Andrea Varsavsky

Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction

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A robust circle criterion observer with application to neural mass models

A robust circle criterion observer is designed and applied to neural mass models. At present, no existing circle criterion observers apply to the considered models, i.e. the required linear matrix inequality is infeasible. Therefore, we generalise available results to derive a suitable estimation algorithm. Additionally, the design also takes into account input uncertainty and measurement noise. We show how to apply the observer to estimate the mean membrane potential of neuronal populations of a popular single cortical column model from the literature.

Estimating the unmeasured membrane potential of neuronal populations from the EEG using a class of deterministic nonlinear filters

We present a model-based estimation method to reconstruct the unmeasured membrane potential of neuronal populations from a single-channel electroencephalographic (EEG) measurement. We consider a class of neural mass models that share a general structure, specifically the models by Stam et al (1999 Clin. Neurophysiol. 110 1801-13), Jansen and Rit (1995 Biol. Cybern. 73 357-66) and Wendling et al (2005 J. Clin. Neurophysiol. 22 343). Under idealized assumptions, we prove the global exponential convergence of our filter. Then, under more realistic assumptions, we investigate the robustness of our filter against model uncertainties and disturbances. Analytic proofs are provided for all results and our analyses are further illustrated via simulations.

The Dynamics of the Epileptic Brain Reveal Long-Memory Processes

The pattern of epileptic seizures is often considered unpredictable and the interval between events without correlation. A number of studies have examined the possibility that seizure activity respects a power-law relationship, both in terms of event magnitude and inter-event intervals. Such relationships are found in a variety of natural and man-made systems, such as earthquakes or Internet traffic, and describe the relationship between the magnitude of an event and the number of events. We postulated that human inter-seizure intervals would follow a power-law relationship, and furthermore that evidence for the existence of a long-memory process could be established in this relationship. We performed a post hoc analysis, studying eight patients who had long-term (up to 2 years) ambulatory intracranial EEG data recorded as part of the assessment of a novel seizure prediction device. We demonstrated that a power-law relationship could be established in these patients (β = − 1.5). In five out of the six subjects whose data were sufficiently stationary for analysis, we found evidence of long memory between epileptic events. This memory spans time scales from 30 min to 40 days. The estimated Hurst exponents range from 0.51 to 0.77 ± 0.01. This finding may provide evidence of phase-transitions underlying the dynamics of epilepsy.

Patient Un-Specific Detection of Epileptic Seizures Through Changes in Variance

Despite much progress and research, fully reliable computer based epileptic seizure detection in EEG recordings is still elusive. This paper outlines a new strategy toward seizure detection. It is proposed that it is not the precise nature of a statistic that is important, but rather its variance over time. Using this, algorithms are presented that are able to successfully identify 97.6% of seizures from over 170 hours of recording and 15 different patients. False positives remain high, but virtually no pre-processing has been applied to the raw data and it is expected that this can be improved with further work

Application of Real-Time Loudness Models Can Improve Speech Recognition for Cochlear Implant Users

The aim of cochlear implant (CI) stimulation strategies is to appropriately encode the important aspects of sound into a pattern of electrical stimulation. Recent research using numerical models of loudness perception has identified that there are large differences between how loudness is encoded by existing CI sound-processing strategies and how loudness is experienced by normally hearing listeners. In this paper, we present a new CI sound-coding algorithm aimed at addressing these discrepancies. This strategy, named SCORE, uses models of electric and acoustic loudness to modify the output of an existing CI sound-processing scheme in real time, so that the loudness changes are more accurately represented in the patterns of electrical stimulation. Five subjects (six implanted ears) were tested for understanding of speech presented at relatively low levels in quiet conditions. Using SCORE, subjects demonstrated an average 8.8 percentage-point statistically significant improvement (p <; 0.02) in the number of words correctly identified relative to ACE, a commonly used stimulation strategy. These findings show that loudness changes over time are important for speech intelligibility, and that improving loudness coding in existing CI devices may lead to perceptual benefits.

A nonlinear estimator for the activity of neuronal populations in the hippocampus

Abstract We present an estimator design to reconstruct the mean membrane potential of individual neuronal populations from a single channel simulated electroencephalographic signal based on a model of the hippocampus. The robustness of the estimator against variations in the synaptic gains of the neuronal populations and disturbances in the input and measurement is studied. Our results are further illustrated in simulations.

Cochlear implant design for better representation of basilar membrane mechanics

The performance of cochlear implants (CIs) is limited by the relatively low number of electrodes used. While CIs can perform adequately when emulating place codes, they cannot be used to convey detailed spatiotemporal codes. This makes it difficult to represent complex sounds that depend on the relative timing of events over a broad region of the basilar membrane. To address this problem it is likely that future implants will increase the number of electrodes. This paper presents analysis and results that may be used to decide how many electrodes would be suitable, in the context of improving pitch discrimination.

A Complete Strategy for Patient Un-specific Detection of Epileptic Seizures Using Crude Estimations of Entropy

This paper outlines a complete strategy for patient un-specific detection of epileptic seizures on scalp data. Using a crude estimation of entropy and contextual information derived from methods employed by human experts, a true positive classification rate of 94% was achieved on 125 seizures, 22 different patients and over 500 hours of EEG recordings. False positives remain low enough for this algorithm to be clinically applicable. This paper outlines the strategy, providing justification and exploration on the estimation of entropy using low number of data samples.