Cluster-based spike detection algorithm adapts to interpatient and intrapatient variation in spike morphology

A data augmentation procedure to improve detection of spike ripples in brain voltage recordings

Epilepsy is a major neurological disorder characterized by recurrent, spontaneous seizures. For patients with drug-resistant epilepsy, treatments include neurostimulation or surgical removal of the epileptogenic zone (EZ), the brain region responsible for seizure generation. Precise targeting of the EZ requires reliable biomarkers. Spike ripples - high-frequency oscillations that co-occur with large amplitude epileptic discharges - have gained prominence as a candidate biomarker. However, spike ripple detection remains a challenge. The gold-standard approach requires an expert manually visualize and interpret brain voltage recordings, which limits reproducibility and high-throughput analysis. Addressing these limitations requires more objective, efficient, and automated methods for spike ripple detection, including approaches that utilize deep neural networks. Despite advancements, dataset heterogeneity and scarcity severely limit machine learning performance. Our study explores long-short term memory (LSTM) neural network architectures for spike ripple detection, leveraging data augmentation to improve classifier performance. We highlight the potential of combining training on augmented and in vivo data for enhanced spike ripple detection and ultimately improving diagnostic accuracy in epilepsy treatment.

Continuous Spike-Waves during Slow Sleep Today: An Update

In the context of childhood epilepsy, the concept of continuous spike-waves during slow sleep (CSWS) includes several childhood-onset heterogeneous conditions that share electroencephalograms (EEGs) characterized by a high frequency of paroxysmal abnormalities during sleep, which have negative effects on the cognitive development and behavior of the child. These negative effects may have the characteristics of a clear regression or of a slowdown in development. Seizures are very often present, but not constantly. The above makes it clear why CSWS have been included in epileptic encephalopathies, in which, by definition, frequent EEG paroxysmal abnormalities have an unfavorable impact on cognitive functions, including socio-communicative skills, causing autistic features, even regardless of the presence of clinically overt seizures. Although several decades have passed since the original descriptions of the electroclinical condition of CSWS, there are still many areas that are little-known and deserve to be further studied, including the EEG diagnostic criteria, the most effective electrophysiological parameter for monitoring the role of the thalamus in CSWS pathogenesis, its long-term evolution, the nosographic location of Landau-Kleffner syndrome, standardized neuropsychological and behavioral assessments, and pharmacological and non-pharmacological therapies.

Pitfalls in scalp EEG: Current obstacles and future directions

Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.

How the Spreading and Intensity of Interictal Epileptic Activity Are Associated with Visuo-Spatial Skills in Children with Self-Limited Focal Epilepsy with Centro-Temporal Spikes

This paper investigates brain-behaviour associations between interictal epileptic discharges and cognitive performance in a population of children with self-limited focal epilepsy with centro-temporal spikes (SeLECTS). Sixteen patients with SeLECTS underwent an extensive neuropsychological assessment, including verbal short-term and episodic memory, non-verbal short-term memory, attentional abilities and executive function. Two quantitative EEG indices were analysed, i.e., the Spike Wave Index (SWI) and the Spike Wave Frequency (SWF), and one qualitative EEG index, i.e., the EEG score, was used to evaluate the spreading of focal SW to other parts of the brain. We investigated associations between EEG indices and neuropsychological performance with non-parametric Spearman correlation analyses, including correction for multiple comparisons. The results showed a significant negative correlation between (i) the awake EEG score and the Block Tapping Test, a visuo-spatial short-term memory task, and (ii) the sleep SWI and the Tower of London, a visuo-spatial planning task (pcorr < 0.05). These findings suggest that, in addition to the usual quantitative EEG indices, the EEG analysis should include the qualitative EEG score evaluating the spreading of focal SW to other parts of the brain and that neuropsychological assessment should include visuo-spatial skills.

Graph neural networks in EEG spike detection

OBJECTIVE

This study develops new machine learning architectures that are more adept at detecting interictal epileptiform discharges (IEDs) in scalp EEG. A comparison of results using the average precision (AP) metric is made with the proposed models on two datasets obtained from Baptist Hospital of Miami and Temple University Hospital.

METHODS

Applying graph neural networks (GNNs) on functional connectivity (FC) maps of different frequency sub-bands to yield a novel architecture we call FC-GNN. Attention mechanism is applied on a complete graph to let the neural network select its important edges, hence bypassing the extraction of features, a model we refer to as CA-GNN.

RESULTS

On the Baptist Hospital dataset, the results were as follows: Vanilla Self-Attention →0.9029±0.0431, Hierarchical Attention →0.8546±0.0587, Vanilla Visual Geometry Group (VGG) →0.92±0.0618, Satelight →0.9219±0.046, FC-GNN →0.9731±0.0187, and CA-GNN →0.9788±0.0125. In the same order, the results on the Temple University Hospital dataset are 0.9692, 0.9113, 0.97, 0.9575, 0.963, and 0.9879.

CONCLUSION

Based on the good results they yield, GNNs prove to have a strong potential in detecting epileptogenic activity.

SIGNIFICANCE

This study opens the door for the discovery of the powerful role played by GNNs in capturing IEDs, which is an essential step for identifying the epileptogenic networks of the affected brain and hence improving the prospects for more accurate 3D source localization.

An improved BECT spike detection method with functional brain network features based on PLV

Background

Children with benign childhood epilepsy with centro-temporal spikes (BECT) have spikes, sharps, and composite waves on their electroencephalogram (EEG). It is necessary to detect spikes to diagnose BECT clinically. The template matching method can identify spikes effectively. However, due to the individual specificity, finding representative templates to detect spikes in actual applications is often challenging.

Purpose

This paper proposes a spike detection method using functional brain networks based on phase locking value (FBN-PLV) and deep learning.

Methods

To obtain high detection effect, this method uses a specific template matching method and the ‘peak-to-peak' phenomenon of montages to obtain a set of candidate spikes. With the set of candidate spikes, functional brain networks (FBN) are constructed based on phase locking value (PLV) to extract the features of the network structure during spike discharge with phase synchronization. Finally, the time domain features of the candidate spikes and the structural features of the FBN-PLV are input into the artificial neural network (ANN) to identify the spikes.

Results

Based on FBN-PLV and ANN, the EEG data sets of four BECT cases from the Children's Hospital, Zhejiang University School of Medicine are tested with the AC of 97.6%, SE of 98.3%, and SP 96.8%.

Epileptic seizure focus detection from interictal electroencephalogram: a survey

Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG signals in an automated manner to identify the epileptic seizure focus. To develop AI system for identifying the epileptic focus, there are many recently-published AI solutions based on biomarkers or statistic features that utilize interictal EEGs. In this review, we survey these solutions and find that they can be divided into three main categories: (i) those that use of biomarkers in EEG signals, including high-frequency oscillation, phase-amplitude coupling, and interictal epileptiform discharges, (ii) others that utilize feature-extraction methods, and (iii) solutions based upon neural networks (an end-to-end approach). We provide a detailed description of seizure focus with clinical diagnosis methods, a summary of the public datasets that seek to reduce the research gap in epilepsy, recent novel performance evaluation criteria used to evaluate the AI systems, and guidelines on when and how to use them. This review also suggests a number of future research challenges that must be overcome in order to design more efficient computer-aided solutions to epilepsy focus detection.

A Hybrid Expert System for Individualized Quantification of Electrical Status Epilepticus During Sleep Using Biogeography-Based Optimization

Electrical status epilepticus during sleep (ESES) is an epileptic encephalopathy in children with complex clinical manifestations. It is accompanied by specific electroencephalography (EEG) patterns of continuous spike and slow-waves. Quantifying such EEG patterns is critical to the diagnosis of ESES. While most of the existing automatic ESES quantification systems ignore the morphological variations of the signal as well as the individual variability among subjects. To address these issues, this paper presents a hybrid expert system that dedicates to mimicking the decision-making process of clinicians in ESES quantification by taking the morphological variations, individual variability, and medical knowledge into consideration. The proposed hybrid system not only offers a general scheme that could propel a semi-auto morphology analysis-based expert decision model to a fully automated ESES quantification with biogeography-based optimization (BBO), but also proposes a more precise individualized quantification system to involve the personalized characteristics by adopting an individualized parameters-selection framework. The feasibility and reliability of the proposed method are evaluated on a clinical dataset collected from twenty subjects at Children's Hospital of Fudan University, Shanghai, China. The estimation error for the individualized quantitative descriptor ESES is 0-4.32% and the average estimation error is 0.95% for all subjects. Experimental results show the presented system outperforms existing works and the individualized system significantly improves the performance of ESES quantification. The favorable results indicate that the proposed hybrid expert system for automatic ESES quantification is promising to support the diagnosis of ESES.

Chronic recording of the vagus nerve to analyze modulations by the light-dark cycle

OBJECTIVE

The vagus nerve is considered to play a key role in the circadian rhythm. Chronic continuous analysis of the vagus nerve activity could contribute to a better understanding of the role of the vagus nerve in light-dark modulations. This paper presents a continuous analysis of spontaneous vagus nerve activity performed in four rats.

APPROACH

We analyzed the vagus electroneurogram (VENG) and electroencephalogram (EEG) over a recording period of 28 days. Spike activity and heart rate estimation were derived from the VENG, and slow-wave activity was derived from the EEG. The presence of repetitive patterns was investigated with periodograms, cosinor fitting, autocorrelation, and statistical tests. The light-dark variations derived from the VENG spikes were compared with EEG slow waves, an established metric in circadian studies.

RESULTS

Our results demonstrate that light-dark variations can be detected in long-term vagus nerve activity monitoring. A recording period of about seven days is required to characterize accurately the VENG light-dark variations.

SIGNIFICANCE

As a major outcome of this study, vagus nerve recordings hold the promise to help understand circadian regulation.

Fast parametric curve matching (FPCM) for automatic spike detection

Epilepsy is a widely spread neurological disease, whose treatment often requires resection of the pathological cortical tissue. Interictal spike analysis observed in the non-invasively collected EEG or MEG data offers a way to localize epileptogenic cortical structures for surgery planning purposes. While a plethora of automatic spike detection techniques have been developed each with its own assumptions and limitations, non of them is ideal and the best results are achieved when the output of several automatic spike detectors are combined. This is especially true in the low signal-to-noise ratio conditions. To this end we propose a novel biomimetic approach for automatic spike detection based on a constrained mixed spline machinery that we dub as fast parametric curve matching (FPCM). Using the peak-wave shape parametrization, the constrained parametric morphological model is constructed and convolved with the observed multichannel data to very efficiently determine mixed spline parameters corresponding to each time-point in the dataset. Then the logical predicates that directly map to the expected interictal event morphology allow us to accomplish the spike detection task. The results of simulations mimicking typical low SNR scenario show the robustness and high ROC AUC values of the FPCM method as compared to the spike detection performed by the means of more conventional approaches such as wavelet decomposition, template matching or simple amplitude thresholding. Applied to the real MEG and EEG data from the human patients and to ECoG data from the rat, the FPCM technique demonstrates reliable detection of the interictal events and localization of epileptogenic zones concordant with independent conclusions made by the epileptologist. Since the FPCM is computationally light, tolerant to high amplitude artifacts and flexible to accommodate verbalized descriptions of the arbitrary target morphology, it may complement the existing arsenal of means for analysis of noisy interictal datasets.

SQNN: A Spike-wave index Quantification Neural Network with a pre-labeling algorithm for epileptiform activity identification and quantification in children

OBJECTIVE

Electrical status epilepticus during slow sleep (ESES) is a phenomenon identified by strong activation of epileptiform activity in the electroencephalogram (EEG) during sleep. For children disturbed by ESES, spike-wave index (SWI) is defined to quantify the epileptiform activity in the EEG during sleep. Accurate SWI quantification is important for clinical diagnosis and prognosis. To quantify SWI automatically, a deep learning method is proposed in this paper.

APPROACH

Firstly, a pre-labeling algorithm (PreLA) composed of the adaptive wavelet enhanced decomposition and a slow-wave discrimination rule is designed to efficiently label the EEG signal. It enables the collection of large-scale EEG dataset with fine-grained labels. Then, an SWI Quantification Neural Network (SQNN) is constructed to accurately classify each sample point as normal or abnormal and to identify the abnormal events. SWI can be calculated automatically based on the total duration of abnormalities and the length of the signal.

MAIN RESULTS

Experiments on two datasets demonstrate that the PreLA is effective and robust for labeling the EEG data and the SQNN accurately and reliably quantifies SWI without using any thresholds. The average estimation error of SWI is 3.12%, indicating that our method is more accurate and robust than experts and previous related works. The processing speed of SQNN is 100 times faster than that of experts.

SIGNIFICANCE

Deep learning provides a novel approach to automatic SWI quantification and PreLA provides an easy way to label the EEG data with ESES syndromes. The results of the experiments indicate that the proposed method has a high potential for clinical diagnosis and prognosis of epilepsy in children.

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.

BECT Spike Detection Based on Novel EEG Sequence Features and LSTM Algorithms

The benign epilepsy with spinous waves in the central temporal region (BECT) is the one of the most common epileptic syndromes in children, that seriously threaten the nervous system development of children. The most obvious feature of BECT is the existence of a large number of electroencephalogram (EEG) spikes in the Rolandic area during the interictal period, that is an important basis to assist neurologists in BECT diagnosis. With this regard, the paper proposes a novel BECT spike detection algorithm based on time domain EEG sequence features and the long short-term memory (LSTM) neural network. Three time domain sequence features, that can obviously characterize the spikes of BECT, are extracted for EEG representation. The synthetic minority oversampling technique (SMOTE) is applied to address the spike imbalance issue in EEGs, and the bi-directional LSTM (BiLSTM) is trained for spike detection. The algorithm is evaluated using the EEG data of 15 BECT patients recorded from the Children's Hospital, Zhejiang University School of Medicine (CHZU). The experiment shows that the proposed algorithm can obtained an average of 88.54% F1 score, 92.04% sensitivity, and 85.75% precision, that generally outperforms several state-of-the-art spike detection methods.

A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram

Objective.Interictal epileptiform discharges (IEDs) are an important and widely accepted biomarker used in the diagnosis of epilepsy based on scalp electroencephalography (EEG). Because the visual detection of IEDs has various limitations, including high time consumption and high subjectivity, a faster, more robust, and automated IED detector is strongly in demand.Approach.Based on deep learning, we proposed an end-to-end framework with multi-scale morphologic features in the time domain and correlation in sensor space to recognize IEDs from raw scalp EEG.Main Results.Based on a balanced dataset of 30 patients with epilepsy, the results of the five-fold (leave-6-patients-out) cross-validation shows that our model achieved state-of-the-art detection performance (accuracy: 0.951, precision: 0.973, sensitivity: 0.938, specificity: 0.968, F1 score: 0.954, AUC: 0.973). Furthermore, our model maintained excellent IED detection rates in an independent test on three datasets.Significance.The proposed model could be used to assist neurologists in clinical EEG interpretation of patients with epilepsy. Additionally, this approach combines multi-level output and correlation among EEG sensors and provides new ideas for epileptic biomarker detection in scalp EEG.

Machine learning for detection of interictal epileptiform discharges

The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased likelihood of seizures and are routinely assessed by visual analysis of the EEG. Visual assessment is, however, time consuming and prone to subjectivity, leading to a high misdiagnosis rate and motivating the development of automated approaches. Research towards automating IED detection started 45 years ago. Approaches range from mimetic methods to deep learning techniques. We review different approaches to IED detection, discussing their performance and limitations. Traditional machine learning and deep learning methods have yielded the best results so far and their application in the field is still growing. Standardization of datasets and outcome measures is necessary to compare models more objectively and decide which should be implemented in a clinical setting.

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.

Standard procedures for the diagnostic pathway of sleep-related epilepsies and comorbid sleep disorders: A European Academy of Neurology, European Sleep Research Society and International League against Epilepsy-Europe consensus review

BACKGROUND

Some epilepsy syndromes (sleep-related epilepsies [SRE]) have a strong link with sleep. Comorbid sleep disorders are common in patients with SRE and can exert a negative impact on seizure control and quality of life.

PURPOSES

To define the standard procedures for the diagnostic pathway of patients with possible SRE (scenario 1) and the general management of patients with SRE and comorbidity with sleep disorders (scenario 2).

METHODS

The project was conducted under the auspices of the European Academy of Neurology (EAN), the European Sleep Research Society (ESRS) and the International League against Epilepsy (ILAE) Europe. The framework of the document entailed the following phases: conception of the clinical scenarios; literature review; statements regarding the standard procedures. For literature search a step-wise approach starting from systematic reviews to primary studies was applied. Published studies were identified from the National Library of Medicine's MEDLINE database and Cochrane Library.

RESULTS

Scenario 1: despite a low quality of evidence, recommendations on anamnestic evaluation, tools for capturing the event at home or in the laboratory are provided for specific SRE. Scenario 2: Early diagnosis and treatment of sleep disorders (especially respiratory disorders) in patients with SRE are likely to be beneficial for seizures control.

CONCLUSIONS

Definitive procedures for evaluating patients with SRE are lacking. We provide advice that could be of help for standardising and improving the diagnostic approach of specific SRE. The importance of identifying and treating specific sleep disorders for the management and outcome of patients with SRE is underlined.

Standard procedures for the diagnostic pathway of sleep-related epilepsies and comorbid sleep disorders: an EAN, ESRS and ILAE-Europe consensus review

BACKGROUND AND PURPOSE

Some epilepsy syndromes (sleep-related epilepsies, SREs) have a strong link with sleep. Comorbid sleep disorders are common in patients with SRE and can exert a negative impact on seizure control and quality of life. Our purpose was to define the standard procedures for the diagnostic pathway of patients with possible SRE (scenario 1) and the general management of patients with SRE and comorbidity with sleep disorders (scenario 2).

METHODS

The project was conducted under the auspices of the European Academy of Neurology, the European Sleep Research Society and the International League Against Epilepsy Europe. The framework entailed the following phases: conception of the clinical scenarios; literature review; statements regarding the standard procedures. For the literature search a stepwise approach starting from systematic reviews to primary studies was applied. Published studies were identified from the National Library of Medicine's MEDLINE database and Cochrane Library.

RESULTS

Scenario 1: Despite a low quality of evidence, recommendations on anamnestic evaluation and tools for capturing the event at home or in the laboratory are provided for specific SREs. Scenario 2: Early diagnosis and treatment of sleep disorders (especially respiratory disorders) in patients with SRE are likely to be beneficial for seizure control.

CONCLUSIONS

Definitive procedures for evaluating patients with SRE are lacking. Advice is provided that could be of help for standardizing and improving the diagnostic approach of specific SREs. The importance of identifying and treating specific sleep disorders for the management and outcome of patients with SRE is underlined.

[Epileptiform activity index for the evaluation of treatment efficacy in patients with epileptic encephalopathy]

Continuous spike and waves during sleep (CSWS) may lead to different cognitive, motor and behavioral symptoms. CSWS is characterized by an electroencephalography (EEG) pattern of electrical status epilepticus during sleep (ESES). The article reviews the main criteria of ESES, localization of EEG-changes and main EEG-patterns. An analysis of abnormal neuroimaging findings in patients with ESES was performed. The main pathophysiologic theories of ESES pattern formation are reviewed. ESES diagnosis is usually based on rough visual assessment of the number of spikes and waves, but other methods of assessment are also utilized, including spike-wave index (SWI), which is widely used in clinical practice. SWI reflects the percentage of sleep covered with spikes and waves. Two different strategies of SWI calculation are used, but none of them is officially validated. Moreover, there is no consensus on the period of sleep to be used for the calculation of SWI. The article presents a detailed review of calculation and utilization of SWI. It is concluded that further research is needed to develop an optimal algorithm of SWI calculation.

Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy

BACKGROUND

Interictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chronic intracranial monitoring. Manual detection of these pathophysiological events is cumbersome, but is still superior to current rule-based approaches in most automated algorithms.

OBJECTIVE

To develop an unsupervised machine-learning algorithm for the improved, automated detection and localization of interictal epileptiform discharges based on spatiotemporal pattern recognition.

METHODS

We decomposed 24 h of intracranial electroencephalography signals into basis functions and activation vectors using non-negative matrix factorization (NNMF). Thresholding the activation vector and the basis function of interest detected interictal epileptiform discharges in time and space (specific electrodes), respectively. We used convolutive NNMF, a refined algorithm, to add a temporal dimension to basis functions.

RESULTS

The receiver operating characteristics for NNMF-based detection are close to the gold standard of human visual-based detection and superior to currently available alternative automated approaches (93% sensitivity and 97% specificity). The algorithm successfully identified thousands of interictal epileptiform discharges across a full day of neurophysiological recording and accurately summarized their localization into a single map. Adding a temporal window allowed for visualization of the archetypal propagation network of these epileptiform discharges.

CONCLUSION

Unsupervised learning offers a powerful approach towards automated identification of recurrent pathological neurophysiological signals, which may have important implications for precise, quantitative, and individualized evaluation of focal epilepsy.

Semi-automated EEG Enhancement Improves Localization of Ictal Onset Zone With EEG-Correlated fMRI

Objective: To improve the accuracy of detecting the ictal onset zone, we propose to enhance the epilepsy-related activity present in the EEG signals, before mapping their BOLD correlates through EEG-correlated fMRI analysis. Methods: Based solely on a segmentation of interictal epileptic discharges (IEDs) on the EEG, we train multi-channel Wiener filters (MWF) which enhance IED-like waveforms, and suppress background activity and noisy influences. Subsequently, we use EEG-correlated fMRI to find the brain regions in which the BOLD signal fluctuation corresponds to the filtered signals' time-varying power (after convolving with the hemodynamic response function), and validate the identified regions by quantitatively comparing them to ground-truth maps of the (resected or hypothesized) ictal onset zone. We validate the performance of this novel predictor vs. that of commonly used unitary or power-weighted predictors and a recently introduced connectivity-based metric, on a cohort of 12 patients with refractory epilepsy. Results: The novel predictor, derived from the filtered EEG signals, allowed the detection of the ictal onset zone in a larger percentage of epileptic patients (92% vs. at most 83% for the other predictors), and with higher statistical significance, compared to existing predictors. At the same time, the new method maintains maximal specificity by not producing false positive activations in healthy controls. Significance: The findings of this study advocate for the use of the MWF to maximize the signal-to-noise ratio of IED-like events in the interictal EEG, and subsequently use time-varying power as a sensitive predictor of the BOLD signal, to localize the ictal onset zone.

A fast machine learning approach to facilitate the detection of interictal epileptiform discharges in the scalp electroencephalogram

BACKGROUND

Finding interictal epileptiform discharges (IEDs) in the EEG is a part of diagnosing epilepsy. Automated software for annotating EEGs of patients with suspected epilepsy can therefore help with reaching a diagnosis. A large amount of data is required for training and evaluating an effective IED detection system. IEDs occur infrequently in the most patients' EEG, therefore, interictal EEG recordings contain mostly background waveforms.

NEW METHOD

As the first step to detect IEDs, we propose a machine learning technique eliminating most EEG background data using an ensemble of simple fast classifiers based on several EEG features. This could save computation time for an IED detection method, allowing the remaining waveforms to be classified by more computationally intensive methods. We consider several efficient features and reject background by applying thresholds on them in consecutive steps.

RESULTS

We applied the proposed algorithm on a dataset of 156 EEGs (93 and 63 with and without IEDs, respectively). We were able to eliminate 78% of background waveforms while retaining 97% of IEDs on our cross-validated dataset.

COMPARISON WITH EXISTING METHODS

We applied support vector machine, k-nearest neighbours, and random forest classifiers to detect IEDs with and without initial background rejection. Results show that rejecting background by our proposed method speeds up the overall classification by a factor ranging from 1.8 to 4.7 for the considered classifiers.

CONCLUSIONS

The proposed method successfully reduces computation time of an IED detection system. Therefore, it is beneficial in speeding up IED detection especially when utilizing large EEG datasets.

Automated epileptic seizure detection based on break of excitation/inhibition balance

Physiological models are attractive for seizure detection, as their parameters are related to physiological meanings. We propose an algorithm to early detect epileptic seizures based on automatic estimation of average synaptic gains (excitatory Ae, slow and fast inhibitory B and G) by combining clinical data with a neural mass model. Three indices (Ae/B, Ae/G and Ae/(B + G)), all related to excitation/inhibition balance, were calculated and used as cues to detect seizures. A simple thresholding method was employed. We evaluated the algorithm against the manual scoring of a human expert on intracranial EEG samples from 23 patients suffering from different types of epilepsy. Best performance was achieved using Ae/(B + G) as a cue, i.e. excitation/(slow + fast) inhibition, on temporal lobe epilepsy (TLE) patients. A leave-one-out cross-validation showed that the algorithm achieved 92.98% sensitivity for TLE patients. The median false positive rate was 0.16 per hour, and median detection delay was 14.5 s. Of interest, the threshold values determined by a leave-one-out cross-validation did nearly not vary among TLE patients, suggesting a general excitation/inhibition balance baseline in TLE patients. The same approach could be used with other types of epilepsy by adapting the neural mass model to these types.

A predictive epilepsy index based on probabilistic classification of interictal spike waveforms

Quantification of interictal spikes in EEG may provide insight on epilepsy disease burden, but manual quantification of spikes is time-consuming and subject to bias. We present a probability-based, automated method for the classification and quantification of interictal events, using EEG data from kainate- and saline-injected mice (C57BL/6J background) several weeks post-treatment. We first detected high-amplitude events, then projected event waveforms into Principal Components space and identified clusters of spike morphologies using a Gaussian Mixture Model. We calculated the odds-ratio of events from kainate- versus saline-treated mice within each cluster, converted these values to probability scores, P(kainate), and calculated an Hourly Epilepsy Index for each animal by summing the probabilities for events where the cluster P(kainate) > 0.5 and dividing the resultant sum by the record duration. This Index is predictive of whether an animal received an epileptogenic treatment (i.e., kainate), even if a seizure was never observed. We applied this method to an out-of-sample dataset to assess epileptiform spike morphologies in five kainate mice monitored for ~1 month. The magnitude of the Index increased over time in a subset of animals and revealed changes in the prevalence of epileptiform (P(kainate) > 0.5) spike morphologies. Importantly, in both data sets, animals that had electrographic seizures also had a high Index. This analysis is fast, unbiased, and provides information regarding the salience of spike morphologies for disease progression. Future refinement will allow a better understanding of the definition of interictal spikes in quantitative and unambiguous terms.

CLASSIFIER CASCADE TO AID IN DETECTION OF EPILEPTIFORM TRANSIENTS IN INTERICTAL EEG

The presence of Epileptiform Transients (ET) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. Automated ET detection can increase the uniformity and speed of ET detection. Current ET detection methods suffer from insufficient precision and high false positive rates. Since ETs occur infrequently in the EEG of most patients, the majority of recordings comprise background EEG waveforms. In this work we establish a method to exclude as much background data as possible from EEG recordings by applying a classifier cascade. The remaining data can then be classified using other ET detection methods. We compare a single Support Vector Machine (SVM) to a cascade of SVMs for detecting ETs. Our results show that the precision and false positive rate improve significantly by incorporating a classifier cascade before ET detection. Our method can help improve the precision and false positive rate of an ET detection system. At a fixed sensitivity, we were able to improve precision by 6.78%; and at a fixed false positive rate, the sensitivity improved by 2.83%.

Patient-specific seizure detection in long-term EEG using signal-derived empirical mode decomposition (EMD)-based dictionary approach

OBJECTIVE

The objective of the work described in this paper is the development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings.

APPROACH

A novel patient-specific seizure detection approach based on a signal-derived empirical mode decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches.

MAIN RESULTS

The performance of the proposed approach is validated using the CHB-MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability of the approach for seizure detection in long-term multi-channel EEG recordings is discussed.

SIGNIFICANCE

The proposed approach describes a computationally efficient method for automatic seizure detection in long-term multi-channel EEG recordings. The method does not rely on hand-engineered features, as are required in traditional approaches. Furthermore, the approach is suitable for scenarios where the dictionary once formed and trained can be used for automatic seizure detection of newly recorded data, making the approach suitable for long-term multi-channel EEG recordings.

Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution

OBJECTIVE

In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG).

APPROACH

We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold.

MAIN RESULTS

Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6  ±  36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time.

SIGNIFICANCE

Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.

DeepIED: An epileptic discharge detector for EEG-fMRI based on deep learning

Presurgical evaluation that can precisely delineate the epileptogenic zone (EZ) is one important step for successful surgical resection treatment of refractory epilepsy patients. The noninvasive EEG-fMRI recording technique combined with general linear model (GLM) analysis is considered an important tool for estimating the EZ. However, the manual marking of interictal epileptic discharges (IEDs) needed in this analysis is challenging and time-consuming because the quality of the EEG recorded inside the scanner is greatly deteriorated compared to the usual EEG obtained outside the scanner. This is one of main impediments to the widespread use of EEG-fMRI in epilepsy. We propose a deep learning based semi-automatic IED detector that can find the candidate IEDs in the EEG recorded inside the scanner which resemble sample IEDs marked in the EEG recorded outside the scanner. The manual marking burden is greatly reduced as the expert need only edit candidate IEDs. The model is trained on data from 30 patients. Validation of IEDs detection accuracy on another 37 consecutive patients shows our method can improve the median sensitivity from 50.0% for the previously proposed template-based method to 84.2%, with false positive rate as 5 events/min. Reproducibility validation on 15 patients is applied to evaluate if our method can produce similar hemodynamic response maps compared with the manual marking ground truth results. We explore the concordance between the maximum hemodynamic response and the intracerebral EEG defined EZ and find that both methods produce similar percentage of concordance (76.9%, 10 out of 13 patients, electrode was absent in the maximum hemodynamic response in two patients). This tool will make EEG-fMRI analysis more practical for clinical usage.

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A deep learning based epileptic discharge detector for EEG-fMRI is proposed.

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The burden of manually marking epileptic discharges is greatly reduced.

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Our method can produce similar EEG-fMRI results compared with traditional method.

Fast spike detection in EEG using eigenvalue analysis and clustering of spatial amplitude distribution

In the current study, we tested a proposed method for fast spike detection using a general-purpose computer. First, we performed eigenvalue analysis using a gradient calculated from two neighboring samples to detect high-amplitude negative peaks. Clustering was performed to classify detected peaks by considering amplitude distribution at scalp electrodes. Negative peaks were scored by considering electrodes in the detection process and the cluster to which each peak belonged. Spikes were detected using two parameters: score threshold, and the number of clusters. We then used precision and recall to eliminate overestimation of the performance of the method. The results revealed a tradeoff between precision and recall. Recall showed a maximum average value of 0.90 in two subjects. In contrast, average precision was 0.21, and the false positive rate was almost four times higher than the true positive rate on the condition that 64 and 54 spikes were included in two subjects. Analysis of required processing time revealed that our method could complete spike detection in approximately one-eighth of the recording time.

Localizing value of electrical source imaging: Frontal lobe, malformations of cortical development and negative MRI related epilepsies are the best candidates

OBJECTIVE

We aimed to prospectively assess the anatomical concordance of electric source localizations of interictal discharges with the epileptogenic zone (EZ) estimated by stereo-electroencephalography (SEEG) according to different subgroups: the type of epilepsy, the presence of a structural MRI lesion, the aetiology and the depth of the EZ.

METHODS

In a prospective multicentric observational study, we enrolled 85 consecutive patients undergoing pre-surgical SEEG investigation for focal drug-resistant epilepsy. Electric source imaging (ESI) was performed before SEEG. Source localizations were obtained from dipolar and distributed source methods. Anatomical concordance between ESI and EZ was defined according to 36 predefined sublobar regions. ESI was interpreted blinded to- and subsequently compared with SEEG estimated EZ.

RESULTS

74 patients were finally analyzed. 38 patients had temporal and 36 extra-temporal lobe epilepsy. MRI was positive in 52. 41 patients had malformation of cortical development (MCD), 33 had another or an unknown aetiology. EZ was medial in 27, lateral in 13, and medio-lateral in 34. In the overall cohort, ESI completely or partly localized the EZ in 85%: full concordance in 13 cases and partial concordance in 50 cases. The rate of ESI full concordance with EZ was significantly higher in (i) frontal lobe epilepsy (46%; p = 0.05), (ii) cases of negative MRI (36%; p = 0.01) and (iii) MCD (27%; p = 0.03). The rate of ESI full concordance with EZ was not statistically different according to the depth of the EZ.

SIGNIFICANCE

We prospectively demonstrated that ESI more accurately estimated the EZ in subgroups of patients who are often the most difficult cases in epilepsy surgery: frontal lobe epilepsy, negative MRI and the presence of MCD.

Automated epileptiform spike detection via affinity propagation-based template matching

Interictal epileptiform spikes are the key diagnostic biomarkers for epilepsy. The clinical gold standard of spike detection is visual inspection performed by neurologists. This is a tedious, time-consuming, and expert-centered process. The development of automated spike detection systems is necessary in order to provide a faster and more reliable diagnosis of epilepsy. In this paper, we propose an efficient template matching spike detector based on a combination of spike and background waveform templates. We generate a template library by clustering a collection of spikes and background waveforms extracted from a database of 50 patients with epilepsy. We benchmark the performance of five clustering techniques based on the receiver operating characteristic (ROC) curves. In addition, background templates are integrated with existing spike templates to improve the overall performance. The affinity propagation-based template matching system with a combination of spike and background templates is shown to outperform the other four conventional methods with the highest area-under-curve (AUC) of 0.953.

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.

CLUSTERING OF INTERICTAL SPIKES BY DYNAMIC TIME WARPING AND AFFINITY PROPAGATION

Epilepsy is often associated with the presence of spikes in electroencephalograms (EEGs). The spike waveforms vary vastly among epilepsy patients, and also for the same patient across time. In order to develop semi-automated and automated methods for detecting spikes, it is crucial to obtain a better understanding of the various spike shapes. In this paper, we develop several approaches to extract exemplars of spikes. We generate spike exemplars by applying clustering algorithms to a database of spikes from 12 patients. As similarity measures for clustering, we consider the Euclidean distance and Dynamic Time Warping (DTW). We assess two clustering algorithms, namely, K-means clustering and affinity propagation. The clustering methods are compared based on the mean squared error, and the similarity measures are assessed based on the number of generated spike clusters. Affinity propagation with DTW is shown to be the best combination for clustering epileptic spikes, since it generates fewer spike templates and does not require to pre-specify the number of spike templates.

FAST AND EFFICIENT REJECTION OF BACKGROUND WAVEFORMS IN INTERICTAL EEG

Automated annotation of electroencephalograms (EEG) of epileptic patients is important in diagnosis and management of epilepsy. Epilepsy is often associated with the presence of epileptiform transients (ET) in the EEG. To develop an efficient ET detector, a vast amount of data is required to train and evaluate the performance of the detector. Interictal EEG data contains mostly background waveforms, since ETs only occur occasionally in most patients. In order to detect ETs in an automated fashion, it is meaningful to first try to eliminate most background waveforms by means of simple, fast classifiers. The remaining waveforms can in a following step be processed by more sophisticated and computationally demanding classification algorithms, such as deep learning systems. In this study, we design a cascade of simple thresholding steps to reject most background waveforms in interictal EEG, while maintaining most ETs. Several simple and quick-to-compute EEG features are chosen. By thresholding these features in consecutive steps, background waveforms are rejected sequentially. In our numerical experiments, a cascade of 10 steps is able to reject 98.65% of all background segments in the dataset, while preserving 90.6% of the ETs.

Impaired sleep-related consolidation of declarative memories in idiopathic focal epilepsies of childhood

OBJECTIVE

Declarative memory is consolidated during sleep in healthy children. We tested the hypothesis that consolidation processes are impaired in idiopathic focal epilepsies (IFE) of childhood in association with frequent interictal epileptiform discharges (IEDs) during sleep.

METHODS

A verbal (word-pair association) and a nonverbal (2D object location) declarative memory task were administrated to 15 children with IFEs and 8 control children 6-12 years of age. Patients had either centrotemporal (11 patients) or occipital (4 patients) IEDs. All but 3 patients had a history of unprovoked seizures, and 6 of them were treated with valproate (VPA). The learning procedure (location of object pairs presented on a grid; association of word pairs) was executed in the evening. Retrieval was tested immediately after learning and on the next morning after a night of sleep. Participants were tested twice, once in natural home conditions and one month later in the unfamiliar conditions of the sleep unit under EEG monitoring.

RESULTS

Overnight recall performance was lower in children with IFE than in control children on both tasks (ps<0.05). Performance in home conditions was similar to that in hospital conditions. Higher spike-wave index (SWI) during nonrapid eye movement (NREM) sleep was associated with poorer performance in the nonverbal task (p<0.05). Valproate treatment was not associated with overnight recall performance for both tasks (ps>0.05).

CONCLUSION

Memory consolidation is impaired in IFE of childhood. The association between higher SWI during NREM sleep and poorer nonverbal declarative memory consolidation supports the hypothesis that interictal epileptic activity could disrupt sleep memory consolidation.

A reliable and time-saving semiautomatic spike-template-based analysis of interictal EEG-fMRI

OBJECTIVE

A prerequisite for the implementation of interictal electroencephalography-correlated functional magnetic resonance imaging (EEG-fMRI) in the presurgical work-up for epilepsy surgery is straightforward processing. We propose a new semi-automatic method as alternative for the challenging and time-consuming visual spike identification.

METHODS

Our method starts from a patient-specific spike-template, built by averaging spikes recorded on the EEG outside the scanner. Spatiotemporal cross-correlations between the template and the EEG measured during fMRI were calculated. To minimize false-positive detections, this time course of cross-correlations was binarized by means of a spike-template-specific threshold determined in healthy controls. To inform our model for statistical parametric mapping, this binarized regressor was convolved with the canonical hemodynamic response function. We validated our "template-based" method in 21 adult patients with refractory focal epilepsy with a well-defined epileptogenic zone and interictal spikes during EEG-fMRI. Sensitivity and specificity for detecting the epileptogenic zone were calculated and represented in receiver operating characteristic (ROC) curves. Our approach was compared with a previously proposed semiautomatic "topography-based" method that used the topographic amplitude distribution of spikes as a starting point for correlation-based fitting.

RESULTS

Good diagnostic performance could be reached with our template-based method. The optimal area under the ROC curve was 0.77. Diagnostic performance of the topography-based method was overall low.

SIGNIFICANCE

Our new template-based method is more standardized and time-saving than visual spike identification on intra-scanner EEG recordings, and preserves good diagnostic performance for detecting the epileptogenic zone.

A self-adapting system for the automated detection of inter-ictal epileptiform discharges

PURPOSE

Scalp EEG remains the standard clinical procedure for the diagnosis of epilepsy. Manual detection of inter-ictal epileptiform discharges (IEDs) is slow and cumbersome, and few automated methods are used to assist in practice. This is mostly due to low sensitivities, high false positive rates, or a lack of trust in the automated method. In this study we aim to find a solution that will make computer assisted detection more efficient than conventional methods, while preserving the detection certainty of a manual search.

METHODS

Our solution consists of two phases. First, a detection phase finds all events similar to epileptiform activity by using a large database of template waveforms. Individual template detections are combined to form "IED nominations", each with a corresponding certainty value based on the reliability of their contributing templates. The second phase uses the ten nominations with highest certainty and presents them to the reviewer one by one for confirmation. Confirmations are used to update certainty values of the remaining nominations, and another iteration is performed where ten nominations with the highest certainty are presented. This continues until the reviewer is satisfied with what has been seen. Reviewer feedback is also used to update template accuracies globally and improve future detections.

KEY FINDINGS

Using the described method and fifteen evaluation EEGs (241 IEDs), one third of all inter-ictal events were shown after one iteration, half after two iterations, and 74%, 90%, and 95% after 5, 10 and 15 iterations respectively. Reviewing fifteen iterations for the 20-30 min recordings 1 took approximately 5 min.

SIGNIFICANCE

The proposed method shows a practical approach for combining automated detection with visual searching for inter-ictal epileptiform activity. Further evaluation is needed to verify its clinical feasibility and measure the added value it presents.

Measuring spike strength in patients with continuous spikes and waves during sleep: comparison of methods for prospective use as a clinical index

OBJECTIVE

To compare methods of estimating spike strength as a potential index in the assessment of continuous spikes and waves during sleep (CSWS).

METHODS

Spikes were searched and averaged automatically from pre- and postoperative EEGs of ten patients with CSWS who underwent corpus callosotomy (eight) or resective epilepsy surgery (two). From the most prominent spike, we measured peak amplitude and root mean square (RMS) over ±150ms window around the peak. In order to compensate for spatiotemporal instability of spikes, the cumulative amplitude and RMS were computed from the highest quartile of electrodes (Ampl-Q and RMS-Q, respectively). The stability of parameters was studied by comparing two ten minute epochs during the first hour of NREM sleep, as well as by analyzing overnight variation of indices in further ten patients with CSWS. The Ampl-Q and RMS-Q were compared between pre- and postoperative recordings.

RESULTS

All four measures, amplitude, RMS, Ampl-Q and RMS-Q, were correlated with each other and highly dependent on NREM/REM-sleep stage and arousals. Expectedly, Ampl-Q and RMS-Q had the greatest intra-individual stability. The amplitude had up to 71% intra-individual variation making it unhelpful for clinical use. Ampl-Q and RMS-Q were comparable in assessing change following surgical treatment.

CONCLUSIONS

Computing an integrated RMS over multiple electrodes during steady NREM sleep offers a stable and reliable parameter for evaluating the strength of spikes in CSWS.

SIGNIFICANCE

Analyzing spike strength with RMS-Q may offer a clinically useful, supplementary index for EEG monitoring of CSWS where spike index has been of limited value.

Clinical value of magnetoencephalographic spike propagation represented by spatiotemporal source analysis: correlation with surgical outcome

OBJECTIVE

To investigate the correlation between spike propagation represented by spatiotemporal source analysis of magnetoencephalographic (MEG) spikes and surgical outcome in patients with temporal lobe epilepsy.

METHODS

Thirty-seven patients were divided into mesial (n=27) and non-mesial (n=10) groups based on the presurgical evaluation. In each patient, ten ipsilateral spikes were averaged, and spatiotemporal source maps of the averaged spike were obtained by using minimum norm estimate. Regions of interest (ROIs) were created including temporoparietal, inferior frontal, mesial temporal, anterior and posterior part of the lateral temporal cortex. We extracted activation values from the source maps and the threshold was set at half of the maximum activation at the peak latency. The leading and propagated areas of the spike were defined as those ROIs with activation reaching the threshold at the earliest and at the peak latencies, respectively. Surgical outcome was assessed based on Engel's classification. Binary variables were created from leading areas (restricted to the anterior and mesial temporal ROIs or not) and from propagation areas (involving the temporoparietal ROI or not), and for surgical outcome (Class I or not). Fisher's exact test was used for significance testing.

RESULTS

In total and mesial group, restricted anterior/mesial temporal leading areas were correlated with Class I (p<0.05). Temporoparietal propagation was correlated with Class II-IV (p<0.05). For the non-mesial group, no significant relation was found.

CONCLUSIONS

Spike propagation patterns represented by spatiotemporal source analysis of MEG spikes may provide useful information for prognostic implication in presurgical evaluation of epilepsy.

Mobile EEG in epilepsy

The sensitivity of routine EEG recordings for interictal epileptiform discharges in epilepsy is limited. In some patients, inpatient video-EEG may be performed to increase the likelihood of finding abnormalities. Although many agree that home EEG recordings may provide a cost-effective alternative to these recordings, their use is still not introduced everywhere. We surveyed Dutch neurologists and patients and evaluated a novel mobile EEG device (Mobita, TMSi). Key specifications were compared with three other current mobile EEG devices. We shortly discuss algorithms to assist in the review process. Thirty percent (33 out of 109) of Dutch neurologists reported that home EEG recordings are used in their hospital. The majority of neurologists think that mobile EEG can have additional value in investigation of unclear paroxysms, but not in the initial diagnosis after a first seizure. Poor electrode contacts and signal quality, limited recording time and absence of software for reliable and effective assistance in the interpretation of EEGs have been important constraints for usage, but in recent devices discussed here, many of these problems have been solved. The majority of our patients were satisfied with the home EEG procedure and did not think that our EEG device was uncomfortable to wear, but they did feel uneasy wearing it in public.