An algorithm for real-time detection of spike-wave discharges in rodents

Detection of seizure onset in childhood absence epilepsy

OBJECTIVE

This study aims to detect the seizure onset, in childhood absence epilepsy, as early as possible. Indeed, interfering with absence seizures with sensory simulation has been shown to be possible on the condition that the stimulation occurs soon enough after the seizure onset.

METHODS

We present four variations (two supervised, two unsupervised) of an algorithm designed to detect the onset of absence seizures from 4 scalp electrodes, and compare their performance with that of a state-of-the-art algorithm. We exploit the characteristic shape of spike-wave discharges to detect the seizure onset. Their performance is assessed on clinical electroencephalograms from 63 patients with confirmed childhood absence epilepsy.

RESULTS

The proposed approaches succeed in early detection of the seizure onset, contrary to the classical detection algorithm. Indeed, the results clearly show the superiority of the proposed methods for small delays of detection, under 750 ms from the onset.

CONCLUSION

The performance of the proposed unsupervised methods is equivalent to that of the supervised ones. The use of only four electrodes makes the pipeline suitable to be embedded in a wearable device.

SIGNIFICANCE

The proposed pipelines perform early detection of absence seizures, which constitutes a prerequisite for a closed-loop system.

EEG phase synchronization during absence seizures

Absence seizures-generalized rhythmic spike-and-wave discharges (SWDs) are the defining property of childhood (CAE) and juvenile (JAE) absence epilepsies. Such seizures are the most compelling examples of pathological neuronal hypersynchrony. All the absence detection algorithms proposed so far have been derived from the properties of individual SWDs. In this work, we investigate EEG phase synchronization in patients with CAE/JAE and healthy subjects to explore the possibility of using the wavelet phase synchronization index to detect seizures and quantify their disorganization (fragmentation). The overlap of the ictal and interictal probability density functions was high enough to preclude effective seizure detection based solely on changes in EEG synchronization. We used a machine learning classifier with the phase synchronization index (calculated for 1 s data segments with 0.5 s overlap) and the normalized amplitude as features to detect generalized SWDs. Using 19 channels (10-20 setup), we identified 99.2% of absences. However, the overlap of the segments classified as ictal with seizures was only 83%. The analysis showed that seizures were disorganized in approximately half of the 65 subjects. On average, generalized SWDs lasted about 80% of the duration of abnormal EEG activity. The disruption of the ictal rhythm can manifest itself as the disappearance of epileptic spikes (with high-amplitude delta waves persisting), transient cessation of epileptic discharges, or loss of global synchronization. The detector can analyze a real-time data stream. Its performance is good for a six-channel setup (Fp1, Fp2, F7, F8, O1, O2), which can be implemented as an unobtrusive EEG headband. False detections are rare for controls and young adults (0.03% and 0.02%, respectively). In patients, they are more frequent (0.5%), but in approximately 82% cases, classification errors are caused by short epileptiform discharges. Most importantly, the proposed detector can be applied to parts of EEG with abnormal EEG activity to quantitatively determine seizure fragmentation. This property is important because a previous study reported that the probability of disorganized discharges is eight times higher in JAE than in CAE. Future research must establish whether seizure properties (frequency, length, fragmentation, etc.) and clinical characteristics can help distinguish CAE and JAE.

Alpha2 Adrenergic Modulation of Spike-Wave Epilepsy: Experimental Study of Pro-Epileptic and Sedative Effects of Dexmedetomidine

In the present report, we evaluated adrenergic mechanisms of generalized spike-wave epileptic discharges (SWDs), which are the encephalographic hallmarks of idiopathic generalized epilepsies. SWDs link to a hyper-synchronization in the thalamocortical neuronal activity. We unclosed some alpha2-adrenergic mechanisms of sedation and provocation of SWDs in rats with spontaneous spike-wave epilepsy (WAG/Rij and Wistar) and in control non-epileptic rats (NEW) of both sexes. Dexmedetomidine (Dex) was a highly selective alpha-2 agonist (0.003-0.049 mg/kg, i.p.). Injections of Dex did not elicit de novo SWDs in non-epileptic rats. Dex can be used to disclose the latent form of spike-wave epilepsy. Subjects with long-lasting SWDs at baseline were at high risk of absence status after activation of alpha2- adrenergic receptors. We create the concept of alpha1- and alpha2-ARs regulation of SWDs via modulation of thalamocortical network activity. Dex induced the specific abnormal state favorable for SWDs-"alpha2 wakefulness". Dex is regularly used in clinical practice. EEG examination in patients using low doses of Dex might help to diagnose the latent forms of absence epilepsy (or pathology of cortico-thalamo-cortical circuitry).

AiED: Artificial intelligence for the detection of intracranial interictal epileptiform discharges

OBJECTIVE

Deep learning provides an appealing solution for the ongoing challenge of automatically classifying intracranial interictal epileptiform discharges (IEDs). We report results from an automated method consisting of a template-matching algorithm and convolutional neural network (CNN) for the detection of intracranial IEDs ("AiED").

METHODS

1000 intracranial electroencephalogram (EEG) epochs extracted randomly from 307 subjects with refractory epilepsy were annotated independently by two expert neurophysiologists. These annotated epochs were divided into 1062 two-second epochs with IEDs and 1428 two-second epochs without IEDs, which were transformed into spectrograms prior to training the neural network. The highest performing network was validated on an annotated external test set.

RESULTS

The final network had an F1-score of 0.95 (95% CI: 0.91-0.98) and an average Area Under the Receiver Operating Characteristic of 0.98 (95% CI: 0.96-1.00). For the external test set, it showed an overall F1-score of 0.71, correctly identifying 100% of all high-amplitude IED complexes, 96.23% of all high-amplitude isolated IEDs, and 66.15% of all IEDs of atypical morphology.

CONCLUSIONS

Template-matching combined with a CNN offers a fast, robust method for detecting intracranial IEDs.

SIGNIFICANCE

"AiED" is generalizable and achieves comparable performance to human reviewers; it may support clinical and research EEG analyses.

Absence Seizure Detection Algorithm for Portable EEG Devices

Absence seizures are generalized nonmotor epileptic seizures with abrupt onset and termination. Transient impairment of consciousness and spike-slow wave discharges (SWDs) in EEG are their characteristic manifestations. This type of seizure is severe in two common pediatric syndromes: childhood (CAE) and juvenile (JAE) absence epilepsy. The appearance of low-cost, portable EEG devices has paved the way for long-term, remote monitoring of CAE and JAE patients. The potential benefits of this kind of monitoring include facilitating diagnosis, personalized drug titration, and determining the duration of pharmacotherapy. Herein, we present a novel absence detection algorithm based on the properties of the complex Morlet continuous wavelet transform of SWDs. We used a dataset containing EEGs from 64 patients (37 h of recordings with almost 400 seizures) and 30 age and sex-matched controls (9 h of recordings) for development and testing. For seizures lasting longer than 2 s, the detector, which analyzed two bipolar EEG channels (Fp1-T3 and Fp2-T4), achieved a sensitivity of 97.6% with 0.7/h detection rate. In the patients, all false detections were associated with epileptiform discharges, which did not yield clinical manifestations. When the duration threshold was raised to 3 s, the false detection rate fell to 0.5/h. The overlap of automatically detected seizures with the actual seizures was equal to ~96%. For EEG recordings sampled at 250 Hz, the one-channel processing speed for midrange smartphones running Android 10 (about 0.2 s per 1 min of EEG) was high enough for real-time seizure detection.

Automatic wavelet-based assessment of behavioral sleep using multichannel electrocorticography in rats

PURPOSE

During the last decade, the reported prevalence of sleep-disordered breathing in adults has been rapidly increasing. Therefore, automatic methods of sleep assessment are of particular interest. In a framework of translational neuroscience, this study introduces a reliable automatic detection system of behavioral sleep in laboratory rats based on the signal recorded at the cortical surface without requiring electromyography.

METHODS

Experimental data were obtained in 16 adult male WAG/Rij rats at the age of 9 months. Electrocorticographic signals (ECoG) were recorded in freely moving rats during the entire day (22.5 ± 2.2 h). Automatic wavelet-based assessment of behavioral sleep (BS) was proposed. The performance of this wavelet-based method was validated in a group of rats with genetic predisposition to absence epilepsy (n=16) based on visual analysis of their behavior in simultaneously recorded video.

RESULTS

The accuracy of automatic sleep detection was 98% over a 24-h period. An automatic BS assessment method can be adjusted for detecting short arousals during sleep (microarousals) with various duration.

CONCLUSIONS

These findings suggest that automatic wavelet-based assessment of behavioral sleep can be used for assessment of sleep quality. Current analysis indicates a temporal relationship between microarousals, sleep, and epileptic discharges in genetically prone subjects.

Deep learning for robust detection of interictal epileptiform discharges

OBJECTIVE

Automatic detection of interictal epileptiform discharges (IEDs, short as ``spikes'') from an epileptic brain can help predict seizure recurrence and support the diagnosis of epilepsy. Developing fast, reliable and robust detection methods for IEDs based on scalp or intracortical EEG may facilitate online seizure monitoring and closed-loop neurostimulation.

APPROACH

We developed a new deep learning approach, which employs a long short-term memory (LSTM) network architecture (``IEDnet'') and an auxiliary classifier generative adversarial network (AC-GAN), to train on both expert-annotated and augmented spike events from intracranial electroencephalography (iEEG) recordings of epilepsy patients. We validated our IEDnet with two real-world iEEG datasets, and compared IEDnet with the support vector machine (SVM) and random forest (RF) classifiers on their detection performances.

MAIN RESULTS

IEDnet achieved excellent cross-validated detection performances in terms of both sensitivity and specificity, and outperformed SVM and RF. Synthetic spike samples augmented by AC-GAN further improved the detection performance. In addition, the performance of IEDnet was robust with respect to the sampling frequency and noise. Furthermore, we also demonstrated the cross-institutional generalization ability of IEDnet while testing between two datasets.

SIGNIFICANCE

IEDnet achieves excellent detection performances in identifying interictal spikes. AC-GAN can produce augmented iEEG samples to improve supervised deep learning.

Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals

Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) is a crucial signal processing problem in epilepsy applications. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new method to detect SWD, with a low computational complexity making it easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the continuous Morlet 1-D wavelet decomposition is computed. The generalized Gaussian distribution (GGD) is fitted to the resulting coefficients and their variance and median are calculated. Next, a k-nearest neighbors (k-NN) classifier is trained to detect the spike-and-wave patterns, using the scale parameter of the GGD in addition to the variance and the median. Experiments were conducted using EEG signals from six human patients. Precisely, 106 spike-and-wave and 106 non-spike-and-wave signals were used for training, and 96 other segments for testing. The proposed SWD classification method achieved 95% sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These promising results set the path for new research to study the causes underlying the so-called absence epilepsy in long-term EEG recordings.

Neonatal Tactile Stimulations Affect Genetic Generalized Epilepsy and Comorbid Depression-Like Behaviors

Recent studies suggest that development of absence epilepsy and comorbid depression might be prevented by increased maternal care of the offspring, in which tactile stimulation induced by licking/grooming and non-nutritive contact seem to be crucial. In this study, we aimed to evaluate the effect of neonatal tactile stimulations (NTS) on absence epilepsy and depression-like behaviors in adulthood. Wistar Albino Glaxo from Rijswijk (WAG/Rij) rat pups with a genetic predisposition to absence epilepsy were divided into tactile stimulation (TS) group, deep touch pressure (DTP) group, maternal separation (MS) group or control group. Between postnatal day 3 and 21, manipulations (TS, DTP, and MS) were carried out for 15 min and three times a day. Animals were submitted to locomotor activity, sucrose consumption test (SCT) and forced swimming test (FST) at five months of age. At the age of six months, the electroencephalogram (EEG) recordings were conducted in order to quantify the spike-wave discharges (SWDs), which is the hallmark of absence epilepsy. The TS and DTP groups showed less and shorter SWDs in later life in comparison to maternally separated and control rats. SWDs’ number and total duration were significantly reduced in TS and DTP groups whereas mean duration of SWDs was reduced only in DTP group (p < 0.05). TS and DTP also decreased depression-like behaviors measured by SCT and FST in adult animals. In the SCT, number of approaches was significantly higher in TS and DTP groups than the maternally separated and control rats. In the FST, while the immobility latency of TS and DTP groups was significantly higher, only TS group showed significantly decreased immobility and increased swimming time. The results showed that NTS decreases both the number and length of SWDs and the depression-like behaviors in WAG/Rij rats probably by increasing arousal level and causing alterations in the level of some neurotrophic factors as well as in functions of the neural plasticity in the developing rat’s brain.

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes.

Establishing Drug Effects on Electrocorticographic Activity in a Genetic Absence Epilepsy Model: Advances and Pitfalls

The genetic rat models such as rats of the WAG/Rij strain and GAERS were developed as models for generalized genetic epilepsy and in particular for childhood absence epilepsy. These animal models were described in the eighties of the previous century and both models have, among others, face, construct and predictive validity. Both models were and are currently used as models to predict the action of antiepileptic medication and other experimental treatments, to elucidate neurobiological mechanisms of spike-wave discharges and epileptogenesis. Although the electroencephalagram (EEG)/electrocorticogram (ECoG) is imperative for establishing absence seizures and to quantify the for absence epilepsy typical spike-wave discharges, monitoring the animals behavior is equally necessary. Here an overview is given regarding the design of drug evaluation studies, which animals to use, classical and new EEG variables, the monitoring and quantification of the behavior of the rats, some pitfalls regarding the interpretation of the data, and some developments in EEG technology.

From Novel Technology to Novel Applications: Comment on "An Integrated Brain-Machine Interface Platform With Thousands of Channels" by Elon Musk and Neuralink

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Percept-related EEG classification using machine learning approach and features of functional brain connectivity

Machine learning is a promising approach for electroencephalographic (EEG) trials classification. Its efficiency is largely determined by the feature extraction and selection techniques reducing the dimensionality of input data. Dimensionality reduction is usually implemented via the mathematical approaches (e.g., principal component analysis, linear discriminant analysis, etc.) regardless of the origin of analyzed data. We hypothesize that since EEG features are determined by certain neurophysiological processes, they should have distinctive characteristics in spatiotemporal domain. If so, it is possible to specify the set of EEG principal features based on the prior knowledge about underlying neurophysiological processes. To test this hypothesis, we consider the classification of EEG trials related to the perception of ambiguous visual stimuli. We observe that EEG features, underlying the different ambiguous stimuli interpretations, are defined by the network properties of neuronal activity. Having analyzed functional neural interactions, we specify the brain area in which neural network architecture exhibits differences for different classes of EEG trials. We optimize the feedforward multilayer perceptron and develop a strategy for the training set selection to maximize the classification accuracy, being 85% when all channels are used. The revealed localization of the percept-related features allows about 95% accuracy, when the number of channels is reduced up to 90%. Obtained results can be used for classification of EEG trials associated with more complex cognitive tasks. Taking into account that cognitive activity is subserved by a distributed functional cortical network, its topological properties have to be considered when selecting optimal features for EEG trial classification.

Can absence seizures be predicted by vigilance states?: Advanced analysis of sleep-wake states and spike-wave discharges' occurrence in rats

Spike-wave discharges (SWDs) are the main manifestation of absence epilepsy. Their occurrence is dependent on the behavioral state, and they preferentially occur during unstable vigilance periods. The present study investigated whether the occurrence of SWDs can be predicted by the preceding behavioral state and whether this relationship is different between the light and the dark phases of the 24-h day. Twenty-four-hour (12:12 light/dark phases) electroencephalographic (EEG) recordings of 12 Wistar Albino Glaxo, originally bred in Rijswijk (WAG/Rij) rats, a well-known genetic model of absence epilepsy, were analyzed and transformed into sequences of 2-s length intervals of the following 6 possible states: active wakefulness (AW), passive wakefulness (PW), deep slow-wave sleep (DSWS), light slow-wave sleep (LSWS), rapid eye movement (REM) sleep, and SWDs, given discrete series of categorical data. Probabilities of all transitions between states and Shannon entropy of transitions were calculated for the light and dark phases separately and statistically analyzed. Common differences between the light and the dark phases were found regarding the time spent in AW, LSWS, DSWS, and SWDs. The most probable transitions were that AW was preceded and followed by PW and vice versa regardless of the phase of the photoperiod. A similar relationship was found for light and deep slow-wave sleep. The most probable transitions to and from SWDs were AW and LSWS, respectively, with these transition likelihoods being consistent across both circadian phases. The second most probable transitions around SWDs appeared more variable between light and dark. During the light phase, SWDs occurred around PW and participated exclusively in sleep initiation; in the dark phase, SWDs were seen on both, ascending and descending steps towards and from sleep. Conditional Shannon entropy showed that AW and DSWS are the most predictable events, while the possible prediction horizon of SWDs is not larger than 4 s and despite the higher occurrence of SWDs in the dark phase, did not differ between phases. It can be concluded that although SWDs show a stable, strong circadian rhythm with a peak in number during the dark phase, their occurrence cannot be reliably predicted by the preceding behavioral state, except at a very short time base.

Statistical Properties and Predictability of Extreme Epileptic Events

The use of extreme events theory for the analysis of spontaneous epileptic brain activity is a relevant multidisciplinary problem. It allows deeper understanding of pathological brain functioning and unraveling mechanisms underlying the epileptic seizure emergence along with its predictability. The latter is a desired goal in epileptology which might open the way for new therapies to control and prevent epileptic attacks. With this goal in mind, we applied the extreme event theory for studying statistical properties of electroencephalographic (EEG) recordings of WAG/Rij rats with genetic predisposition to absence epilepsy. Our approach allowed us to reveal extreme events inherent in this pathological spiking activity, highly pronounced in a particular frequency range. The return interval analysis showed that the epileptic seizures exhibit a highly-structural behavior during the active phase of the spiking activity. Obtained results evidenced a possibility for early (up to 7 s) prediction of epileptic seizures based on consideration of EEG statistical properties.

Spike-Wave Discharges and Sleep-Wake States during Circadian Desynchronization: No Effects of Agomelatine upon Re-Entrainment

Rapid changes in the light-dark cycle cause circadian desynchronization between rhythms of spike-wave discharges (SWDs) and motor activity in genetic epileptic rats, and this is accompanied by an increase in epileptic activity. Given the close relationship between absence seizures and sleep-wake states, the present study assessed firstly a putative relationship between vigilance rhythms and SWDs during re-synchronization, and secondly sleep-wake patterns responsible for increased epileptic activity. Lastly, in a view of existing evidence that melatonin and its agonists accelerate re-synchronization, the effects of different doses of agomelatine upon the speed of re-synchronization of different sleep-wake states and SWDs were investigated. Simultaneous electroencephalographic and electromyographic recordings were made in symptomatic WAG/Rij rats, before, during and 10 days following an 8 h light phase delay. Agomelatine was orally administered acutely and sub-chronically, during 10 post-shift days. The magnitude of the advance after the shift and the speed of re-synchronization were specific for various rhythms. Most prominent change was the increase in REM sleep duration during the dark phase. A post-shift increase in passive wakefulness and a reduction in deep slow-wave sleep coincided with an aggravation of SWDs during the light phase. Agomelatine showed neither an effect on sleep-wake parameters and SWDs, nor affected re-synchronization. The same speed of re-synchronization of SWDs and light slow-wave sleep suggests that both are controlled by a common circadian mechanism. The redistribution of SWDs and their increase in the light phase after the shift may be of importance for patients with absence epilepsy planning long trans-meridian flight across time zones.

A universal automated tool for reliable detection of seizures in rodent models of acquired and genetic epilepsy

OBJECTIVE

Prolonged electroencephalographic (EEG) monitoring in chronic epilepsy rodent models has become an important tool in preclinical drug development of new therapies, in particular those for antiepileptogenesis, disease modification, and treating drug-resistant epilepsy. We have developed an easy-to-use, reliable, computational tool for automated detection of electrographic seizures from prolonged EEG recordings in rodent models of epilepsy.

METHODS

We applied a novel method based on advanced time-frequency analysis that detects EEG episodes with excessive activity in certain frequency bands. The method uses an innovative technique of short-term spectral analysis, the Similar Basis Function algorithm. The method was applied for offline seizure detection from long-term EEG recordings from four spontaneously seizing, chronic epilepsy rat models: the fluid percussion injury (n = 5 rats, n = 49 seizures) and post-status epilepticus models (n = 119 rats, n = 993 seizures) of acquired epilepsy, and two genetic models of absence epilepsy, Genetic Absence Epilepsy Rats from Strasbourg and Wistar Albino Glaxo from Rijswijk (n = 41 and 14 rats, n = 8733 and 825 seizures, respectively).

RESULTS

Our comparative analysis revealed that the EEG amplitude spectra of these four rat models are remarkably similar during epileptiform activity and have a single expressed peak within the 17- to 25-Hz frequency range. Focusing on this band, our computer program detected all seizures in the 179 rats. A quick semiautomated user inspection of the EEGs for the period of each identified event allowed quick rejection of artifact events. The overall processing time for 12-day-long recordings varied from a few minutes (5-10) to 30 minutes, depending on the number of artifact events, which was strongly correlated with the signal quality of the raw EEG data.

SIGNIFICANCE

Our automated seizure detection tool provides high sensitivity, with acceptable specificity, for long- and short-term EEG recordings from both acquired and genetic chronic epilepsy rat models. This tool has the potential to improve the efficiency and rigor of preclinical research and therapy development using these models.

An automated, machine learning-based detection algorithm for spike-wave discharges (SWDs) in a mouse model of absence epilepsy

OBJECTIVE

Manual detection of spike-wave discharges (SWDs) from electroencephalography (EEG) records is time intensive, costly, and subject to inconsistencies/biases. In addition, manual scoring often omits information on SWD confidence/intensity, which may be important for the investigation of mechanistic-based research questions. Our objective is to develop an automated method for the detection of SWDs in a mouse model of absence epilepsy that is focused on the characteristics of human scoring of preselected events to establish a confidence-based, continuous-valued scoring.

METHODS

We develop a support vector machine (SVM)-based algorithm for the automated detection of SWDs in the γ2R43Q mouse model of absence epilepsy. The algorithm first identifies putative SWD events using frequency- and amplitude-based peak detection. Four humans scored a set of 2500 putative events identified by the algorithm. Then, using predictors calculated from the wavelet transform of each event and the labels from human scoring, we trained an SVM to classify (SWD/nonSWD) and assign confidence scores to each event identified from 60, 24-hour EEG records. We provide a detailed assessment of intra- and interrater scoring that demonstrates advantages of automated scoring.

RESULTS

The algorithm scored SWDs along a continuum that is highly correlated with human confidence and that allows us to more effectively characterize ambiguous events. We demonstrate that events along our scoring continuum are temporally and proportionately correlated with abrupt changes in spectral power bands relevant to normal behavioral states including sleep.

SIGNIFICANCE

Although there are automated and semi-automated methods for the detection of SWDs in humans and rats, we contribute to the need for continued development of SWD detection in mice. Our results demonstrate the value of viewing detection of SWDs as a continuous classification problem to better understand "ground truth" in SWD detection (ie, the most reliable features agreed upon by humans that also correlate with objective physiologic measures).

Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks

In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces.

Absence seizure susceptibility correlates with pre-ictal β oscillations

Absence seizures are generalized, cortico-thalamo-cortical (CTC) high power electroencephalographic (EEG) or electrocorticographic (ECoG) events that initiate and terminate suddenly. ECoG recordings of absence seizures in animal models of genetic absence epilepsy show a sudden spike-wave-discharge (SWD) onset that rapidly emerges from normal ECoG activity. However, given that absence seizures occur most often during periods of drowsiness or quiet wakefulness, we wondered whether SWD onset correlates with pre-ictal changes in network activity. To address this, we analyzed ECoG recordings of both spontaneous and induced SWDs in rats with genetic absence epilepsy. We discovered that the duration and intensity of spontaneous SWDs positively correlate with pre-ictal 20-40Hz (β) spectral power and negatively correlate with 4-7Hz (Ø) power. In addition, the output of thalamocortical neurons decreases within the same pre-ictal window of time. In separate experiments we found that the propensity for SWD induction was correlated with pre-ictal β power. These results argue that CTC networks undergo a pre-seizure state transition, possibly due to a functional reorganization of cortical microcircuits, which leads to the generation of absence seizures.

Absence Seizure Control by a Brain Computer Interface

The ultimate goal of epileptology is the complete abolishment of epileptic seizures. This might be achieved by a system that predicts seizure onset combined with a system that interferes with the process that leads to the onset of a seizure. Seizure prediction remains, as of yet, unresolved in absence-epilepsy, due to the sudden onset of seizures. We have developed a real-time absence seizure prediction algorithm, evaluated it and implemented it in an on-line, closed-loop brain stimulation system designed to prevent the spike-wave-discharges (SWDs), typical for absence epilepsy, in a genetic rat model. The algorithm corretly predicted 88% of the SWDs while the remaining were quickly detected. A high number of false-positive detections occurred mainly during light slow-wave-sleep. Inclusion of criteria to prevent false-positives greatly reduced the false alarm rate but decreased the sensitivity of the algoritm. Implementation of the latter version into a closed-loop brain-stimulation-system resulted in a 72% decrease in seizure activity. In contrast to long standing beliefs that SWDs are unpredictable, these results demonstrate that they can be predicted and that the development of closed-loop seizure prediction and prevention systems is a feasable step towards interventions to attain control and freedom from epileptic seizures.

A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform

OBJECTIVE

This paper investigates the multivariate oscillatory nature of electroencephalogram (EEG) signals in adaptive frequency scales for epileptic seizure detection.

METHODS

The empirical wavelet transform (EWT) has been explored for the multivariate signals in order to determine the joint instantaneous amplitudes and frequencies in signal adaptive frequency scales. The proposed multivariate extension of EWT has been studied on multivariate multicomponent synthetic signal, as well as on multivariate EEG signals of Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) scalp EEG database. In a moving-window-based analysis, 2-s-duration multivariate EEG signal epochs containing five automatically selected channels have been decomposed and three features have been extracted from each 1-s part of the 2-s-duration joint instantaneous amplitudes of multivariate EEG signals. The extracted features from each oscillatory level have been processed using a proposed feature processing step and joint features have been computed in order to achieve better discrimination of seizure and seizure-free EEG signal epochs.

RESULTS

The proposed detection method has been evaluated over 177 h of EEG records using six classifiers. We have achieved average sensitivity, specificity, and accuracy values as 97.91%, 99.57%, and 99.41%, respectively, using tenfold cross-validation method, which are higher than the compared state of art methods studied on this database.

CONCLUSION

Efficient detection of epileptic seizure is achieved when seizure events appear for long duration in hours long EEG recordings.

SIGNIFICANCE

The proposed method develops time-frequency plane for multivariate signals and builds patient-specific models for EEG seizure detection.

Automatic Identification of Interictal Epileptiform Discharges in Secondary Generalized Epilepsy

Ictal epileptiform discharges (EDs) are characteristic signal patterns of scalp electroencephalogram (EEG) or intracranial EEG (iEEG) recorded from patients with epilepsy, which assist with the diagnosis and characterization of various types of epilepsy. The EEG signal, however, is often recorded from patients with epilepsy for a long period of time, and thus detection and identification of EDs have been a burden on medical doctors. This paper proposes a new method for automatic identification of two types of EDs, repeated sharp-waves (sharps), and runs of sharp-and-slow-waves (SSWs), which helps to pinpoint epileptogenic foci in secondary generalized epilepsy such as Lennox-Gastaut syndrome (LGS). In the experiments with iEEG data acquired from a patient with LGS, our proposed method detected EDs with an accuracy of 93.76% and classified three different signal patterns with a mean classification accuracy of 87.69%, which was significantly higher than that of a conventional wavelet-based method. Our study shows that it is possible to successfully detect and discriminate sharps and SSWs from background EEG activity using our proposed method.

Timing of high-frequency cortical stimulation in a genetic absence model

INTRODUCTION

Seizure control is one of the ultimate aims of epileptology: here acute and prolonged effects of closed loop high-frequency stimulation of the somatosensory cortex on the expression of spontaneously occurring spike-wave discharges (SWD) were investigated in a genetic absence model. Effects of closed loop stimulation in the experimental group were compared with a yoked control group allowing to investigate the effect of timing related to SWD occurrence, while controlling for amount and intensity of stimulation.

METHODS

WAG/Rij rats were implanted with stimulation electrodes in the deep layers of the somatosensory cortex, and recording electrodes in the cortex and thalamus. Closed-loop and yoked stimulation (1 sec trains, biphasic 0.4 msec pulses, 130 Hz) sessions lasted 24h. The stimulation sessions were preceded and followed by baseline and post stimulation 24-h recordings.

RESULTS

Closed-loop stimulation interrupted SWD and duration of SWD was shortened. Both types of stimulation resulted in a reduction in SWD number during stimulation sessions. Closed-loop stimulation also resulted in less SWD during the last eight hours of the post-stimulation recording session. Sometimes yoked stimulation induced low-frequency afterdischarges.

DISCUSSION

SWD can be aborted by closed-loop stimulation of the somatosensory cortex, and at the same time the number of SWD was reduced. It can be regarded as a relatively safe neuromodulatory technique without habituation. The reduction of SWD during yoked stimulation session might be caused by 3 Hz afterdischarges. The reduction of SWD on the stimulation and post-stimulation sessions demonstrates the critical relevance of timing for the induction of longer lasting neuromodulatory effects: it suggests that absence seizures themselves might be involved in their reoccurrence.

Unilateral and Bilateral Cortical Resection: Effects on Spike-Wave Discharges in a Genetic Absence Epilepsy Model

Research Question

Recent discoveries have challenged the traditional view that the thalamus is the primary source driving spike-and-wave discharges (SWDs). At odds, SWDs in genetic absence models have a cortical focal origin in the deep layers of the perioral region of the somatosensory cortex. The present study examines the effect of unilateral and bilateral surgical resection of the assumed focal cortical region on the occurrence of SWDs in anesthetized WAG/Rij rats, a well described and validated genetic absence model.

Methods

Male WAG/Rij rats were used: 9 in the resected and 6 in the control group. EEG recordings were made before and after craniectomy, after unilateral and after bilateral removal of the focal region.

Results

SWDs decreased after unilateral cortical resection, while SWDs were no longer noticed after bilateral resection. This was also the case when the resected areas were restricted to layers I-IV with layers V and VI intact.

Conclusions

These results suggest that SWDs are completely abolished after bilateral removal of the focal region, most likely by interference with an intracortical columnar circuit. The evidence suggests that absence epilepsy is a network type of epilepsy since interference with only the local cortical network abolishes all seizures.

Evaluation of an automated spike-and-wave complex detection algorithm in the EEG from a rat model of absence epilepsy

The aim of this prospective blinded study was to evaluate an automated algorithm for spike-and-wave discharge (SWD) detection applied to EEGs from genetic absence epilepsy rats from Strasbourg (GAERS). Five GAERS underwent four sessions of 20-min EEG recording. Each EEG was manually analyzed for SWDs longer than one second by two investigators and automatically using an algorithm developed in MATLAB®. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the manual (reference) versus the automatic (test) methods. The results showed that the algorithm had specificity, sensitivity, PPV and NPV >94%, comparable to published methods that are based on analyzing EEG changes in the frequency domain. This provides a good alternative as a method designed to mimic human manual marking in the time domain.

Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models

BACKGROUND

Genetic rat models for childhood absence epilepsy have become instrumental in developing theories on the origin of absence epilepsy, the evaluation of new and experimental treatments, as well as in developing new methods for automatic seizure detection, prediction, and/or interference of seizures.

METHOD

Various methods for automated off and on-line analyses of ECoG in rodent models are reviewed, as well as data on how to interfere with the spike-wave discharges by different types of invasive and non-invasive electrical, magnetic, and optical brain stimulation. Also a new method for seizure prediction is proposed.

RESULTS

Many selective and specific methods for off- and on-line spike-wave discharge detection seem excellent, with possibilities to overcome the issue of individual differences. Moreover, electrical deep brain stimulation is rather effective in interrupting ongoing spike-wave discharges with low stimulation intensity. A network based method is proposed for absence seizures prediction with a high sensitivity but a low selectivity. Solutions that prevent false alarms, integrated in a closed loop brain stimulation system open the ways for experimental seizure control.

CONCLUSIONS

The presence of preictal cursor activity detected with state of the art time frequency and network analyses shows that spike-wave discharges are not caused by sudden and abrupt transitions but that there are detectable dynamic events. Their changes in time-space-frequency characteristics might yield new options for seizure prediction and seizure control.

Age-Dependent Increase of Absence Seizures and Intrinsic Frequency Dynamics of Sleep Spindles in Rats

The risk of neurological diseases increases with age. In WAG/Rij rat model of absence epilepsy, the incidence of epileptic spike-wave discharges is known to be elevated with age. Considering close relationship between epileptic spike-wave discharges and physiologic sleep spindles, it was assumed that age-dependent increase of epileptic activity may affect time-frequency characteristics of sleep spindles. In order to examine this hypothesis, electroencephalograms (EEG) were recorded in WAG/Rij rats successively at the ages 5, 7, and 9 months. Spike-wave discharges and sleep spindles were detected in frontal EEG channel. Sleep spindles were identified automatically using wavelet-based algorithm. Instantaneous (localized in time) frequency of sleep spindles was determined using continuous wavelet transform of EEG signal, and intraspindle frequency dynamics were further examined. It was found that in 5-months-old rats epileptic activity has not fully developed (preclinical stage) and sleep spindles demonstrated an increase of instantaneous frequency from beginning to the end. At the age of 7 and 9 months, when animals developed matured and longer epileptic discharges (symptomatic stage), their sleep spindles did not display changes of intrinsic frequency. The present data suggest that age-dependent increase of epileptic activity in WAG/Rij rats affects intrinsic dynamics of sleep spindle frequency.

Head-to head comparison of mGlu1 and mGlu5 receptor activation in chronic treatment of absence epilepsy in WAG/Rij rats

Acute treatment with positive allosteric modulators (PAMs) of mGlu1 and mGlu5 metabotropic glutamate receptors (RO0711401 and VU0360172, respectively) reduces the incidence of spike-and wave discharges in the WAG/Rij rat model of absence epilepsy. However, from the therapeutic standpoint, it was important to establish whether tolerance developed to the action of these drugs. We administered either VU0360172 (3 mg/kg, s.c.) or RO0711401 (10 mg/kg, s.c.) to WAG/Rij rats twice daily for ten days. VU0360172 maintained its activity during the treatment, whereas rats developed tolerance to RO0711401 since the 3rd day of treatment and were still refractory to the drug two days after treatment withdrawal. In response to VU0360172, expression of mGlu5 receptors increased in the thalamus of WAG/Rij rats after 1 day of treatment, and remained elevated afterwards. VU0360172 also enhanced mGlu5 receptor expression in the cortex after 8 days of treatment without changing the expression of mGlu1a receptors. Treatment with RO0711401 enhanced the expression of both mGlu1a and mGlu5 receptors in the thalamus and cortex of WAG/Rij rats after 3-8 days of treatment. These data were different from those obtained in non-epileptic rats, in which repeated injections of RO0711401 and VU0360172 down-regulated the expression of mGlu1a and mGlu5 receptors. Levels of VU0360172 in the thalamus and cortex remained unaltered during the treatment, whereas levels of RO0711401 were reduced in the cortex at day 8 of treatment. These findings suggest that mGlu5 receptor PAMs are potential candidates for the treatment of absence epilepsy in humans.

Time-frequency characteristics and dynamics of sleep spindles in WAG/Rij rats with absence epilepsy

In rat models of absence epilepsy, epileptic spike-wave discharges appeared in EEG spontaneously, and the incidence of epileptic activity increases with age. Spike-wave discharges and sleep spindles are known to share common thalamo-cortical mechanism, suggesting that absence seizures might affect some intrinsic properties of sleep spindles. This paper examines time-frequency EEG characteristics of anterior sleep spindles in non-epileptic Wistar and epileptic WAG/Rij rats at the age of 7 and 9 months. Considering non-stationary features of sleep spindles, EEG analysis was performed using Morlet-based continuous wavelet transform. It was found, first, that the average frequency of sleep spindles in non-epileptic Wistar rats was higher than in WAG/Rij (13.2 vs 11.2 Hz). Second, the instantaneous frequency ascended during a spindle event in Wistar rats, but it was constant in WAG/Rij. Third, in WAG/Rij rats, the number and duration of epileptic discharges increased in a period between 7 and 9 months of age, but duration and mean value of intra-spindle frequency did not change. In general, age-dependent aggravation of absence seizures in WAG/Rij rats did not affect EEG properties of sleep spindles; it was suggested that pro-epileptic changes in thalamo-cortical network in WAG/Rij rats might prevent dynamic changes of sleep spindles that were detected in Wistar.

Anti-epileptogenesis: Electrophysiology, diffusion tensor imaging and behavior in a genetic absence model

The beneficial effects of chronic and early pharmacological treatment with ethosuximide on epileptogenesis were studied in a genetic absence epilepsy model comorbid for depression. It was also investigated whether there is a critical treatment period and treatment length. Cortical excitability in the form of electrical evoked potentials, but also to cortico-thalamo-cortical network activity (spike-wave discharges, SWD and afterdischarges), white matter changes representing extra cortico-thalamic functions and depressive-like behavior were investigated. WAG/Rij rats received either ethosuximide for 2 months (post natal months 2-3 or 4-5), or ethosuximide for 4 months (2-5) in their drinking water, while control rats drank plain water. EEG measurements were made during treatment, and 6 days and 2 months post treatment. Behavioral test were also done 6 days post treatment. DTI was performed ex vivo post treatment. SWD were suppressed during treatment, and 6 days and 2 months post treatment in the 4 month treated group, as well as the duration of AD elicited by cortical electrical stimulation 6 days post treatment. Increased fractional anisotropy in corpus callosum and internal capsula on DTI was found, an increased P8 evoked potential amplitude and a decreased immobility in the forced swim test. Shorter treatments with ETX had no large effects on any parameter. Chronic ETX has widespread effects not only within but also outside the circuitry in which SWD are initiated and generated, including preventing epileptogenesis and reducing depressive-like symptoms. The treatment of patients before symptom onset might prevent many of the adverse consequences of chronic epilepsy.

Internal desynchronization facilitates seizures

PURPOSE

The occurrence of spike-wave discharges (SWDs) in WAG/Rij rats is modulated by the circadian timing system and is shaped by the presence of a light-dark cycle, motor activity, and state of vigilance. Here it is investigated whether the response to a phase shift is different between the SWDs and general motor activity rhythm. The process of reentrainment of both rhythms and its effect on number of absences was compared after a phase shift in the light-dark cycle, a condition known to induce internal desynchronization in the circadian timing system.

METHODS

Chronic electroencephalographic and motor activity recordings were made in adult WAG/Rij rats, kept in the 12:12 h light-dark cycle. After four baseline days, rats were exposed to an 8-h phase delay by shifting the light onset. Recordings were continuously made for another 10 consecutive days.

KEY FINDINGS

An immediate effect of the phase shift on both rhythms was observed: the acrophases were 7.5 h advanced. Next, they gradually returned to the baseline level, however, with a different speed. The more robust motor activity rhythm stabilizes first, whereas the weaker rhythm of SWDs adapted more slowly. The phase shift caused a prolonged aggravation of epileptic activity, observed mostly during the light phase.

SIGNIFICANCE

Different speed and character of reentrainment suggests that the occurrence of seizures and motor activity are controlled by distinct circadian oscillators. The prolonged increase in absences after the phase shift has immediate practical consequences.

On-off intermittency of thalamo-cortical oscillations in the electroencephalogram of rats with genetic predisposition to absence epilepsy

Spike-wave discharges (SWD) are electroencephalographic hallmarks of absence epilepsy. SWD are known to originate from thalamo-cortical neuronal network that normally produces sleep spindle oscillations. Although sleep spindles and SWD are considered as thalamo-cortical oscillations, functional relationship between them is not obvious. The present study describes temporal dynamics of SWD and sleep spindles as determined in 24h EEG recorded in WAG/Rij rat model of absence epilepsy. SWD, sleep spindles (10-15 Hz) and 5-9 Hz oscillations were automatically detected in EEG using wavelet-based algorithm. It was found that non-linear dynamics of SWD fitted well to the law of 'on-off intermittency'. Sleep spindles also demonstrated 'on-off intermittency', in contrast to 5-9 Hz oscillations, whose dynamics could not be classified as having any known type of non-linear behavior. Intermittency in sleep spindles and SWD implies that (1) temporal dynamics of these oscillations are deterministic in nature, and (2) it might be controlled by a system-level mechanism responsible for circadian modulation of neuronal network activity.