Drausin Wulsin

Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo

Arrays of electrodes for recording and stimulating the brain are used throughout clinical medicine and basic neuroscience research, yet are unable to sample large areas of the brain while maintaining high spatial resolution because of the need to individually wire each passive sensor at the electrode-tissue interface. To overcome this constraint, we developed new devices that integrate ultrathin and flexible silicon nanomembrane transistors into the electrode array, enabling new dense arrays of thousands of amplified and multiplexed sensors that are connected using fewer wires. We used this system to record spatial properties of cat brain activity in vivo, including sleep spindles, single-trial visual evoked responses and electrographic seizures. We found that seizures may manifest as recurrent spiral waves that propagate in the neocortex. The developments reported here herald a new generation of diagnostic and therapeutic brain-machine interface devices.

Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement.

Clinical electroencephalography (EEG) records vast amounts of human complex data yet is still reviewed primarily by human readers. Deep belief nets (DBNs) are a relatively new type of multi-layer neural network commonly tested on two-dimensional image data but are rarely applied to times-series data such as EEG. We apply DBNs in a semi-supervised paradigm to model EEG waveforms for classification and anomaly detection. DBN performance was comparable to standard classifiers on our EEG dataset, and classification time was found to be 1.7-103.7 times faster than the other high-performing classifiers. We demonstrate how the unsupervised step of DBN learning produces an autoencoder that can naturally be used in anomaly measurement. We compare the use of raw, unprocessed data--a rarity in automated physiological waveform analysis--with hand-chosen features and find that raw data produce comparable classification and better anomaly measurement performance. These results indicate that DBNs and raw data inputs may be more effective for online automated EEG waveform recognition than other common techniques.

Temporal changes of neocortical high-frequency oscillations in epilepsy.

High-frequency (100-500 Hz) oscillations (HFOs) recorded from intracranial electrodes are a potential biomarker for epileptogenic brain. HFOs are commonly categorized as ripples (100-250 Hz) or fast ripples (250-500 Hz), and a third class of mixed frequency events has also been identified. We hypothesize that temporal changes in HFOs may identify periods of increased the likelihood of seizure onset. HFOs (86,151) from five patients with neocortical epilepsy implanted with hybrid (micro + macro) intracranial electrodes were detected using a previously validated automated algorithm run over all channels of each patients entire recording. HFOs were characterized by extracting quantitative morphologic features and divided into four time epochs (interictal, preictal, ictal, and postictal) and three HFO clusters (ripples, fast ripples, and mixed events). We used supervised classification and nonparametric statistical tests to explore quantitative changes in HFO features before, during, and after seizures. We also analyzed temporal changes in the rates and proportions of events from each HFO cluster during these periods. We observed patient-specific changes in HFO morphology linked to fluctuation in the relative rates of ripples, fast ripples, and mixed frequency events. These changes in relative rate occurred in pre- and postictal periods up to thirty min before and after seizures. We also found evidence that the distribution of HFOs during these different time periods varied greatly between individual patients. These results suggest that temporal analysis of HFO features has potential for designing custom seizure prediction algorithms and for exploring the relationship between HFOs and seizure generation.

Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets

Clinical electroencephalography (EEG) is routinely used to monitor brain function in critically ill patients, and specific EEG waveforms are recognized by clinicians as signatures of abnormal brain. These pathologic EEG waveforms, once detected, often necessitate accute clinincal interventions, but these events are typically rare, highly variable between patients, and often hard to separate from background, making them difficult to reliably detect. We show that Deep Belief Nets (DBNs), a type of multi-layer generative neural network, can be used effectively for such EEG anomaly detection. We compare this technique to the state-of-the-art, a one-class Support Vector Machine (SVM), showing that the DBN outperforms the SVM by the F1 measure for our EEG dataset. We also show how the outputs of a DBN-based detector can be used to aid visualization of anomalies in large EEG data sets and propose a method for using DBNs to gain insight into which features of signals are characteristically anomalous. These findings show that Deep Belief Nets can facilitate human review of large amounts of clinical EEG as well as mining new EEG features that may be indicators of unusual activity.

Mining continuous intracranial EEG in focal canine epilepsy: Relating interictal bursts to seizure onsets.

Brain regions are localized for resection during epilepsy surgery based on rare seizures observed during a short period of intracranial electroencephalography (iEEG) monitoring. Interictal epileptiform bursts, which are more prevalent than seizures, may provide complementary information to aid in epilepsy evaluation. In this study, we leverage a long‐term iEEG dataset from canines with naturally occurring epilepsy to investigate interictal bursts and their electrographic relationship to seizures.

Temporal behavior of seizures and interictal bursts in prolonged intracranial recordings from epileptic canines

Epilepsy is a chronic disorder, but seizure recordings are usually obtained in the acute setting. The chronic behavior of seizures and the interictal bursts that sometimes initiate them is unknown. We investigate the variability of these electrographic patterns over an extended period of time using chronic intracranial recordings in canine epilepsy.

An unsupervised method for identifying regions that initiate seizures on intracranial EEG

Epilepsy patients who do not respond to pharmacological treatments currently have only brain surgery as a major alternative therapy. Identifying which brain areas to remove is thus of critical importance for physicians and the patient. Currently, this process is almost entirely manual, can vary greatly between clinical experts and centers, and depends only on qualitative EEG features, all of which may help explain the only modest success of extratemperal lobe epilepsy surgery. In this study, we explore an unsupervised, quantitative method for identifying seizure onset regions. A Gaussian mixture model (GMM) was used to cluster 500 ms epochs of intracranial electroencephalogram (EEG) prior to (preictal) and during (ictal) seizures in week-long continuous recordings from three patients during evalulation for epilepsy surgery. The GMM learning paradigm determines the optimal number of clusters for each patient. For the two patients whose epochs sorted into two clusters, we found that one cluster was predominantly composed of seizure epochs, and a subset of the channels made brief “forays” into that cluser in the time leading up to seizure onset. This observation is in keeping with the clinical hypothesis that certain brain areas may be the initiators of seizure activity, and we find that the channels independently labeled by physicians as seizure onset zones (SOZs) are statistically overrepesented in the seizure-defined cluster. Nevertheless, we also find that a subset of channels not labeled as SOZs has similar properties as those labeled SOZs. In this study we have tried to avoid many of the assumptions commonly made about what features and events are indicative of epileptogenic activity and believe that such analysis can help avoid many of the pitfalls of manual, non-objective human SOZ marking.

Nonparametric multi-level clustering of human epilepsy seizures

Understanding neuronal activity in the human brain is an extremely difficult problem both in terms of measurement and statistical modeling. We address a particular research question in this area: the analysis of human intracranial electroencephalogram (iEEG) recordings of epileptic seizures from a collection of patients. In these data, each seizure of each patient is defined by the activities of many individual recording channels. The modeling of epileptic seizures is challenging due the large amount of heterogeneity in iEEG signal between channels within a particular seizure, between seizures within an individual, and across individuals. We develop a new nonparametric hierarchical Bayesian model that simultaneously addresses these multiple levels of heterogeneity in our epilepsy data. Our approach, which we call a multi-level clustering hierarchical Dirichlet process (MLC-HDP), clusters over channel activities within a seizure, over seizures of a patient and over patients. We demonstrate the advantages of our methodology over alternative approaches in human EEG seizure data and show that its seizure clustering is close to manual clustering by a physician expert. We also address important clinical questions like “to which seizures of other patients is this seizure similar?”

Does the morphology of high-frequency (100–500 Hz) brain oscillations change during epileptic seizures?

Transient high-frequency (100–500 Hz) oscillations (HFOs) recorded directly from the surface of the human brain are emerging as a potential biomarker for epileptogenic brain tissue. Whether the morphology of these events can be used to understand the process of seizure generation is unknown. In this experiment, we used supervised learning techniques in an attempt to distinguish HFOs occurring during versus outside of seizures in five patients implanted with intracranial micro-and macroelectrodes as part of routine evaluation for epilepsy surgery. We trained three classifiers using logistic regression, k-nearest neighbors, and support vector machines, respectively, and assessed their performance using the F1 measure in conjunction with permutation testing. All of the classifiers produced a low number of true positives relative to false positives and false negatives, but two of the classifiers performed slightly better than chance in certain patients. These results suggest that ictal HFOs are difficult to distinguish from those occurring interictally, and that a marked change in HFO morphology is not likely to be associated with seizure generation.