Biomedical subjects

Brian Litt

Publications and source records attributed to Brian Litt.

7 recordsLinked to original sources

PubMed2003

Evaluating devices for treating epilepsy.

PURPOSE: Research into new implantable devices for treating epilepsy is expanding rapidly. Pilot studies suggest sufficient safety and potential efficacy to justify proceeding with larger scale clinical trials. Understanding the challenges presented by these trials, the testing and approval process for implantable devices, and how these differ from requirements for antiepileptic drugs (AEDs) is vital to evaluating when and where these new technologies will fit into the therapeutic armamentarium. METHODS: Important lessons regarding the limitations of uncontrolled pilot studies, patient registries, and how the Food and Drug Administration (FDA) approval process can influence trials are drawn from the implantable device literature. Some discussion of the role of animal experiments is presented, both as justification for investigational device exemptions and their potential role in establishing safety. Clinical trial experience with the vagal nerve stimulator, the first device approved for the treatment of epilepsy, is also discussed. RESULTS: New implantable devices hold great promise for medically refractory epilepsy patients who have no other therapeutic alternative. If effective, they may become a viable alternative to epilepsy surgery or multiple AED therapy in appropriate patients. CONCLUSIONS: The proper evaluation, use, and acceptance of antiepileptic devices will ultimately depend on carefully executed clinical trials that take into account unique aspects of these devices, such as the requirement for surgery, electrode placement, and navigation through FDA-monitored testing and approval.

Animals↗

PubMed2003

Detection of seizure precursors from depth-EEG using a sign periodogram transform.

Brief bursts of focal, low amplitude rhythmic activity have been observed on depth electroencephalogram (EEG) in the minutes before electrographic onset of seizures in human mesial temporal lobe epilepsy. We have found these periods to contain discrete, individualized synchronized activity in patient-specific frequency bands ranging from 20 to 40 Hz. We present a method for detecting and displaying these events using a periodogram of the sign-limited temporal derivative of the EEG signal, denoted joint sign periodogram event characterization transform (JSPECT). When applied to continuous 2-6 day depth-EEG recordings from ten patients with temporal lobe epilepsy, JSPECT demonstrated that these patient-specific EEG events reliably occurred 5-80 s prior to electrical onset of seizures in five patients with focal, unilateral seizure onsets. JSPECT did not reveal this type of activity prior to seizures in five other patients with bilateral, extratemporal or more diffuse seizure onsets on EEG. Patient-specific, localized rhythmic events may play an important role in seizure generation in temporal lobe epilepsy. The JSPECT method efficiently detects these events, and may be useful as part of an automated system for predicting electrical seizure onset in appropriate patients.

Algorithms↗

PubMed2003

Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients.

Epileptic seizure prediction has steadily evolved from its conception in the 1970s, to proof-of-principle experiments in the late 1980s and 1990s, to its current place as an area of vigorous, clinical and laboratory investigation. As a step toward practical implementation of this technology in humans, we present an individualized method for selecting electroencephalogram (EEG) features and electrode locations for seizure prediction focused on precursors that occur within ten minutes of electrographic seizure onset. This method applies an intelligent genetic search process to EEG signals simultaneously collected from multiple intracranial electrode contacts and multiple quantitative features derived from these signals. The algorithm is trained on a series of baseline and preseizure records and then validated on other, previously unseen data using split sample validation techniques. The performance of this method is demonstrated on multiday recordings obtained from four patients implanted with intracranial electrodes during evaluation for epilepsy surgery. An average probability of prediction (or block sensitivity) of 62.5% was achieved in this group, with an average block false positive (FP) rate of 0.2775 FP predictions/h, corresponding to 90.47% specificity. These findings are presented as an example of a method for training, testing and validating a seizure prediction system on data from individual patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual's neurophysiology prior to clinical deployment.

Adult↗

PubMed2002

Evidence for self-organized criticality in human epileptic hippocampus.

Self-organized criticality (SOC) is a property of complex dynamic systems that evolve to a critical state, capable of producing scale-free energy fluctuations. A characteristic feature of dynamical systems exhibiting SOC is the power-law probability distributions that describe the dynamics of energy release. We show experimental evidence for SOC in the epileptic focus of seven patients with medication-resistant temporal lobe epilepsy. In the epileptic focus the probability density of pathological energy fluctuations and the time between these energy fluctuations scale as (energy) and (time), respectively. The power-laws characterizing the probability distributions from these patients are consistent with computer simulations of integrate-and-fire oscillator networks that have been reported recently. These findings provide insight into the neuronal dynamics of epileptic hippocampus and suggest a mechanism for interictal epileptiform fluctuations. The presence of SOC in human epileptic hippocampus may provide a method for identifying the network involved in seizure generation.

Electroencephalography↗

PubMed2002

Prediction of epileptic seizures.

For almost 40 years, neuroscientists thought that epileptic seizures began abruptly, just a few seconds before clinical attacks. There is now mounting evidence that seizures develop minutes to hours before clinical onset. This change in thinking is based on quantitative studies of long digital intracranial electroencephalographic (EEG) recordings from patients being evaluated for epilepsy surgery. Evidence that seizures can be predicted is spread over diverse sources in medical, engineering, and patent publications. Techniques used to forecast seizures include frequency-based methods, statistical analysis of EEG signals, non-linear dynamics (chaos), and intelligent engineered systems. Advances in seizure prediction promise to give rise to implantable devices able to warn of impending seizures and to trigger therapy to prevent clinical epileptic attacks. Treatments such as electrical stimulation or focal drug infusion could be given on demand and might eliminate side-effects in some patients taking antiepileptic drugs long term. Whether closed-loop seizure-prediction and treatment devices will have the profound clinical effect of their cardiological predecessors will depend on our ability to perfect these techniques. Their clinical efficacy must be validated in large-scale, prospective, controlled trials.

Brain↗

PubMed2002

Seizure prediction and the preseizure period.

Beginning in the 1970s engineers designed systems to predict epileptic seizures based upon quantitative changes in the electroencephalogram, which they hypothesized began well in advance of clinical seizure onset. These efforts flourished in the 1990s, as independent laboratories demonstrated evidence of a 'preseizure period' up to 20 min prior to clinical symptoms in patients implanted with intracranial electrodes during evaluation for epilepsy surgery. Years later, clinical and laboratory experiments leave little doubt that a preseizure period exists in temporal lobe and perhaps other forms of epilepsy. Its existence, however, raises fundamental questions about what constitutes a seizure, what brain regions are involved in seizure generation, and whether discrete interictal, preictal, ictal and post-ictal physiologies exist, or blend together in a continuous process. Pressing milestones, necessary for clinical utility, are: (1) demonstrating prospective seizure prediction from prolonged human data sets, (2) elucidating mechanisms underlying seizure precursors and (3) implementing these algorithms on implantable hardware platforms. The notion of a preseizure state is catalyzing new clinical and basic science research, which has the potential to dramatically increase our understanding of epilepsy, and to generate exciting new therapies for patients.

Brain↗

PubMed2002

Time-frequency spectral estimation of multichannel EEG using the Auto-SLEX method.

In this paper, we apply a new time-frequency spectral estimation method for multichannel data to epileptiform electroencephalography (EEG). The method is based on the smooth localized complex exponentials (SLEX) functions which are time-frequency localized versions of the Fourier functions and, hence, are ideal for analyzing nonstationary signals whose spectral properties evolve over time. The SLEX functions are simultaneously orthogonal and localized in time and frequency because they are obtained by applying a projection operator rather than a window or taper. In this paper, we present the Auto-SLEX method which is a statistical method that 1) computes the periodogram using the SLEX transform, 2) automatically segments the signal into approximately stationary segments using an objective criterion that is based on log energy, and 3) automatically selects the optimal bandwidth of the spectral smoothing window. The method is applied to the intracranial EEG from a patient with temporal lobe epilepsy. This analysis reveals a reduction in average duration of stationarity in preseizure epochs of data compared to baseline. These changes begin up to hours prior to electrical seizure onset in this patient.

Algorithms↗