ABSTRACT
High-frequency oscillations (HFOs) are a reliable indicator for the epileptic seizure onset zone (SOZ) in ECoG recordings. We propose a novel method for the automatic detection of ictal HFOs in the ripple band (80-250 Hz) based on CFAR matched sub-space filtering. This allows to track the early propagation of ictal HFOs, revealing initial and follow-up epileptic activity on the electrodes. We apply this methodology to two seizures from one patient suffering from focal epilepsy. The electrodes identified are in very good accordance with the visual HFO analysis by clinicians. Furthermore the electrodes with initial HFO activity are correlated well with the SOZ (conventional v-activity).
Subject(s)
Electroencephalography/methods , Epilepsies, Partial/diagnosis , Signal Processing, Computer-Assisted , Adult , Algorithms , Automation , Electrodes , Electroencephalography/instrumentation , Humans , MaleABSTRACT
Granger causality is a useful concept for studying causal relations in networks. However, numerical problems occur when applying the corresponding methodology to high-dimensional time series showing co-movement, e.g. EEG recordings or economic data. In order to deal with these shortcomings, we propose a novel method for the causal analysis of such multivariate time series based on Granger causality and factor models. We present the theoretical background, successfully assess our methodology with the help of simulated data and show a potential application in EEG analysis of epileptic seizures.
Subject(s)
Brain/physiopathology , Electroencephalography/statistics & numerical data , Epilepsies, Partial/physiopathology , Adult , Brain/ultrastructure , Causality , Forecasting , Humans , Magnetic Resonance Imaging , Male , Models, Neurological , Multivariate Analysis , Principal Component Analysis , Stochastic Processes , Time FactorsABSTRACT
In this paper we propose a novel segmentation method based on the relative frequency contributions of ictal multichannel ECoG data. Segments with predominant [[see text]]-activity are classified as epileptic. The seizure onset zone is determined by the temporal delay of the epileptic [[see text]]-activity (4-9Hz) on the different channels. We apply this methodology to three seizures of one patient suffering from focal epilepsy. The resulting segments reflect the visual characteristics of the ictal ECoG well. The seizure onset zone identified by the proposed method is in very good accordance with the clinical findings.
Subject(s)
Electroencephalography/methods , Epilepsy/physiopathology , Humans , Signal Processing, Computer-AssistedABSTRACT
In this paper we assess a dependency measure for multivariate time series termed Extrinsic-to-Intrinsic-Power-Ratio (EIPR) using two different signal models. In a comparison with Partial Directed Coherence (PDC) we show that both measures correctly identify imposed couplings, but that limitations of the PDC do not affect EIPR. Moreover, EIPR is successfully used for the localization of the seizure onset zone in ECoG recordings from two epilepsy patients, given the exact seizure onset time. The electrodes identified by the proposed method are in excellent accordance with the clinical findings.
