TY - JOUR T1 - Seizure prediction for epilepsy using a multi-stage phase synchrony based system. JF - Conf Proc IEEE Eng Med Biol Soc Y1 - 2009 A1 - Christopher J James A1 - Disha Gupta KW - Algorithms KW - Artificial Intelligence KW - Diagnosis, Computer-Assisted KW - Electroencephalography KW - Epilepsy KW - Humans KW - Pattern Recognition, Automated KW - Reproducibility of Results KW - Sensitivity and Specificity AB - Seizure onset prediction in epilepsy is a challenge which is under investigation using many and varied signal processing techniques. Here we present a multi-stage phase synchrony based system that brings to bear the advantages of many techniques in each substage. The 1(st) stage of the system unmixes continuous long-term (2-4 days) multichannel scalp EEG using spatially constrained Independent Component Analysis and estimates the long term significant phase synchrony dynamics of narrowband (2-8 Hz and 8-14 Hz) seizure components. It then projects multidimensional features onto a 2-D map using Neuroscale and evaluates the probability of predictive events using Gaussian Mixture Models. We show the possibility of seizure onset prediction within a prediction window of 35-65 minutes with a sensitivity of 65-100% and specificity of 65-80% across epileptic patients. VL - 2009 UR - http://www.ncbi.nlm.nih.gov/pubmed/19965104 ER - TY - JOUR T1 - Space-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity. JF - Conf Proc IEEE Eng Med Biol Soc Y1 - 2007 A1 - Christopher J James A1 - Abásolo, Daniel A1 - Disha Gupta KW - Algorithms KW - Artificial Intelligence KW - Diagnosis, Computer-Assisted KW - Electroencephalography KW - Epilepsy KW - Humans KW - Pattern Recognition, Automated KW - Principal Component Analysis KW - Reproducibility of Results KW - Sensitivity and Specificity AB - In this proof-of-principle study we analyzed intracranial electroencephalogram recordings in patients with intractable focal epilepsy. We contrast two implementations of Independent Component Analysis (ICA) - Ensemble (or spatial) ICA (E-ICA) and Space-Time ICA (ST-ICA) in separating out the ictal components underlying the measurements. In each case we assess the outputs of the ICA algorithms by means of a non-linear method known as the Lempel-Ziv (LZ) complexity. LZ complexity quantifies the complexity of a time series and is well suited to the analysis of non-stationary biomedical signals of short length. Our results show that for small numbers of intracranial recordings, standard E-ICA results in marginal improvements in the separation as measured by the LZ complexity changes. ST-ICA using just 2 recording channels both near and far from the epileptic focus result in more distinct ictal components--although at this stage there is a subjective element to the separation process for ST-ICA. Our results are promising showing that it is possible to extract meaningful information from just 2 recording electrodes through ST-ICA, even if they are not directly over the seizure focus. This work is being further expanded for seizure onset analysis. VL - 08/2007 UR - http://www.ncbi.nlm.nih.gov/pubmed/18003250 ER -