Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset.

OBJECTIVE: To develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity. METHODS: Our algorithm was tested on intracranial EEG from epilepsy patients admitted to the EMU for presurgical evaluation. Our framework uses a one-class Support Vector Machine (SVM) that is being trained dynamically according to past activity in all available channels to classify the novelty of the current activity. In this study we compared multiple configurations using a one-class SVM to assess if there is significance over specific neural features or electrode locations. RESULTS: Our results show that the algorithm reaches a sensitivity of 87% for early-onset seizure detection and of 97.7% as a generic seizure detection. CONCLUSIONS: Our algorithm is capable of running in real-time and achieving a high performance for early seizure-onset detection with a low false positive rate and robustness in detection of different type of seizure-onset patterns. SIGNIFICANCE: This algorithm offers a solution to warning systems in the EMU as well as a tool for seizure characterization during post-hoc analysis of intracranial EEG data for surgical resection of the epileptogenic network.

Identification of seizure onset zone and preictal state based on characteristics of high frequency oscillations.

OBJECTIVE: We investigate the relevance of high frequency oscillations (HFO) for biomarkers of epileptogenic tissue and indicators of preictal state before complex partial seizures in humans. METHODS: We introduce a novel automated HFO detection method based on the amplitude and features of the HFO events. We examined intracranial recordings from 33 patients and compared HFO rates and characteristics between channels within and outside the seizure onset zone (SOZ). We analyzed changes of HFO activity from interictal to preictal and to ictal periods. RESULTS: The average HFO rate is higher for SOZ channels compared to non-SOZ channels during all periods. Amplitudes and durations of HFO are higher for events within the SOZ in all periods compared to non-SOZ events, while their frequency is lower. All analyzed HFO features increase for the ictal period. CONCLUSIONS: HFO may occur in all channels but their rate is significantly higher within SOZ and HFO characteristics differ from HFO outside the SOZ, but the effect size of difference is small. SIGNIFICANCE: The present results show that based on accumulated dataset it is possible to distinguish HFO features different for SOZ and non-SOZ channels, and to show changes in HFO characteristics during the transition from interictal to preictal and to ictal periods.

Network dynamics of the brain and influence of the epileptic seizure onset zone.

The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.

Computing network-based features from intracranial EEG time series data: Application to seizure focus localization.

The surgical resection of the epileptogenic zone (EZ) is the only effective treatment for many drug-resistant epilepsy (DRE) patients, but the pre-surgical identification of the EZ is challenging. This study investigates whether the EZ exhibits a computationally identifiable signature during seizures. In particular, we compute statistics of the brain network from intracranial EEG (iEEG) recordings and track the evolution of network connectivity before, during, and after seizures. We define each node in the network as an electrode and weight each edge connecting a pair of nodes by the gamma band cross power of the corresponding iEEG signals. The eigenvector centrality (EVC) of each node is tracked over two seizures per patient and the electrodes are ranked according to the corresponding EVC value. We hypothesize that electrodes covering the EZ have a signature EVC rank evolution during seizure that differs from electrodes outside the EZ. We tested this hypothesis on multi-channel iEEG recordings from 2 DRE patients who had successful surgery (i.e., seizures were under control with or without medications) and 1 patient who had unsuccessful surgery. In the successful cases, we assumed that the resected region contained the EZ and found that the EVC rank evolution of the electrodes within the resected region had a distinct "arc" signature, i.e., the EZ ranks first rose together shortly after seizure onset and then fell later during seizure.

Characterization of early partial seizure onset: frequency, complexity and entropy.

OBJECTIVE: A clear classification of partial seizures onset features is not yet established. Complexity and entropy have been very widely used to describe dynamical systems, but a systematic evaluation of these measures to characterize partial seizures has never been performed. METHODS: Eighteen different measures including power in frequency bands up to 300 Hz, Gabor atom density (GAD), Higuchi fractal dimension (HFD), Lempel-Ziv complexity, Shannon entropy, sample entropy, and permutation entropy, were selected to test sensitivity to partial seizure onset. Intracranial recordings from 45 patients with mesial temporal, neocortical temporal and neocortical extratemporal seizure foci were included (331 partial seizures). RESULTS: GAD, Lempel-Ziv complexity, HFD, high frequency activity, and sample entropy were the most reliable measures to assess early seizure onset. CONCLUSIONS: Increases in complexity and occurrence of high-frequency components appear to be commonly associated with early stages of partial seizure evolution from all regions. The type of measure (frequency-based, complexity or entropy) does not predict the efficiency of the method to detect seizure onset. SIGNIFICANCE: Differences between measures such as GAD and HFD highlight the multimodal nature of partial seizure onsets. Improved methods for early seizure detection may be achieved from a better understanding of these underlying dynamics.

Improving early seizure detection.

Over the last decade, the search for a method able to reliably predict seizures hours in advance has been largely replaced by the more realistic goal of very early detection of seizure onset, which would allow therapeutic or warning devices to be triggered prior to the onset of disabling clinical symptoms. We explore in this article the steps along the pathway from data acquisition to closed-loop applications that can and should be considered to design the most efficient early seizure detection. Microelectrodes, high-frequency oscillations, high sampling rate, high-density arrays, and modern analysis techniques are all elements of the recording and detection process that in combination with modeling studies can provide new insights into the dynamics of seizure onsets. Each of these steps needs to be considered if detection devices that will favorably impact the quality of life of patients are to be implemented. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Controversies in epilepsy: debates held during the Fourth International Workshop on Seizure Prediction.

Debates on six controversial topics were held during the Fourth International Workshop on Seizure Prediction (IWSP4) convened in Kansas City, KS, USA, July 4-7, 2009. The topics were (1) Ictogenesis: Focus versus Network? (2) Spikes and Seizures: Step-relatives or Siblings? (3) Ictogenesis: A Result of Hyposynchrony? (4) Can Focal Seizures Be Caused by Excessive Inhibition? (5) Do High-Frequency Oscillations Provide Relevant Independent Information? (6) Phase Synchronization: Is It Worthwhile as Measured? This article, written by the IWSP4 organizing committee and the debaters, summarizes the arguments presented during the debates.

Partial seizures are associated with early increases in signal complexity.

OBJECTIVES: Partial seizures are often believed to be associated with EEG signals of low complexity because seizures are associated with increased neural network synchrony. The investigations reported here provide an assessment of the signal complexity of epileptic seizure onsets using newly developed quantitative measures. METHODS: Using the Gabor atom density (GAD) measure of signal complexity, 339 partial seizures in 45 patients with intracranial electrode arrays were analyzed. Segmentation procedures were applied to determine the timing and amplitude of GAD changes relative to the electrographic onset of the seizure. RESULTS: Three hundred and thirty out of 339 seizures have significant complexity level changes, with 319 (97%) having an increase in complexity. GAD increases occur within seconds of the onset of the partial seizure but are not observed in channels remote from the focus. The complexity increase is similar for seizures from mesial temporal origin, neocortical temporal and extra-temporal origin. CONCLUSIONS: Partial onset seizures are associated with early increases in signal complexity as measured by GAD. This increase is independent of the location of the seizure focus. SIGNIFICANCE: Despite the often predominant rhythmic activity that characterizes onset and early evolution of epileptic seizures, partial seizure onset is associated with an early increase in complexity. These changes are common to partial seizures originating from different brain regions, indicating a similar seizure dynamic.

Analysis of dynamics and propagation of parietal cingulate seizures with secondary mesial temporal involvement.

Cingulate-onset seizures, particularly those originating from parietal cingulate regions, are inadequately described and confounded by patterns of propagation. We analyzed scalp and depth electrode recordings in a patient whose seizures originated from a lesion in the right posterior cingulate region and produced secondary seizure activity in ipsilateral mesial temporal structures. Analyses included the matching pursuit (MP) method of time-frequency decomposition and the Gabor atom density (GAD) measure of signal complexity. Although scalp recordings suggested a right temporal onset, seizures recorded with depth electrodes clearly began in the parietal cingulate region before producing a secondary discharge in ipsilateral mesial structures. GAD revealed a significant increase in complexity during ictal cingulate activity and a consistent pattern of subsequent complexity changes in the hippocampus 30 seconds later. MP and GAD measures were valuable supplements to confirm the stereotyped pattern of both time-frequency changes and complexity. This provides additional evidence for pathways between the parietal cingulate region and mesial temporal structures and raises questions as to whether parietal cingulate seizures can produce clinical symptoms independent of regional or remote propagation.

Duration of complex partial seizures: an intracranial EEG study.

PURPOSE: The dynamics of partial seizures originating from neocortical and mesial temporal regions are thought to differ, yet there are no quantitative comparative studies. The studies reported here investigate the duration of complex partial seizures in these populations using analyses of seizures recorded from intracranial arrays. METHODS: Data were collected from patients undergoing presurgical evaluation with intracranial electrodes. Seizure duration was defined as the time of earliest sustained ictal activity until the termination either in all electrodes (global duration, GD), or at the onset area (focal duration, FD). Patients were divided into three groups: mesial temporal lobe epilepsy (MTLE), neocortical temporal lobe epilepsy (NCTLE), and neocortical extratemporal lobe epilepsy (NCXTLE). RESULTS: Complex partial seizure durations were significantly longer in the MTLE group compared to the NCXTLE group. Median GD for MTLE was 106 s, and for NCXTLE was 78 s. There were no significant differences between seizure durations when comparing MTLE group to NCTLE group, or comparing NCTLE group to NCXTLE group. In the MTLE group, patients with bilateral recording arrays had significantly longer median seizure durations (GD and FD) than those sampled with unilateral arrays. CONCLUSIONS: In this select group of patients there is a significant difference between the duration of complex partial seizures of mesial temporal and neocortical extratemporal origin with mesial temporal complex partial seizures being longer. This may result from a number of possibilities including the bilateral propagation of some mesial temporal seizures and differences in ictal generators of the underlying networks.

Intrinsic ictal dynamics at the seizure focus: effects of secondary generalization revealed by complexity measures.

PURPOSE: Partial seizures (PSs) may be self-limited regional events or propagate further and secondarily generalize. The mechanisms and dynamics of secondarily generalized tonic-clonic seizures (GTCSs) are not well understood. Methods with which to assess the dynamic of those events are also limited. METHODS: Seizures were analyzed from patients with intractable partial seizures undergoing monitoring with intracranial electrodes. Inclusion in this study required patients to have at least one PS and one GTCS. From >120 patients, seven patients fulfilled these criteria, three with mesial temporal (MTLE) onset seizures and four with neocortical lesional (NCLE) onset seizures. In total, 50 seizures were analyzed by using the matching pursuit (MP) method and the Gabor atom density (GAD), a measure of signal complexity derived from the MP method. RESULTS: The GAD complexity pattern at the seizure focus for the initial ictal period is remarkably consistent in a given patient, regardless of whether secondary generalization occurs. Secondary generalization produces greater modification of seizure activity at the focus in patients with NCLE than in patients with MTLE. In seizures from four patients with NCLE, secondary generalization resulted in an average increase of 115% in complexity at the focus compared to PSs. CONCLUSIONS: GAD shows that seizure dynamics of PSs are often very stereotyped from seizure to seizure in a given patient, particularly during early ictal evolution. Secondary generalization is more likely to produce changes in the duration and dynamics at the seizure focus in NCLE patients compared with MTLE patients. These observations suggest distinct mechanisms (e.g., feedback) that are operational during secondary generalization.

Signal complexity and synchrony of epileptic seizures: is there an identifiable preictal period?

OBJECTIVE: Epileptic seizures are characterized by increases in synchronized activity and increased signal complexity. Prediction of seizures depends upon detectable preictal changes before the actual ictal event. The studies reported here test whether two methods designed to detect changes in synchrony and complexity can identify any changes in a preictal period before visual EEG changes or clinical manifestations. METHODS: Two methods are used to characterize different, but linked, properties of the signal-complexity and synchrony. The Gabor atom density (GAD) method allows for quantification of the time-frequency components of the EEG and characterizes the complexity of the EEG signal. The measure S, based on the goodness of fit of a multivariable autoregressive model, allows for characterization of the degree of synchrony of the EEG signal. RESULTS: Complex partial seizures produce very specific patterns of increased signal complexity and subsequent postictal low complexity states. The measure S shows increased synchronization later including a prolonged period of increased synchrony in the postictal period. No significant preictal changes were seen unless contaminated by residual postictal changes in closely clustered seizures. CONCLUSIONS: Both GAD and S measures reveal ictal and prolonged postictal changes; however, there were no significant preictal changes in either complexity or synchrony. Any application of methods to detect preictal changes must be tested on seizures sufficiently separated to avoid residual postictal changes in the potential preictal period.

Human ECoG analysis during speech perception using matching pursuit: a comparison between stochastic and dyadic dictionaries.

We use the matching pursuit (MP) algorithm to detect induced gamma activity in human EEG during speech perception. We show that the MP algorithm is particularly useful for detecting small power changes at high gamma frequencies (> 70 Hz). We also compare the performance of the MP using a stochastic versus a dyadic dictionary and show that despite the frequency bias the time-frequency power plot (averaged over 100 trials) generated by the dyadic MP is almost identical (> 98.5%) to the one generated by the stochastic MP. However, the dyadic MP is computationally much faster than the stochastic MP.

Characterization of epileptic seizure dynamics using Gabor atom density.

OBJECTIVE: The study of epileptic electroencephalograph (EEG) dynamics can potentially provide insights into seizure onset, evolution and termination. We propose a new synthetic measure based on time-frequency decomposition to provide detailed characterization of these dynamic changes. METHODS: The matching pursuit (MP) method allows for continuous time-frequency decomposition. We have developed a derivative of the MP method, the Gabor atom density method (GAD) that facilitates interpretation during the dynamic ictal period. The GAD analysis was applied to intracranial recordings of complex partial seizures (n = 43) of mesial temporal origin in 7 patients. RESULTS: Complex partial seizure occurrence is systematically associated with a GAD increase of 400 +/- 150%. The GAD increase coincides with the electrographical evidence of seizure onset. The similarity between seizures in a given patient is very high with uniform onset slope, maximum level and termination pattern. Global GAD responses over all channels can reveal detailed seizure propagation patterns including secondary independent foci and secondary generalization. CONCLUSIONS: The GAD measure based on the MP decomposition is a reliable tool to detect seizure occurrence in long-term recordings, to differentiate seizures from artifacts on a multi-channel basis and to examine patterns of seizure propagation. The reproducible GAD pattern suggests consistent changes in signal inner structure and may provide new clues about seizure dynamics and evolution. SIGNIFICANCE: The GAD method can provide information about seizure dynamics that can contribute to methods of seizure detection. These analyses may lead to better understanding of seizure termination and help facilitate application of responsive seizure control devices in humans.