 2002, Clinical Neurophysiology.

[16]  C Faure,et al.  Attributed strings for recognition of epileptic transients in EEG. , 1985, International journal of bio-medical computing.

[17]  S Makeig,et al.  Spatially independent activity patterns in functional MRI data during the stroop color-naming task. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[18]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[19]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[20]  P J Bones,et al.  Wavelet Analysis of Transient Biomedical Signals and its Application to Detection of Epileptiform Activity in the EEG , 2000, Clinical EEG.

[21]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[22]  Ronald G. Emerson,et al.  Spike detection II: automatic, perception-based detection and clustering , 1999, Clinical Neurophysiology.

[23]  J. Gotman,et al.  Isolation of epileptiform discharges from unaveraged EEG by independent component analysis , 1999, Clinical Neurophysiology.

[24]  J. Gotman,et al.  Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG. , 1976, Electroencephalography and clinical neurophysiology.

[25]  Luis Diambra,et al.  Nonlinear models for detecting epileptic spikes , 1999 .

[26]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[27]  W R Webber,et al.  Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: a comparison of raw and parameterized EEG data. , 1994, Electroencephalography and clinical neurophysiology.

[28]  Piotr J. Franaszczuk,et al.  An autoregressive method for the measurement of synchronization of interictal and ictal EEG signals , 1999, Biological Cybernetics.

[29]  C D Binnie,et al.  EEG in Clinical Practice, 2nd edn , 1995 .

[30]  Erkki Oja,et al.  Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings , 1997, NIPS.

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

[32]  P. Rossini,et al.  Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals , 2004, Clinical Neurophysiology.

[33]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[34]  O. Ozdamar,et al.  Wavelet preprocessing for automated neural network detection of EEG spikes , 1995 .

[35]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[36]  P Y Ktonas,et al.  An automated system for epileptogenic focus localization in the electroencephalogram. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[37]  P. Rappelsberger.  EEG informatics. A didactic review of methods and applications of EEG data processing , 1978 .

[38]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[39]  R H Bayford,et al.  Using the GRID to improve the computation speed of electrical impedance tomography (EIT) reconstruction algorithms. , 2005, Physiological measurement.

[40]  Richard H. Bayford,et al.  Applications of GRID in Clinical Neurophysiology and Electrical Impedance Tomography of Brain Function , 2005, HealthGrid.

[41]  S. Baillet,et al.  Automated interictal spike detection and source localization in magnetoencephalography using independent components analysis and spatio-temporal clustering , 2004, Clinical Neurophysiology.

[42]  T J Sejnowski,et al.  Learning the higher-order structure of a natural sound. , 1996, Network.

[43]  Jean Gotman,et al.  Systematic source estimation of spikes by a combination of independent component analysis and RAP-MUSIC II: Preliminary clinical application , 2002, Clinical Neurophysiology.