Summary: Purpose:We describe an algorithm for rapid realtime detection, quantitation, localization of seizures, and prediction of their clinical onset.Methods: Advanced digital signal processing techniques used in time-frequency localization, image processing, and identification of time-varying stochastic systems were used to develop the algorithm, which operates in generic or adaptable "modes." The "generic mode" was tested on (a) 125 partial seizures (each contained in a 10-min segment) involving the mesial temporal regions and recorded using depth electrodes from 16 subjects, and (b) 205 ten-minute segments of randomly selected interictal (nonseizure) data. The performance of the algorithm was compared with expert visual analysis, the current "gold standard." Results:The generic algorithm achieved perfect sensitivity and specificity (no false-positive and no false-negative detections) over the entire data set. Seizure intensity, a novel measure that seems clinically relevant, ranged between 35.7 and 6129. Detection was sufficiently rapid to allow prediction of clinical onset in 92% of seizures by a mean of 15.5 s.Conclusions: This algorithm, which was implemented with a personal computer, represents a definitive step toward rapid and accurate detection and prediction of seizures. It may also enable development of intelligent devices for automated seizure warning and treatment and stimulate new study of the dynamics of seizures and of the epileptic brain. Key Words: Epilepsy-Seizure-Detection-Prediction-Real time.The membership of the American Epilepsy Society recently ranked ' 'seizure prediction, early recognition, and blockage of seizures" as its primary research priority (1). The importance of accurate, automated, real-time detection, quantitation, and localization of seizures, a singular change in a highly complex, nonstationq time series [the EEG/electrocorticogram, (ECoG)], parallels its elusiveness (2-7). The inability to detect and quantify these changes rapidly, accurately, and automatically and to analyze such long-time series efficiently has limited the understanding of epilepsy and other dynamic diseases (8,9) and possibly the development of more effective and tolerable therapies as well. To be accurate, a solution must distinguish seizure signal changes from those caused by interictal epileptiform discharges,* or activity of extracerebral origin (artifacts) whose spectral domain often overlaps that of seizures. To be in real time, a solution must be highly computationally efficient, allowing on-line prospective rapid identification and quantitation of relevant changes, using short time windows, with limited a priori subject-specific data. We report a method that provides rapid automatic detection, quantitative analysis, and spatiotemporal localization of seizures. This algorithm, in its generic form, achieved sensitivity and specificity equal to that of expert visual analysis, the current ''gold standard," which it surpasses in its ability to quantitate seizure intensity objectively. In addition,...

The need for novel, efficacious, antiseizure therapies is widely acknowledged. This study investigates in humans the feasibility, safety, and efficacy of high-frequency electrical stimulation (HFES; 100-500 Hz) triggered by automated seizure detections. Eight patients were enrolled in this study, which consisted of a control and an experimental phase. HFES was delivered directly to the epileptogenic zone (local closed-loop) in four patients and indirectly, through anterior thalami (remote closed-loop), to the other four patients for every other automated seizure detection made by a validated algorithm. Interphase (control vs experimental phase) and intraphase (stimulated vs nonstimulated) comparisons of clinical seizure rate and relative severity (clinical and electrographic) were performed, and differences were assessed using effect size. Patients were deemed "responders" if seizure rate was reduced by at least 50%; the remaining patients were deemed "nonresponders." All patients completed the study; rescue medications were not required. There were 1,491 HFESs (0.2% triggered after-discharges). Mean change in seizure rate in the local closed-loop group was -55.5% (-100 to +36.8%); three of four responders had a mean change of -86% (-100 to -58.8%). In the remote closed-loop, the mean change of seizure rate was -40.8% (-72.9 to +1.4%); two of four responders had a mean change of -74.3% (-75.6 to -72.9%). Mean effect size was zero in the local closed-loop (responders: beneficial and medium to large in magnitude) and negligible in the remote closed-loop group (responders: beneficial and medium to large). HFES effects on epileptogenic tissue were immediate and also outlasted the stimulation period. This study demonstrates the feasibility and short-term safety of automated HFES for seizure blockage, and also raises the possibility that it may be beneficial in pharmaco-resistant epilepsies.

We introduce a new algorithm, the intrinsic time-scale decomposition (ITD), for efficient and precise time–frequency–energy (TFE) analysis of signals. The ITD method overcomes many of the limitations of both classical (e.g. Fourier transform or wavelet transform based) and more recent (empirical mode decomposition based) approaches to TFE analysis of signals that are nonlinear and/or non-stationary in nature. The ITD method decomposes a signal into (i) a sum of proper rotation components, for which instantaneous frequency and amplitude are well defined, and (ii) a monotonic trend. The decomposition preserves precise temporal information regarding signal critical points and riding waves, with a temporal resolution equal to the time-scale of extrema occurrence in the input signal. We also demonstrate how the ITD enables application of single-wave analysis and how this, in turn, leads to a powerful new class of real-time signal filters, which extract and utilize the inherent instantaneous amplitude and frequency/phase information in combination with other relevant morphological features.

A dynamical analogy supported by five scale-free statistics (the Gutenberg-Richter distribution of event sizes, the distribution of interevent intervals, the Omori and inverse Omori laws, and the conditional waiting time until the next event) is shown to exist between two classes of seizures ("focal" in humans and generalized in animals) and earthquakes. Increments in excitatory interneuronal coupling in animals expose the system's dependence on this parameter and its dynamical transmutability: moderate increases lead to power-law behavior of seizure energy and interevent times, while marked ones to scale-free (power-law) coextensive with characteristic scales and events. The coextensivity of power law and characteristic size regimes is predicted by models of coupled heterogeneous threshold oscillators of relaxation and underscores the role of coupling strength in shaping the dynamics of these systems.

Summary:Purpose: Automated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by using a more extensive database than that of a previous study and its suitability for safety/efficacy closed-loop studies to block seizures in humans.Methods: Up to eight electrocorticography (EcoG) channels from 15 subjects were analyzed off-line. Visual and computerized analyses of the data were performed by different (blinded) investigators. Independent visual analysis also was performed for clinical seizures and for detections identified only by the algorithm. The following were computed: FP rate, number of FNs, latency to automated detection, warning rate for clinical onset and warning times, seizure duration/intensity, and interrater agreement. Adaptations to improve performance were performed when indicated.Results: Fourteen subjects met inclusion criteria. Generic algorithm "relative sensitivity" for clinical seizures was 100%; two undetected subclinical seizures and two unclassified seizures were captured after adaptation. FPs/day were zero in seven and fewer than one in an additional three subjects. Adaptations for four subjects with greater than 1 FP/day (7.7-66.6/day) reduced the rate to 0 in one subject and to fewer than five FP/day (1.7-4.2/day) in the remainder. Generic latency to automated detection was <5 s in eight of 13 subjects, and in 12 of 13 after adaptation. Detections provided warning of clinical onset in three of four subjects in whom it always followed electrographic onset, and in four of four after adaptation. Interrater agreement was low for FPs and EDs.Conclusions: The generic algorithm demonstrated high sensitivity, specificity, and speed, characteristics further enhanced by adaptation. This algorithm is well suited for seizure detection/warning and use in safety/efficacy closed-loop therapy studies. Key Words: Real-time-Seizure-DetectionAlgorithm-Warning.The importance of automated real-time detection and quantitative analysis of seizures has been recognized by practitioners and researchers alike (1). To appreciate the role and potential contributions of this technology to the understanding of the dynamics of seizures and to the improvement of current therapies or discovery of new ones, the reader need only reflect on the history of cardiology. Through early and heavy reliance on automated signal-processing tools, algorithms for the automated detection and treatment of arrhythmias have been, for many years now, successfully implemented in implantable devices. The wide technologic gap between epileptology and cardiology is, in no small measure, due to the much higher dynamic complexity of the brain compared with that of the heart. This is reflected in the electrical signals each system generates and translates into increased operational and computational demands for any algorithm designed for seizure detection. The challenge is to develop algorithms that address the nonstationarity, high complexity, and interindividual...

Automated seizure blockage is a top research priority of the American Epilepsy Society. This delivery modality (referred to herein as contingent or closed loop) requires for implementation a seizure detection algorithm for control of delivery of therapy via a suitable device. The authors address the many potential advantages of this modality over conventional alternatives (periodic or continuous), and the challenges it poses in the design and analysis of trials to assess efficacy and safety-in the particular context of direct delivery of electrical stimulation to brain tissue. The experimental designs of closed-loop therapies are currently limited by ethical, technical, medical, and practical considerations. One type of design that has been used successfully in an in-hospital "closed-loop" trial using subjects undergoing epilepsy surgery evaluation as their own controls is discussed in detail. This design performs a two-way comparison of seizure intensity, duration, and extent of spread between the control (surgery evaluation) versus the experimental phase, and, within the experimental phase, between treated versus untreated seizures. The proposed statistical analysis is based on a linear model that accounts for possible circadian effects, changes in treatment protocols, and other important factors such as change in seizure probability. The analysis is illustrated using seizure intensity as one of several possible end points from one of the subjects who participated in this trial. In-hospital ultra-short-term trials to assess safety and efficacy of closed-loop delivery of electrical stimulation for seizure blockage are both feasible and valuable.

We focus on an anomalous scaling region in correlation integral [C(epsilon)] analysis of electrocorticogram in epilepsy patients. We find that epileptic seizures typically are accompanied by wide fluctuations in the slope of this scaling region. An explanation, based on analyzing the interplay between the autocorrelation and C(epsilon), is provided for these fluctuations. This anomalous slope appears to be a sensitive measure for tracking (but not predicting) seizures.

Debates on 6 controversial topics were held during the Fourth International Workshop on Seizure Prediction (IWSP4) convened in Kansas City (July 4–7, 2009). The topics were 1) Ictogenesis: focus vs. 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 (HFOs) provide relevant independent information? and 6) Phase synchronization – is it worthwhile as measured? This manuscript, written by the IWSP4 organizing committee and the debaters, summarizes the arguments presented during the debates.