01698nas a2200265   4500008004100000022001400041245008600055210006900141260001200210300000900222490000900231520087600240653001501116653002801131653003301159653002701192653001301219653001101232653003501243653003101278653003201309100002601341700001701367856004801384       2009                        eng d  a1557-170X00aSeizure prediction for epilepsy using a multi-stage phase synchrony based system.0 aSeizure prediction for epilepsy using a multistage phase synchro  c09/2009  a25-80 v20093 aSeizure 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.10aAlgorithms10aArtificial Intelligence10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aHumans10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity1 aJames, Christopher, J1 aGupta, Disha  uhttp://www.ncbi.nlm.nih.gov/pubmed/1996510401751nas a2200145   4500008004100000020002200041245009600063210006900159260004200228520117100270100001701441700002601458700002101484856010001505       2008                        eng d  a978-0-86341-934-800aPhase synchronization with ICA for epileptic seizure onset prediction in the long term EEG.0 aPhase synchronization with ICA for epileptic seizure onset predi  aSanta Margherita LigurebIETc07/20083 aThe apparently unpredictable nature of epileptic seizures can be devastating for people with epilepsy. Current medical interventions can help 75% of patients while 25% have to live with uncontrolled seizures. This motivates the search for a seizure prediction prototype using electroencephalograms (electrical signals that capture brain activity). The concept of phase synchrony has attracted much attention recently in the context of seizure prediction but is still in need of further study. The basis of our analysis is to track changes in synchrony in brain signals at and before seizure onset. The novel concept in our analysis is the use of unmixed signals as opposed to scalp EEG signals for phase synchrony analysis. The unmixing is performed by a Blind Source Separation technique called Independent component Analysis (ICA). ICA seeks underlying independent source signals from the EEG and it allows multivariate analysis using spatial as well as temporal information inherent to EEG signals. The present study on long-term continuous EEG data sets indicates that the concept of using phase synchronization with ICA may prove useful for predicting seizures.1 aGupta, Disha1 aJames, Christopher, J1 aGray, William, P  uhttp://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4609101&abstractAccess=no&userType=inst00795nas a2200217   4500008004100000245007400041210006900115260006800184653003100252653002800283653002600311653002800337653003500365653002300400653002400423100001500447700001700462700001700479700002600496856005500522       2007                        eng d00aFlux-continuous schemes for solving EEG source localization problems.0 aFluxcontinuous schemes for solving EEG source localization probl  aGlasgow, UKbCivil-Comp Press, Curran Associates, Inc.c07/200710acontrol volume distributed10aelectroencephalographic10afinite element method10aflux-continuous schemes10aindependent component analysis10aPoisson's equation10asource localization1 aPal, Mayur1 aGupta, Disha1 aEdwards, M G1 aJames, Christopher, J  uhttp://www.ctresources.info/ccp/paper.html?id=431201751nas a2200229   4500008004100000022001400041245012300055210006900178260001200247300001100259490000900270520101400279653001501293653002701308653001101335653003001346653001301376653004101389100001701430700002601447856004801473       2007                        eng d  a1557-170X00aNarrowband vs. broadband phase synchronization analysis applied to independent components of ictal and interictal EEG.0 aNarrowband vs broadband phase synchronization analysis applied t  c08/2007  a3864-70 v20073 aThis paper presents a comparison of the use of broadband and narrow band signals for phase synchronization analysis as applied to Independent Components of ictal and interictal scalp EEG in the context of seizure onset detection and prediction. Narrow band analysis for phase synchronization is found to be better performed in the present context than the broad band signal analysis. It has been observed that the phase synchronization of Independent Components in a narrow band (particularly the Gamma band) shows a prominent trend of increasing and decreasing synchronization at seizure onset near the epileptogenic area (spatially). This information is not always found to be consistent in analysis with the raw EEG signals, which may show spurious synchronization happening due to volume conduction effects. These observations lead us to believe that tracking changes in phase synchronization of narrow band activity, on continuous data records will be of great value in the context of seizure prediction.10aAlgorithms10aElectroencephalography10aHumans10aPredictive Value of Tests10aSeizures10aSignal Processing, Computer-Assisted1 aGupta, Disha1 aJames, Christopher, J  uhttp://www.ncbi.nlm.nih.gov/pubmed/1800284202201nas a2200289   4500008004100000022001400041245012000055210006900175260000900244300001100253490001200264520126500276653001501541653002801556653003301584653002701617653001301644653001101657653003501668653003301703653003101736653003201767100002601799700002101825700001701846856004801863       2007                        eng d  a1557-170X00aSpace-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity.0 aSpacetime ICA versus Ensemble ICA for ictal EEG analysis with co  c2007  a5473-60 v08/20073 aIn 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.10aAlgorithms10aArtificial Intelligence10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aHumans10aPattern Recognition, Automated10aPrincipal Component Analysis10aReproducibility of Results10aSensitivity and Specificity1 aJames, Christopher, J1 aAbásolo, Daniel1 aGupta, Disha  uhttp://www.ncbi.nlm.nih.gov/pubmed/1800325001706nas a2200145   4500008004100000020002200041245006000063210005800123260006100181520118300242100001701425700002601442700002101468856007101489       2006                        eng d  a978-0-86341-658-300aDe-noising epileptic EEG using ICA and phase synchrony.0 aDenoising epileptic EEG using ICA and phase synchrony  aGlasgow, ScotlandbIET, Curran Associates, Inc.c07/20063 aA multi-channel recording of scalp electroencephalogram (EEG) is a non-invasive tool important for analysis and treatment of patients with epilepsy. These recordings are usually contaminated with artifacts and background activity, which may sometimes render them misleading or useless. Epileptic EEG is also useful for seizure detection, localisation and prediction. It would be useful to de-noise epileptic EEG in order to improve the efficiency of such diagnostic and prognostic procedures. The basic method of denoising a signal is through filtering, but filtering physiological signals is not trivial and highly subjective as the information is spread over different frequency bands and different measurement channels. This paper demonstrates a system for objectively de-noising epileptic EEG using Independent Component Analysis (ICA). In the standard implementation of ICA it is generally required to subjectively choose independent components (ICs) relevant to the epileptic activity; here we automate this process through the concept of phase synchronisation between ICs. In this manner de-noising the epileptic EEG with ICA becomes an objective (and automated) process.1 aGupta, Disha1 aJames, Christopher, J1 aGray, William, P  uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=422523502816nas a2200133   4500008004000000245009700040210006900137260004700206490000800253520226700261100001702528700002602545856011102571       0                           engd00aAdvances in Epileptic Seizure Onset Prediction in the EEG with ICA and Phase Synchronization0 aAdvances in Epileptic Seizure Onset Prediction in the EEG with I  aSouthampton, UKbUniversity of Southampton0 vPhD3 aSeizure onset prediction in epilepsy is a challenge which is under investigation using many and varied signal processing techniques, across the world. This research thesis contributes to the advancement of digital signal analysis of neurophysiological signals of epileptic patients. It has been studied especially in the context of epileptic seizure onset prediction, with a motivation to help epileptic patients by advancing the knowledge on the possibilities of seizure prediction and inching towards a clinically viable seizure predictor. In this work, a synchrony based multi-stage system is analyzed that brings to bear the advantages of many techniques in each substage. The 1st stage of the system unmixes and de-noises continuous long-term (2-4 days) multichannel scalp Electroencephalograms using spatially constrained Independent Component Analysis. The 2d stage estimates the long term significant phase synchrony dynamics of narrowband (2-8 Hz and 8-14 Hz) seizure components. The synchrony dynamics are assessed with a novel statistic, the PLV-d, analyzing the joint synchrony in two frequency bands of interest. The 3rd stage creates multidimensional features of these synchrony dynamics for two classes (‘seizure free’ and ‘seizure predictive’) which are then projected onto a 2-dimensional map using a supervised Neuroscale, a topographic projection scheme based on a Radial Basis Neural Network. The 4th stage evaluates the probability of occurrence of predictive events using Gaussian Mixture Models used in supervised and semi-supervised forms. Preliminary analysis is performed on shorter data segments and the final system is based on nine patient’s long term (2-4 days each) continuous data. The training and testing for feature extraction analysis is performed on five patient datasets. The features extracted and the parameters ascertained with this analysis are then applied on the remaining four long-term datasets as a test of performance. The analysis is tested against random predictors as well. We show the possibility of seizure onset prediction (performing better than a random predictor) within a prediction window of 35-65 minutes with a sensitivity of 65-100% and specificity of 60-100% across the epileptic patients.1 aGupta, Disha1 aJames, Christopher, J  uhttps://www.neurotechcenter.org/publications/advances-epileptic-seizure-onset-prediction-eeg-ica-and-phase