Interictal State Research Articles

Cramer Distance: A Deep Learning Approach for Better Epileptic Seizure Prediction

Epilepsy is a neurological condition that is found in most people all over the world, and the ability to accurately anticipate seizures in epileptic patients has a significant impact on both their level of protection and their overall quality of life. This research proposes a unique patient specific seizure prediction approach based on Deep Learning (DL) using long-term scalp electroencephalogram (EEG) recordings to predict seizure onset. Preictal brain states should be adequately detected and differentiated from the prevalent interictal brain states as early as possible to make this technology acceptable for real-time use. A single automated system has been designed for the Features Extraction (FE) and classification processes. The raw EEG signal that has not been pre-processed is considered the input to the system, and the signal is further reduced using subsequent computations. An innovative reconstruction approach using Variational Auto-Encoder Generative Adversarial Networks (VAE+C+GAN) with the Cramer Distance (CD) and a Temporal-Spatial-Frequency (TSF) loss function is presented in this research work. The machine that discriminates receives instructions to differentiate between created tests and actual samples, while the generator is verified to produce false samples that the discriminator does not recognize as fake. The proposed VAE+C+GAN’s experimental results have been examined, and a classification accuracy of 95% has been achieved. According to the experiment's findings, the VAE-C-GAN performs better than the current EEG classification system and has excellent potential for real-time applications.

Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding

Objective. Identifying the seizure occurrence period (SOP) in extended EEG recordings is crucial for neurologists to diagnose seizures effectively. However, many existing computer-aided diagnosis systems for epileptic seizure detection (ESD) primarily focus on distinguishing between ictal and interictal states in EEG recordings. This focus has limited their application in clinical settings, as these systems typically rely on supervised learning approaches that require labeled data.Approach. To address this, our study introduces an unsupervised learning framework for ESD using a 1D- cascaded convolutional autoencoder (1D-CasCAE). In this approach, EEG recordings from selected patients in the CHB-MIT datasets are first segmented into 5 s epochs. Eight informative channels are chosen based on the correlation coefficient and Shannon entropy. The 1D-CasCAE is designed to autonomously learn the characteristic patterns of interictal (non-seizure) segments through downsampling and upsampling processes. The integration of adaptive thresholding and a moving window significantly enhances the model's robustness, enabling it to accurately identify ictal segments in long EEG recordings.Main results. Experimental results demonstrate that the proposed 1D-CasCAE effectively learns normal EEG signal patterns and efficiently detects anomalies (ictal segments) using reconstruction errors. When compared with other leading methods in anomaly detection, our model exhibits superior performance, as evidenced by its average Gmean, sensitivity, specificity, precision, and false positive rate scores of 98.00% ± 3.51%, 94.94% ± 6.92%, 99.60% ± 0.30%, 79.92% ± 13.56% and 0.0044 ± 0.0030 h-1respectively for a typical patient in CHB-MIT datasets.Significance. The developed model framework can be employed in clinical settings, replacing the manual inspection process of EEG signals by neurologists. Furthermore, the proposed automated system can adapt to each patient's SOP through the use of variable time windows for seizure detection.

Unveiling the epilepsy enigma: an agile and optimal machine learning approach for detecting inter-ictal state from electroencephalogram signals

Epilepsy is a chronic neurological disorder characterized by the occurrence of paroxysmal recurrent seizures, which are caused by abnormal electrical activity in the brain. Seizures vary widely in their presentation, depending on the specific region of the brain involved and the extent of the abnormal electrical discharges. The disease can affect cognitive function posing a serious threat to the patients’ lives. Epilepsy causes emotional and behavioral changes, along with sleep disorders and migraines, leading to social isolation and discrimination. Timely administration of medication can cure most cases of epilepsy. However, identifying epileptic patients requires reviewing multiple EEG signal sheets, which can delay disease prediction. Therefore, the aim of our study is to apply simplistic machine learning algorithms that can study the EEG signal data swiftly to identify individuals in seizure, inter-ictal, and normal states, thereby aiding in medical diagnosis. The novelty of this study lies in the utilization of pre-built methods and develop a fast and efficient model that is lightweight and easy to integrate in healthcare to provide relief to epileptic patients. While previous studies have achieved high accuracy, the discussion involving time complexity of their models has been scarce. Given the importance of timely medication in managing epilepsy, it is crucial to consider the runtime of the model rather than solely focusing on accuracy. Therefore, a model that balances both a short runtime (2.9 min) and a satisfactory accuracy (97.46%) has been developed in this project. Integration of this project's findings will catalyze transformative changes within the healthcare industry, enabling healthcare professionals to detect epilepsy at earlier stages and provide timely interventions, ultimately fostering a system that prioritizes precision, innovation, and improved patient outcomes.

An epilepsy classification based on FFT and fully convolutional neural network nested LSTM.

Epilepsy, which is associated with neuronal damage and functional decline, typically presents patients with numerous challenges in their daily lives. An early diagnosis plays a crucial role in managing the condition and alleviating the patients' suffering. Electroencephalogram (EEG)-based approaches are commonly employed for diagnosing epilepsy due to their effectiveness and non-invasiveness. In this study, a classification method is proposed that use fast Fourier Transform (FFT) extraction in conjunction with convolutional neural networks (CNN) and long short-term memory (LSTM) models. Most methods use traditional frameworks to classify epilepsy, we propose a new approach to this problem by extracting features from the source data and then feeding them into a network for training and recognition. It preprocesses the source data into training and validation data and then uses CNN and LSTM to classify the style of the data. Upon analyzing a public test dataset, the top-performing features in the fully CNN nested LSTM model for epilepsy classification are FFT features among three types of features. Notably, all conducted experiments yielded high accuracy rates, with values exceeding 96% for accuracy, 93% for sensitivity, and 96% for specificity. These results are further benchmarked against current methodologies, showcasing consistent and robust performance across all trials. Our approach consistently achieves an accuracy rate surpassing 97.00%, with values ranging from 97.95 to 99.83% in individual experiments. Particularly noteworthy is the superior accuracy of our method in the AB versus (vs.) CDE comparison, registering at 99.06%. Our method exhibits precise classification abilities distinguishing between epileptic and non-epileptic individuals, irrespective of whether the participant's eyes are closed or open. Furthermore, our technique shows remarkable performance in effectively categorizing epilepsy type, distinguishing between epileptic ictal and interictal states versus non-epileptic conditions. An inherent advantage of our automated classification approach is its capability to disregard EEG data acquired during states of eye closure or eye-opening. Such innovation holds promise for real-world applications, potentially aiding medical professionals in diagnosing epilepsy more efficiently.

Anterior thalamic nucleus local field potentials during focal temporal lobe epileptic seizures.

To analyze the local field potentials (LFPs) in patients with focal drug-resistant epilepsy (DRE) from the anterior nucleus of the thalamus (ANT) during inter-ictal state and seizure state. ANT stereotactic EEG (SEEG) recordings were studied in four patients with focal temporal lobe epilepsy. SEEG data was classified as inter-ictal and ictal state and sub-categorized into electrographic (ESz), focal aware seizure (FAS), focal with impaired awareness (FIA), or focal to bilateral tonic-clonic seizure (FBTC). LFP was analyzed at 4 Hz, 8 Hz, 16 Hz, 32 Hz, high gamma (100 Hz), and ripples (200 Hz) using spectrogram analysis and a statistical comparison of normalized power spectral density (PSD) averaged during seizures versus pre-ictal baseline segments. The LFP recordings were analyzed for 162 seizures (127 ESz, 23 FAS, 6 FIA, and 6 FBTC). Based on time-frequency data (spectrogram), a broad band of activity, occurring between 2 and 6 Hz and centered at 4 Hz, and thin-band activity occurring specifically at 8 Hz on the frequency spectrogram were observed during the inter-ictal state. Statistically significant changes in LFP-PSD were seen for FAS, FIA, and FBTC. We observed a significant gain in LFP at the lower frequency band during FAS at 4 Hz, FIA, and FBTC at 4, 8, and 16 Hz while also observing increases at higher frequencies during FBTC at 100 and 200 Hz and a decrease during FAS seizures at 32 Hz. In contrast, no significant change in LFP power was seen for electrographic seizures. Our observations from a limited dataset indicate that all clinical seizure types, but not electrographic seizures, caused a change in ANT-LFP based on the magnitude of the associated power spectral density (PSD). Future work will be needed to validate the use of ANT-LFP at these frequencies as accurate measurements of seizure occurrence and severity. This work represents a first step toward understanding ANT thalamic LFP patterns during focal seizures and developing adaptive DBS strategies.

A generalized seizure type: Myoclonic-to-tonic seizure

Background and purposeTo test the hypothesis that myoclonic seizures can evolve to tonic seizures, we documented the electroclinical features of this under-recognized seizure type. MethodsWe observed a distinct seizure pattern starting with myoclonus without returning to an interictal state, which subsequently evolved into generalized tonic seizures. The detailed symptomatic and electroencephalographic characteristics of this seizure were extracted, and the clinical manifestations, drug curative responses in patients with this seizure were reviewed and analyzed. ResultsThe onset of all seizures was characterized by a preceding period of myoclonus and bursts of generalized spike or poly-spike slow wave discharges with high amplitude. This was closely followed by the occurrence of tonic seizures, which were distinguished by bursts of generalized fast activity at 10 Hz or higher frequency. This under-recognized seizure type has been designated as myoclonic-to-tonic (MT) seizure. The number of patients identified with MT seizures in this study was 34. The prevalence rate of MT seizures was found to be higher in males. While MT seizures typically included a tonic component, it should be noted that some patients experiencing this seizure type never presented with isolated tonic seizures. Generalized Epilepsy not further defined (GE) accounted for approximately one-third of the diagnosed cases, followed by Lennox-Gastaut syndrome and Epilepsy with Myoclonic-Atonic seizures. In comparison to other types of epilepsy, GE with MT seizures demonstrated a more favorable prognosis. ConclusionsThe classification of myoclonic-to-tonic seizure represents a novel approach in comprehending the ictogenesis of generalized seizures and can provide valuable assistance to clinicians in epilepsy diagnosis.

EDeeplepsy: An artificial neural framework to reveal different brain states in children with epileptic spasms

ObjectiveDespite advances, analysis and interpretation of EEG still essentially rely on visual inspection by a super-specialized physician. Considering the vast amount of data that composes the EEG, much of the detail inevitably escapes ordinary human scrutiny. Significant information may not be evident and is missed, and misinterpretation remains a serious problem. Can we develop an artificial intelligence system to accurately and efficiently classify EEG and even reveal novel information? In this study, deep learning techniques and, in particular, Convolutional Neural Networks, have been used to develop a model (which we have named eDeeplepsy) for distinguishing different brain states in children with epilepsy. MethodsA novel EEG database from a homogenous pediatric population with epileptic spasms beyond infancy was constituted by epileptologists, representing a particularly intriguing seizure type and challenging EEG. The analysis was performed on such samples from long-term video-EEG recordings, previously coded as images showing how different parts of the epileptic brain are distinctly activated during varying states within and around this seizure type. ResultsResults show that not only could eDeeplepsy differentiate ictal from interictal states but also discriminate brain activity between spasms within a cluster from activity away from clusters, usually undifferentiated by visual inspection. Accuracies between 86 % and 94 % were obtained for the proposed use cases. SignificanceWe present a model for computer-assisted discrimination that can consistently detect subtle differences in the various brain states of children with epileptic spasms, and which can be used in other settings in epilepsy with the purpose of reducing workload and discrepancies or misinterpretations. The research also reveals previously undisclosed information that allows for a better understanding of the pathophysiology and evolving characteristics of this particular seizure type. It does so by documenting a different state (interspasms) that indicates a potentially non-standard signal with distinctive epileptogenicity at that period.

Exploring brain network oscillations during seizures in drug-naïve patients with juvenile absence epilepsy.

We aimed to investigate the brain network activity during seizures in patients with untreated juvenile absence epilepsy. Thirty-six juvenile absence epilepsy (JAE) patients with a current high frequency of seizures (more than five seizures during a 2 h EEG examination) were included. Each participant underwent a 2 h video EEG examination. Five 10 s EEG epochs for inter-ictal, pre-ictal, and post-ictal, and five 5 s EEG epochs for ictal states were extracted. Five 10 s resting-state EEG epochs for each participant from a sex- and age-matched healthy control (HC) were enrolled. The topological parameters of the brain networks were calculated using a graph theory analysis. Compared with the resting state of the HC group, the global efficiency, local efficiency, and clustering coefficients of the JAE group decreased in the inter-ictal state. In addition, the ictal state showed significantly increased global and local efficiency and clustering coefficients (p < 0.05) and a decreased small-world index and the shortest path length (p < 0.05) in the theta and alpha bands, compared to the remaining states within the JAE group. Moreover, subgroup analysis revealed that those JAE patients with typical 3 Hz discharges had upgraded global efficiency, local efficiency, and clustering coefficients in both delta and beta1 bands, compared to those JAE patients with non-3 Hz discharges during seizures. The present study supported the idea that the changes in the EEG brain networks in JAE patients are characterized by decreased global and local efficiency and clustering coefficient in the alpha band. Moreover, the onset of seizures is accompanied by excessively enhanced network efficiency. JAE patients with different ictal discharge patterns may have different functional network oscillations.

Bifurcations and bursting in the Epileptor.

The Epileptor is a phenomenological model for seizure activity that is used in a personalized large-scale brain modeling framework, the Virtual Epileptic Patient, with the aim of improving surgery outcomes for drug-resistant epileptic patients. Transitions between interictal and ictal states are modeled as bifurcations, enabling the definition of seizure classes in terms of onset/offset bifurcations. This establishes a taxonomy of seizures grounded in their essential underlying dynamics and the Epileptor replicates the activity of the most common class, as observed in patients with focal epilepsy, which is characterized by square-wave bursting properties. The Epileptor also encodes an additional mechanism to account for interictal spikes and spike and wave discharges. Here we use insights from a more generic model for square-wave bursting, based on the Unfolding Theory approach, to guide the bifurcation analysis of the Epileptor and gain a deeper understanding of the model and the role of its parameters. We show how the Epileptor's parameters can be modified to produce activities for other seizures classes of the taxonomy, as observed in patients, so that the large-scale brain models could be further personalized. Some of these classes have already been described in the literature in the Epileptor, others, predicted by the generic model, are new. Finally, we unveil how the interaction with the additional mechanism for spike and wave discharges alters the bifurcation structure of the main burster.

Parallel Ictal-Net, a Parallel CNN Architecture with Efficient Channel Attention for Seizure Detection.

Around 70 million people worldwide are affected by epilepsy, a neurological disorder characterized by non-induced seizures that occur at irregular and unpredictable intervals. During an epileptic seizure, transient symptoms emerge as a result of extreme abnormal neural activity. Epilepsy imposes limitations on individuals and has a significant impact on the lives of their families. Therefore, the development of reliable diagnostic tools for the early detection of this condition is considered beneficial to alleviate the social and emotional distress experienced by patients. While the Bonn University dataset contains five collections of EEG data, not many studies specifically focus on subsets D and E. These subsets correspond to EEG recordings from the epileptogenic zone during ictal and interictal events. In this work, the parallel ictal-net (PIN) neural network architecture is introduced, which utilizes scalograms obtained through a continuous wavelet transform to achieve the high-accuracy classification of EEG signals into ictal or interictal states. The results obtained demonstrate the effectiveness of the proposed PIN model in distinguishing between ictal and interictal events with a high degree of confidence. This is validated by the computing accuracy, precision, recall, and F1 scores, all of which consistently achieve around 99% confidence, surpassing previous approaches in the related literature.

EEG-Based Seizure Detection Using Variable-Frequency Complex Demodulation and Convolutional Neural Networks

Epilepsy is a complex neurological disorder characterized by recurrent and unpredictable seizures that affect millions of people around the world. Early and accurate epilepsy detection is critical for timely medical intervention and improved patient outcomes. Several methods and classifiers for automated epilepsy detection have been developed in previous research. However, the existing research landscape requires innovative approaches that can further improve the accuracy of diagnosing and managing patients. This study investigates the application of variable-frequency complex demodulation (VFCDM) and convolutional neural networks (CNN) to discriminate between healthy, interictal, and ictal states using electroencephalogram (EEG) data. For testing this approach, the EEG signals were collected from the publicly available Bonn dataset. A high-resolution time–frequency spectrum (TFS) of each EEG signal was obtained using the VFCDM. The TFS images were fed to the CNN classifier for the classification of the signals. The performance of CNN was evaluated using leave-one-subject-out cross-validation (LOSO CV). The TFS shows variations in its frequency for different states that correspond to variation in the neural activity. The LOSO CV approach yields a consistently high performance, ranging from 90% to 99% between different combinations of healthy and epilepsy states (interictal and ictal). The extensive LOSO CV validation approach ensures the reliability and robustness of the proposed method. As a result, the research contributes to advancing the field of epilepsy detection and brings us one step closer to developing practical, reliable, and efficient diagnostic tools for clinical applications.

Prediction Of Seizures In Patients With Temporal Lobe Epilepsy, More Than 30 Minutes Before Onset.

Dear Madam, Around 52.5 million people worldwide suffer from the neurological illness known as epilepsy, which is frequently characterized by recurring seizures. (1, 2) As epilepsy is a chronic disorder that can lead to cognitive deficits, sadness, and anxiety in sufferers, it has a considerable negative impact on individuals and society. The most common type of seizures, affecting around 61% of persons with epilepsy, are focal seizures, which raise the risk of injury and early death (2). One of the most common forms of focal epilepsy, temporal lobe epilepsy (TLE), is characterized by recurring spontaneous seizures that begin in the temporal lobe. (s). (3) In the last two decades, many new antiseizure medications (ASMs)have been developed; however, about 40% of people with epilepsy may be drug-resistant, meaning ASMs are unable to control their seizures; therefore, despite the advent of new treatments, a high unmet need remains unaddressed. (2) Surgery is an alternate treatment, however, it cannot be used if larger areas of the brain are affected, in such cases, neuromodulation is a novel and successful therapeutic tool developed. (4) Conventionally, neural activity in epilepsy is categorized into interictal, and post-ictal states. With the help of continuous EEG, researchers have subdivided interictal state into a pre-ictal and pro-ictal state in temporal lobe epilepsy patients (which is the typical drug-resistant focal epilepsy targeted for interventions such as neuromodulation). The pre-ictal states indicate imminent seizure onset, while pro-ictal states reflect changes in neural activity that create a propensity for seizures over longer periods. The EEG-based pro-ictal state detection is critical to adaptive neuromodulation, with early detection of seizure allowing electrodes to be applied therapeutically to the brain's seizure onset zone and thalamus enabling treatment to be modified as per seizure risk. (5) A prospective cohort study was conducted on adults with drug-resistant temporal lobe epilepsy at UTHealth Houston, in which pro-ictal state – i.e., pathologic brain activities during periods of heightened seizure risk could be detected up to one-half hour prior to seizure onset. A classifier was developed that could reasonably accurately distinguish between physiological and pathologic brain states in temporal lobe epilepsy. Thalamocortical dynamics were used to identify pro-ictal states in each patient. As the limbic thalamus exerts a diverse influence on cortex activity, thalamocortical EEG was used for pro-ictal state detection as opposed to previous paradigms derived solely from cortical structures. ---Continue

Quantitative EEG analysis in typical absence seizures: unveiling spectral dynamics and entropy patterns.

A typical absence seizure is a generalized epileptic event characterized by a sudden, brief alteration of consciousness that serves as a hallmark for various generalized epilepsy syndromes. Distinguishing between similar interictal and ictal electroencephalographic (EEG) epileptiform patterns poses a challenge. However, quantitative EEG, particularly spectral analysis focused on EEG rhythms, shows potential for differentiation. This study was designed to investigate discernible differences in EEG spectral dynamics and entropy patterns during the pre-ictal and post-ictal periods compared to the interictal state. We analyzed 20 EEG ictal patterns from 11 patients with confirmed typical absence seizures, and assessed recordings made during the pre-ictal, post-ictal, and interictal intervals. Power spectral density (PSD) was used for the quantitative analysis that focused on the delta, theta, alpha, and beta bands. In addition, we measured EEG signal regularity using approximate (ApEn) and multi-scale sample entropy (MSE). Findings demonstrate a significant increase in delta and theta power in the pre-ictal and post-ictal intervals compared to the interictal interval, especially in the posterior brain region. We also observed a notable decrease in entropy in the pre-ictal and post-ictal intervals, with a more pronounced effect in anterior brain regions. These results provide valuable information that can potentially aid in differentiating epileptiform patterns in typical absence seizures. The implications of our findings are promising for precision medicine approaches to epilepsy diagnoses and patient management. In conclusion, our quantitative analysis of EEG data suggests that PSD and entropy measures hold promise as potential biomarkers for distinguishing ictal from interictal epileptiform patterns in patients with confirmed or suspected typical absence seizures.

Working memory during spontaneous migraine attacks: an fMRI study

ObjectiveTo investigate the neural correlates of working memory during a spontaneous migraine attack compared to the interictal phase, using functional magnetic resonance imaging (fMRI).BackgroundCognitive disturbances are commonly observed during migraine attacks, particularly in the headache phase. However, the neural basis of these changes remains unknown.MethodsIn a fMRI within-subject test-retest design study, eleven women (32 years of age, average) with episodic migraine were evaluated twice, first during a spontaneous migraine attack, and again in a pain-free period. Each session consisted in a cognitive assessment and fMRI while performing a working memory task (N-back).ResultsCognitive test scores were lower during the ictal session than in the pain-free session. Regions typically associated with working memory were activated during the N-back task in both sessions. A voxel wise between session comparison showed significantly greater activation in the left frontal pole and orbitofrontal cortex during the attack relative to the interictal phase.ConclusionMigraine patients exhibited greater activation of the left frontal pole and orbitofrontal cortex while executing a verbal working memory task during a spontaneous migraine attack when compared to the interictal state. Given the association of these regions with pain processing and inhibitory control, these findings suggest that patients recruit inhibitory areas to accomplish the cognitive task during migraine attacks, a neural signature of their cognitive difficulties.

Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection.

Although it is clinically important, a reliable and economical solution to automatic seizure detection for patients at home is yet to be developed. Traditional algorithms rely on multi-channel EEG signals and features of canonical EEG power description. This study is aimed to propose an effective single-channel EEG seizure detection method centered on novel EEG power parameterization and channel selection algorithms.We employed the publicly available multi-channel CHB-MIT Scalp EEG database to gauge the effectiveness of our approach. We first adapted a power spectra parameterization algorithm to characterize the aperiodic and periodic components of the ictal and inter-ictal EEGs. We selected four features based on their statistical significance and interpretability, and developed a ranking approach to channel selection for each patient. We then tested the effectiveness of our approaches to channel and feature selection for automatic seizure detection using support vector machine (SVM) as the classifier. The performance of our algorithm was evaluated using five-fold cross-validation and compared to those methods of comparable complexity (using one or two channels of EEG), in terms of accuracy, specificity, sensitivity, precision and F1 score.Some channels of EEG signals show strikingly different distributions of PSD features between the ictal and inter-ictal states. Four features including the offset and exponent parameters for the aperiodic component and the first and second highest total power (TPW1 and TPW2) form the basis of channel selection and the input of SVM classifier. The selected channel is found to be patient-specific. Our approach has achieved a mean sensitivity of 95.6%, specificity of 99.2%, accuracy of 98.6%, precision of 95.5%, and F1 score of 95.5%. Compared with algorithms in previous studies that used one or two channels of EEG signals, ours outperforms in specificity and accuracy with comparable sensitivity.EEG power spectra parameterization to feature extraction and feature ranking-based channel selection are found to enable efficient and effective automatic seizure detection based on single-channel EEG signal.

Sensing with deep brain stimulation device in epilepsy: Aperiodic changes in thalamic local field potential during seizures.

Thalamic deep brain stimulation (DBS) is an effective therapeutic option in patients with drug-resistant epilepsy. Recent DBS devices with sensing capabilities enable chronic, outpatient local field potential (LFP) recordings. Whereas beta oscillations have been demonstrated to be a useful biomarker in movement disorders, the clinical utility of DBS sensing in epilepsy remains unclear. Our aim was to determine LFP features that distinguish ictal from inter-ictal states, which may aid in tracking seizure outcomes with DBS. Electrophysiology data were obtained from DBS devices implanted in the anterior nucleus (N = 12) or centromedian nucleus (N = 2) of the thalamus. Power spectra recorded during patient/caregiver-marked seizure events were analyzed with a method that quantitatively separates the oscillatory and non-oscillatory/aperiodic components of the LFP using non-parametric statistics, without the need for pre-specification of the frequency bands of interest. Features of the LFP parameterized using this algorithm were compared with those from inter-ictal power spectra recorded in clinic. Oscillatory activity in multiple canonical frequency bands was identified from the power spectra in 86.48% of patient-marked seizure events. Delta oscillations were present in all patients, followed by theta (N = 10) and beta (N = 9). Although there were no differences in oscillatory LFP features between the ictal and inter-ictal states, there was a steeper decline in the 1/f slope of the aperiodic component of the LFP during seizures. Our work highlights the potential and shortcomings of chronic LFP recordings in thalamic DBS for epilepsy. Findings suggest that no single frequency band in isolation clearly differentiates seizures, and that features of aperiodic LFP activity may be clinically-relevant biomarkers of seizures.

Epileptic Seizure Prediction Using Attention Augmented Convolutional Network.

Early seizure prediction is crucial for epilepsy patients to reduce accidental injuries and improve their quality of life. Identifying pre-ictal EEG from the inter-ictal state is particularly challenging due to their nonictal nature and remarkable similarities. In this study, a novel epileptic seizure prediction method is proposed based on multi-head attention (MHA) augmented convolutional neural network (CNN) to address the issue of CNN's limit of capturing global information of input signals. First, data enhancement is performed on original EEG recordings to balance the pre-ictal and inter-ictal EEG data, and the EEG recordings are sliced into 6-second-long EEG segments. Subsequently, EEG time-frequency distribution is obtained using Stockwell transform (ST), and the attention augmented convolutional network is employed for feature extraction and classification. Finally, post-processing is utilized to reduce the false prediction rate (FPR). The CHB-MIT EEG database was used to evaluate the system. The validation results showed a segment-based sensitivity of 98.24% and an event-based sensitivity of 94.78% with a FPR of 0.05/h were yielded, respectively. The satisfying results of the proposed method demonstrate its possible potential for clinical applications.

Identifying and troubleshooting the pitfalls of ictal/interictal brain perfusion SPECT studies.

Epilepsy is a prevalent condition, and surgical intervention can benefit patients with refractory seizures. Single photon emission computed tomography (SPECT) using 99mTc-HMPAO or 99mTc-ECD provides assessment of regional cerebral blood flow and is the primary non-invasive approach for imaging brain perfusion in ictal and interictal states. Ictal/interictal SPECT is valuable in localising epileptogenic foci, particularly when MRI and electroencephalography are negative. However, to obtain accurate images reflecting brain perfusion in both states, meticulous preparation of the patient, timely radiotracer injection and close coordination between neurology and nuclear medicine teams are essential. Tracers also have inherent limitations, and patients may present with coexisting brain pathologies for which coregistration of SPECT images with MRI is recommended to improve diagnostic accuracy. Inconclusive SPECT findings may require repeating the exam or considering additional investigations. A comprehensive approach, considering various factors, is crucial for accurate interpretation of SPECT studies in presurgical epilepsy evaluations. This article provides a summary of the organisation and key challenges involved in conducting ictal/interictal SPECT studies, covering the entire process from a patient's hospital arrival to the integration of results within their presurgical pathway and using our experience of 182 patients over 10 years.

A novel epilepsy seizure prediction model using deep learning and classification

Epilepsy is a common neurological disease where the earlier disease prediction significantly impacts those patients’ lives. In this paper, a novel epilepsy seizure prediction approach is designed using deep learning. The proposed model is applied to the Electroencephalogram (EEG) recordings collected from Children’s Hospital Boston (CHB-MIT). The recording data is grouped into 23 cases, including 17 females and five males of different ages. The recordings are sampled at 256 samples/s of 16-bit resolution. The target is to analyse the brain’s state and evaluate the changes encountered from the interictal state. The earlier prediction process helped in timely disease identification and treatment to rescue the patients. Feeding the raw EEG signals over the feature extractor reduces the computational complexity and execution time. An Adaptive Grey Wolf Optimizer (AGWO) is used for learning the features and promoting those discriminative features to enhance the prediction rate and classification accuracy. To optimize the features integrating the auto-encoder concept with Genetic Algorithm (GA) in an adaptive manner termed as to enhance the prediction rate. The functionality of is tested over the subjects of the CHB-MIT EEG dataset to achieve resourceful outcomes. The proposed attains higher accuracy of 99% and reduces the False Alarm Rate (FAR) with little prediction time. The model’s functionality is evaluated using the MATLAB simulation environment and shows a better trade-off than existing approaches.

Visual hypersensitivity in patients treated with anti-calcitonin gene-related peptide (receptor) monoclonal antibodies.

To evaluate the effect of treatment with anti-calcitonin gene-related peptide (CGRP; receptor) antibodies on visual hypersensitivity in patients with migraine. Increased visual sensitivity can be present both during and outside migraine attacks. CGRP has been demonstrated to play a key role in light-aversive behavior. In this prospective follow-up study, patients treated for migraine with erenumab (n = 105) or fremanezumab (n = 100) in the Leiden Headache Center were invited to complete a questionnaire on visual sensitivity (the Leiden Visual Sensitivity Scale [L-VISS]), pertaining to both their ictal and interictal state, before starting treatment (T0) and 3 months after treatment initiation (T1). Using a daily e-diary, treatment effectiveness was assessed in weeks 9-12 compared to a 4-week pre-treatment baseline period. L-VISS scores were compared between T0 and T1. Subsequently, the association between the reduction in L-VISS scores and the reduction in monthly migraine days (MMD) was investigated. At 3 months, the visual hypersensitivity decreased, with a decrease in mean ± standard deviation (SD) ictal L-VISS (from 20.1 ± 7.7 to 19.2 ± 8.1, p = 0.042) and a decrease in mean ± SD interictal L-VISS (from 11.8 ± 6.6 to 11.1 ± 7.0, p = 0.050). We found a positive association between the reduction in MMD and the decrease in interictal L-VISS (β = 0.2, p = 0.010) and the reduction in ictal L-VISS (β = 0.3, p = 0.001). A decrease in visual hypersensitivity in patients with migraine after treatment with anti-CGRP (receptor) antibodies is positively associated with clinical response on migraine.