EEG Experts Research Articles

What Do You See? Signature Pedagogy in Continuous Electroencephalography Teaching.

Electroencephalography (EEG) is commonly used in neurology, but there is variability in how neurologists interpret EEGs, potentially from variability in EEG teaching. Little is known about how EEG teaching is done to prepare neurologists for professional practice. We interviewed a group of EEG experts to characterize their teaching practices around continuous EEG (cEEG). We used signature pedagogy as a framework to analyze and interpret the data. We identified pervasive and characteristic forms of cEEG teaching. Teaching is based on apprenticeship, relying on "learning by doing" in the context of real-life clinical practice. There are habitual steps that learners take to anchor teaching, which typically occurs during rounds. There is a common language and core knowledge that trainees need to master early in their training. There are pervasive characteristic forms of cEEG teaching. These findings can help facilitate instructional design and implementation of complementary or enhanced cEEG teaching practices.

Expert accuracy and inter-rater agreement of "must-know" EEG findings for adult and child neurology residents.

We published a list of "must-know" routine EEG (rEEG) findings for trainees based on expert opinion. Here, we studied the accuracy and inter-rater agreement (IRA) of these "must-know" rEEG findings among international experts. A previously validated online rEEG examination was disseminated to EEG experts. It consisted of a survey and 30 multiple-choice questions predicated on the previously published "must-know" rEEG findings divided into four domains: normal, abnormal, normal variants, and artifacts. Questions contained de-identified 10-20-s epochs of EEG that were considered unequivocal examples by five EEG experts. The examination was completed by 258 international EEG experts. Overall mean accuracy and IRA (AC1) were 81% and substantial (0.632), respectively. The domain-specific mean accuracies and IRA were: 76%, moderate (0.558) (normal); 78%, moderate (0.575) (abnormal); 85%, substantial (0.678) (normal variants); 85%, substantial (0.740) (artifacts). Academic experts had a higher accuracy than private practice experts (82% vs. 77%; p = .035). Country-specific overall mean accuracies and IRA were: 92%, almost perfect (0.836) (U.S.); 86%, substantial (0.762) (Brazil); 79%, substantial (0.646) (Italy); and 72%, moderate (0.496) (India). In conclusion, collective expert accuracy and IRA of "must-know" rEEG findings are suboptimal and heterogeneous. We recommend the development and implementation of pragmatic, accessible, country-specific ways to measure and improve the expert accuracy and IRA.

Association Between EEG Patterns and Serum Neurofilament Light After Cardiac Arrest: A Post Hoc Analysis of the TTM Trial.

Background and ObjectivesEEG is widely used for prediction of neurologic outcome after cardiac arrest. To better understand the relationship between EEG and neuronal injury, we explored the association between EEG and neurofilament light (NfL) as a marker of neuroaxonal injury, evaluated whether highly malignant EEG patterns are reflected by high NfL levels, and explored the association of EEG backgrounds and EEG discharges with NfL.MethodsWe performed a post hoc analysis of the Target Temperature Management After Out-of-Hospital Cardiac Arrest trial. Routine EEGs were prospectively performed after the temperature intervention ≥36 hours postarrest. Patients who awoke or died prior to 36 hours postarrest were excluded. EEG experts blinded to clinical information classified EEG background, amount of discharges, and highly malignant EEG patterns according to the standardized American Clinical Neurophysiology Society terminology. Prospectively collected serum samples were analyzed for NfL after trial completion. The highest available concentration at 48 or 72 hours postarrest was used.ResultsA total of 262/939 patients with EEG and NfL data were included. Patients with highly malignant EEG patterns had 2.9 times higher NfL levels than patients with malignant patterns and NfL levels were 13 times higher in patients with malignant patterns than those with benign patterns (95% CI 1.4–6.1 and 6.5–26.2, respectively; effect size 0.47; p < 0.001). Both background and the amount of discharges were independently strongly associated with NfL levels (p < 0.001). The EEG background had a stronger association with NfL levels than EEG discharges (R2 = 0.30 and R2 = 0.10, respectively). NfL levels in patients with a continuous background were lower than for any other background (95% CI for discontinuous, burst-suppression, and suppression, respectively: 2.26–18.06, 3.91–41.71, and 5.74–41.74; effect size 0.30; p < 0.001 for all). NfL levels did not differ between suppression and burst suppression. Superimposed discharges were only associated with higher NfL levels if the EEG background was continuous.DiscussionBenign, malignant, and highly malignant EEG patterns reflect the extent of brain injury as measured by NfL in serum. The extent of brain injury is more strongly related to the EEG background than superimposed discharges. Combining EEG and NfL may be useful to better identify patients misclassified by single methods.Trial Registration InformationClinicalTrials.gov NCT01020916.

Quantitative EEG-Based Seizure Estimation in Super-Refractory Status Epilepticus.

The objective of this study was to evaluate the accuracy of seizure burden in patients with super-refractory status epilepticus (SRSE) by using quantitative electroencephalography (qEEG). EEG recordings from 69 patients with SRSE (2009-2019) were reviewed and annotated for seizures by three groups of reviewers: two board-certified neurophysiologists using only raw EEG (gold standard), two neurocritical care providers with substantial experience in qEEG analysis (qEEG experts), and two inexperienced qEEG readers (qEEG novices) using only a qEEG trend panel. Raw EEG experts identified 35 (51%) patients with seizures, accounting for 2950 seizures (3,126min). qEEG experts had a sensitivity of 93%, a specificity of 61%, a false positive rate of 6.5 per day, and good agreement (κ = 0.64) between both qEEG experts. qEEG novices had a sensitivity of 98.5%, a specificity of 13%, a false positive rate of 15 per day, and fair agreement (κ = 0.4) between both qEEG novices. Seizure burden was not different between the qEEG experts and the gold standard (3,257 vs. 3,126min), whereas qEEG novices reported higher burden (6066 vs. 3126min). Both qEEG experts and novices had a high sensitivity but a low specificity for seizure detection in patients with SRSE. qEEG could be a useful tool for qEEG experts to estimate seizure burden in patients with SRSE.

Rapid annotation of seizures and interictal-ictal-injury continuum EEG patterns

BackgroundManual annotation of seizures and interictal-ictal-injury continuum (IIIC) patterns in continuous EEG (cEEG) recorded from critically ill patients is a time-intensive process for clinicians and researchers. In this study, we evaluated the accuracy and efficiency of an automated clustering method to accelerate expert annotation of cEEG. New methodWe learned a local dictionary from 97 ICU patients by applying k-medoids clustering to 592 features in the time and frequency domains. We utilized changepoint detection (CPD) to segment the cEEG recordings. We then computed a bag-of-words (BoW) representation for each segment. We further clustered the segments by affinity propagation. EEG experts scored the resulting clusters for each patient by labeling only the cluster medoids. We trained a random forest classifier to assess validity of the clusters. ResultsMean pairwise agreement of 62.6% using this automated method was not significantly different from interrater agreements using manual labeling (63.8%), demonstrating the validity of the method. We also found that it takes experts using our method 5.31 ± 4.44 min to label the 30.19 ± 3.84 h of cEEG data, more than 45 times faster than unaided manual review, demonstrating efficiency. Comparison with existing methodsPrevious studies of EEG data labeling have generally yielded similar human expert interrater agreements, and lower agreements with automated methods. ConclusionsOur results suggest that long EEG recordings can be rapidly annotated by experts many times faster than unaided manual review through the use of an advanced clustering method.

Machine-learning-based diagnostics of EEG pathology

Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding have typically analyzed a limited number of features, decoders, or both. For a I) more elaborate feature-based EEG analysis, and II) in-depth comparisons of both approaches, here we first develop a comprehensive feature-based framework, and then compare this framework to state-of-the-art end-to-end methods. To this aim, we apply the proposed feature-based framework and deep neural networks including an EEG-optimized temporal convolutional network (TCN) to the task of pathological versus non-pathological EEG classification. For a robust comparison, we chose the Temple University Hospital (TUH) Abnormal EEG Corpus (v2.0.0), which contains approximately 3000 EEG recordings. The results demonstrate that the proposed feature-based decoding framework can achieve accuracies on the same level as state-of-the-art deep neural networks. We find accuracies across both approaches in an astonishingly narrow range from 81 to 86%. Moreover, visualizations and analyses indicated that both approaches used similar aspects of the data, e.g., delta and theta band power at temporal electrode locations. We argue that the accuracies of current binary EEG pathology decoders could saturate near 90% due to the imperfect inter-rater agreement of the clinical labels, and that such decoders are already clinically useful, such as in areas where clinical EEG experts are rare. We make the proposed feature-based framework available open source and thus offer a new tool for EEG machine learning research.

Predicting the non-survival outcome of large hemispheric infarction patients via quantitative electroencephalography: Superiority to visual electroencephalography and the Glasgow Coma Scale

BackgroundQuantitative electroencephalography (QEEG) data are useful to predict outcomes of cerebral infarction patients. This study was performed to establish the value of QEEG in the prediction of outcomes in patients with large hemispheric infarction (LHI). MethodsA prognostic blinded cohort study was conducted on patients diagnosed with LHI in our neurocritical care unit. The electroencephalography (EEG) was recorded at the bedside within 3 days of LHI onset. Each EEG expert scored the raw EEG, and QEEG parameters including the absolute power, (delta + theta)/ (alpha + beta) ratio and brain symmetry index were obtained afterwards. Baseline data including Glasgow Coma Scale (GCS) was recorded at the meantime. Outcomes included survival or non-survival at the time of discharge and 6 months after the onset of LHI. ResultsA total of 50 patients entered into the final analysis. There were no differences in baseline data or visual EEG grades between survival and non-survival groups. QEEG analysis showed that the absolute theta power of all of the electrodes and the contralateral electrodes was significantly higher in the non-survival group than in the survival group at discharge. Multivariable logistic regression analysis demonstrated that theta power of the contralateral electrodes was an independent predictor of death at discharge and at 6 months. Compared to the GCS and EEG grading, the QEEG index exhibited higher accuracy in predicting non-survival outcomes. ConclusionsAmong QEEG indices, theta power is valuable in predicting non-survival outcome in participants and is superior to visual EEG and GCS.

Rapid Annotation of Seizures and Interictal-ictal Continuum EEG Patterns.

Seizures, status epilepticus, and seizure-like rhythmic or periodic activities are common, pathological, harmful states of brain electrical activity seen in the electroencephalogram (EEG) of patients during critical medical illnesses or acute brain injury. Accumulating evidence shows that these states, when prolonged, cause neurological injury. In this study we developed a valid method to automatically discover a small number of homogeneous pattern clusters, to facilitate efficient interactive labelling by EEG experts. 592 time domain and spectral features were extracted from continuous EEG (cEEG) data of 369 ICU (intensive care unit) patients. For each patient, feature dimensionality was reduced using principal component analysis (PCA), retaining 95% of the variance. K-medoids clustering was applied to learn a local dictionary from each patient, consisting of k=100 exemplars/words. Changepoint detection (CPD) was utilized to break each EEG into segments. A bag-of-words (BoW) representation was computed for each segment, specifically, a normalized histogram of the words found within each segment. Segments were further clustered using the BoW histograms by Affinity Propagation (AP) using a χ2 distance to measure similarities between histograms. The resulting 30 50 clusters for each patient were scored by EEG experts through labeling only the cluster medoids. Embedding methods t-SNE (t-distributed stochastic neighbor embedding) and PCA were used to provide a 2D representation for visualization and exploration of the data. Our results illustrate that it takes approximately 3 minutes to annotate 24 hours of cEEG by experts, which is at least 60 times faster than unaided manual review.

Automated Long-Term EEG Review: Fast and Precise Analysis in Critical Care Patients.

Background: Ongoing or recurrent seizure activity without prominent motor features is a common burden in neurological critical care patients and people with epilepsy during ICU stays. Continuous EEG (CEEG) is the gold standard for detecting ongoing ictal EEG patterns and monitoring functional brain activity. However CEEG review is very demanding and time consuming. The purpose of the present multirater, EEG expert reviewer study, is to test and assess the clinical feasibility of an automatic EEG pattern detection method (Neurotrend).Methods: Four board certified EEG reviewers used Neurotrend to annotate 76 CEEG datasets à 6 h (in total 456 h of EEG) for rhythmic and periodic EEG patterns (RPP), unequivocal ictal EEG patterns and burst suppression. All reviewers had a predefined time limit of 5 min (± 2 min) per CEEG dataset and were compared to a predefined gold standard (conventional EEG review with unlimited time). Subanalysis of specific features of RPP was conducted as well. We used Gwet's AC1 and AC2 coefficients to calculate interrater agreement (IRA) and multirater agreement (MRA). Also, we determined individual performance measures for unequivocal ictal EEG patterns and burst suppression. Bonferroni-Holmes correction for multiple testing was applied to all statistical tests.Results: Mean review time was 3.3 min (± 1.9 min) per CEEG dataset. We found substantial IRA for unequivocal ictal EEG patterns (0.61–0.79; mean sensitivity 86.8%; mean specificity 82.2%, p < 0.001) and burst suppression (0.68–0.71; mean sensitivity 96.7%; mean specificity 76.9% p < 0.001). Two reviewers showed substantial IRA for RPP (0.68–0.72), whereas the other two showed moderate agreement (0.45–0.54), compared to the gold standard (p < 0.001). MRA showed almost perfect agreement for burst suppression (0.86) and moderate agreement for RPP (0.54) and unequivocal ictal EEG patterns (0.57).Conclusions: We demonstrated the clinical feasibility of an automatic critical care EEG pattern detection method on two levels: (1) reasonable high agreement compared to the gold standard, (2) reasonable short review times compared to previously reported EEG review times with conventional EEG analysis.

T70. Automatic clustering of EEG data from ICU patients

Introduction Seizures, status epilepticus, and seizure-like rhythmic or periodic activity are common, pathological, and harmful states of brain electrical activity seen in the electroencephalogram (EEG) of patients during critical medical illnesses or acute brain injury. A growing body of evidence shows that these states, when prolonged, cause neurological injury. In this study, we aimed to develop a valid method to automatically discover a small number of homogeneous pattern clusters, to facilitate efficient interactive labelling by EEG experts. Methods In this study, we analysed continuous EEG recordings from 100 ICU patients at MGH. The duration of each recording is at least 24 h, with a sampling rate of 200 Hz. We extracted 592 time and frequency domain features from each EEG recording. Principal component analysis (PCA) with 95% variance retained to reduce the dimensionality for each feature array. We further segment the EEG based on change point detection on spectrogram. In each segment, we associate each 1 s epoch to its nearest exemplar found in a dictionary pre-defined using Textons, and compute the normalized histogram. It is followed by unsupervised clustering method Affinity Propagation, to further split the data into various clusters. Three experts independently labelled all clusters into one of 6 standard pattern categories (seizures, GPDs, LPDs, LRDA, GRDA, and Other). We compared interrater agreement (IRA) indexed by Gwet’s AC1 among experts vs. between each expert and consensus labels using two methods for labelling clusters: (1) “Labour intensive labelling” (LIL): assign the most frequent of 30 expert provided labels; (2) “Labour efficient labelling “(LEL): assign the most frequent of the 3 expert labels for the central sample. Finally, we used the embedding method t-SNE to visualize the data. Results Median [IQR] expert-expert IRA for all label pairs across subjects was 0.65 [0.58, 0.75]. IRA for individual expert labels and the final consensus label was 0.76 [0.70, 0.82] using LIL, and 0.71 [0.63, 0.78] using LEL. Differences between LIL and LEL were not statistically significant (p = 0.34). The t-SNE visualizations of the feature space generally revealed interictal-ictal continuum. Conclusion This research suggests that large EEG datasets can be automatically clustered into a small number of patterns described by standard ICU EEG patterns. We demonstrated efficient cluster labelling by inspecting only the central most representative of each cluster. Furthermore, t-SNE visualizations support the hypothesis of an interictal-ictal continuum.

T23. Individuals without EEG training can detect subclinical seizures and seizure-like activity by listening to sonified EEG

The traditional approach to interpreting EEG requires physicians with formal training to visually assess the waveforms. This approach can be less practical in critical settings where a trained EEG specialist may not be readily available to diagnose subclinical seizures, such as non-convulsive status epilepticus, in patients with altered mental status. As a proof of concept, we designed the current study to explore whether individuals without EEG training can detect ongoing seizures by simply listening to one channel of sonified EEG instead of viewing 20+ channels of EEG. We selected 84, 15-s long, EEG samples to represent various conditions commonly seen in the ICU. Our reference standard was defined by unanimous agreement of 3 epileptologists reviewing visually displayed EEGs in double banana montage. Samples were classified as seizure (focal or generalized, n = 7), seizure-like (LPD, GPD, or burst suppression, n = 25), or non-periodic non-rhythmic (normal or focal/generalized slowing, n = 52). EEG data were then converted to sound files (separating left and right hemispheres to 168 clips, also 15 s long) using a novel sonification method. After a brief training video, medical students (n = 34) and nurses (n = 30) were asked to indicate each audio sample as “seizure” or “non-seizure”. We then compared their performance (sensitivity and specificity; reported as mean ± SD) with that of EEG experts [epilepsy attendings with >10 years of experience (n = 2) and epilepsy fellows (n = 7)] and some of the medical students (n = 29) who also diagnosed the same EEGs on visual display. Our prediction was that students and nurses without EEG training would be able to detect seizures in sonified EEGs with high sensitivity while they will be able to differentiate non-seizure or non-seizure-like events from seizure or seizure-like events with high specificity. Non-experts listening to single-channel sonified EEGs detected seizures with remarkable sensitivity (students: 98 ± 5%; nurses: 95 ± 14%) compared to experts or non-experts reviewing the same EEGs on visual display (attendings: 100%; fellows: 90 ± 11%; students: 76 ± 19%). If the EEGs contained seizures or seizure-like activity, non-experts listening to sonified EEGs rated them as seizures with high specificity (students: 85 ± 9%; nurses: 82 ± 12%) compared to experts or non-experts viewing the EEGs visually (attendings: 95 ± 1%; fellows: 91 ± 7%; students: 65 ± 20%). Our study is the first of its kind to confirm that individuals without EEG training, such as medical students and nurses, can detect ongoing seizures or seizure-like rhythmic periodic activity by merely listening to short duration of sonified EEG. While sonification of EEG cannot replace the traditional approaches to EEG interpretation, it provides a meaningful triage tool for fast assessment of patients with suspected subclinical seizures.

S27. Outcome prediction in postanoxic coma with deep learning

Introduction Visual assessment of the electroencephalogram (EEG) by experienced clinical neurophysiologists allows reliable outcome prediction in up to half of all comatose patients after cardiac arrest. We hypothesize that deep neural networks can achieve similar or better performance, while being objective and consistent. Methods In a prospective cohort study, continuous EEG recordings from comatose patients after cardiac arrest were collected from the intensive care units of two large teaching hospitals. Functional outcome at six months was assessed using the Cerebral Performance Category scale (CPC), dichotomized as good (CPC 1–2) or poor (CPC 3–5). Five-minute artifact-free EEG epochs at 12 and 24 h after cardiac arrest were partitioned into 10 s epochs. We trained a convolutional neural network, using the raw EEG epochs and outcome labels as inputs to predict outcome using data from 80% of the patients. Validation was performed in the remaining 20%. The probability of recovery to good neurological outcome was quantified for each individual patient. Analyses of diagnostic accuracy included receiver operating characteristics and calculation of predictive values at 12 and 24 h. Results Four hundred and fifty-six patients were included, resulting in 306 and 439 EEGs epochs at 12 and 24 h, respectively. Outcome prediction was most accurate at 12 h, with an area under the ROC curve (AUC) of 0.89 versus 0.81 at 24 h. Poor outcome could be predicted at 12 h with a sensitivity of 62% (95% confidence interval (CI): 45–78%) at false positive rate (FPR) of 0% (CI: 0–14%); good outcome could be predicted at 12 h with a sensitivity of 50% (CI: 29–71%) at a FPR of 5% (CI: 1–18%). Conclusion Deep learning of raw EEG signals outperforms any previously reported outcome predictor of coma after cardiac arrest, including visual assessment by trained EEG expert. Our approach offers the potential for objective and real-time insight in the prognosis of neurological outcome on a continuous scale, and can provide low-cost expertise at the bedside.

T74. Factors that reduce the accuracy of seizure identification using quantitative EEG displays

Introduction Quantitative electroencephalography (QEEG) trending displays are being used increasingly commonly to facilitate seizure identification during critical care EEG monitoring. Our previous work showed that the accuracy of seizure identification using QEEG is lower in some recordings, even for EEG experts. In this study, we sought to determine which seizure characteristics make identification on QEEG more difficult. Methods Three neurophysiologists were independently asked to mark all suspected seizures in 27 continuous EEG recordings from critically ill children that had been processed using two QEEG techniques: amplitude-integrated EEG (aEEG) and color density spectral array (CDSA). The raw EEG was concealed. We analyzed the following factors for their influence on the probability of correct seizure identification: spectral characteristics of individual seizures such as power and frequency (both absolute during the seizure and relative to the immediate pre-ictal background); number of seizures per recording, rate of seizures per hour and age of the child. We then performed a multivariable regression analysis to identify independent predictors of poor seizure detectability on CDSA and aEEG. Results Seizure detectability declined with decreasing seizure duration. Using CDSA, seizure identification rates were 65% for seizure durations of >180 s, 62% for 60–179 s, 44% for 30–59 s and 16% for less than 30 s. Using aEEG, seizure identification rates were 81% for seizures lasting >180 s, 67% for 60–179 s, 38% for 30–59 s and 13% for less than 30s. Focal seizures were more difficult to detect on CDSA/aEEG (26%/33%) compared to both hemispheric seizures (44%/53%) and generalized seizures (69%/64%). With CDSA, additional independent predictors of poor seizure detectability were: younger age, lower number of seizures during the recording, lower ictal/pre-ictal spectral edge frequency at 50% power (Ratio-SEF50), lower ictal/pre-ictal spectral edge frequency at 95% power (Ratio-SEF95), and lower ictal/pre-ictal average total power (Ratio-TTLPower). With aEEG, other independent predictors of poor seizure detectability were: lower number of seizures during the recording, lower Ratio-SEF50, and lower Ratio-TTLPower. Conclusion The ability of neurophysiologists to identify seizures on both CDSA and aEEG declines significantly as seizures become shorter and more focal, with fewer seizures during the recording, and with lower ictal/pre-ictal power and spectral edge frequency. Also, for CDSA but not aEEG, seizure detection was more difficult in younger children. These insights into the characteristics that reduce seizure detectability using QEEG trending displays will help to inform the optimal use of these displays, and the design of new QEEG trends that may permit more accurate seizure identification.

Insights into teaching a complex skill: Threshold concepts and troublesome knowledge in electroencephalography (EEG)

Background: Threshold concepts (TCs) are defined as ideas within a discipline that are often conceptually difficult (“troublesome”), but when learned, transform a learner’s understanding. Electroencephalography (EEG) has been recognized as a conceptually difficult field in neurology, and a study of threshold concepts in EEG may provide insights into how it is taught and learned.Methods: Semi-structured interviews were performed with 12 EEG experts in the US and Canada. Experts identified potential TCs and troublesome knowledge, and explored how these concepts were taught and learned. Interview transcripts were coded and analyzed using a general thematic analysis approach, based on the core elements of the threshold concepts framework.Results: One concept (polarity) emerged most clearly as a threshold concept. Other troublesome areas included pattern interpretation and clinical significance, but these lacked some of the characteristics of TCs. Several themes emerged, including the role of TCs and troublesome knowledge in determining expertise and the role of prior experience.Conclusions: We have used the threshold concepts framework to explore potential barriers to learning, suggest ways to support learners, and identify potential points of emphasis for teaching and learning EEG. A similar approach could be applied to the study of teaching and learning in other conceptually difficult areas of medical education.

The effect of increased intracranial EEG sampling rates in clinical practice

ObjectiveRecent research suggests that high frequency intracranial EEG (iEEG) may improve localization of epileptic networks. This study aims to determine whether recording macroelectrode iEEG with higher sampling rates improves seizure localization in clinical practice. Methods14 iEEG seizures from 10 patients recorded with >2000 Hz sampling rate were downsampled to four sampling rates: 100, 200, 500, 1000 Hz. In the 56 seizures, seizure onset time and location was marked by 5 independent, blinded EEG experts. ResultsWhen reading iEEG under clinical conditions, there was no consistent difference in time or localization of seizure onset or number of electrodes involved in the seizure onset zone with sampling rates varying from 100 to 1000 Hz. Stratification of patients by outcome did not improve with higher sampling rate. ConclusionWhen utilizing standard clinical protocols, there was no benefit to acquiring iEEGs with sampling rate >100 Hz. Significant variability was noted in EEG marking both within and between individual expert EEG readers. SignificanceAlthough commercial equipment is capable of sampling much faster than 100 Hz, tools allowing visualization of subtle high frequency activity such as HFOs will be required to improve patient care. Quantitative methods may decrease reader variability, and potentially improve patient outcomes.

Personalized structural image analysis in patients with temporal lobe epilepsy

Volumetric and morphometric studies have demonstrated structural abnormalities related to chronic epilepsies on a cohort- and population-based level. On a single-patient level, specific patterns of atrophy or cortical reorganization may be widespread and heterogeneous but represent potential targets for further personalized image analysis and surgical therapy. The goal of this study was to compare morphometric data analysis in 37 patients with temporal lobe epilepsies with expert-based image analysis, pre-informed by seizure semiology and ictal scalp EEG. Automated image analysis identified abnormalities exceeding expert-determined structural epileptogenic lesions in 86% of datasets. If EEG lateralization and expert MRI readings were congruent, automated analysis detected abnormalities consistent on a lobar and hemispheric level in 82% of datasets. However, in 25% of patients EEG lateralization and expert readings were inconsistent. Automated analysis localized to the site of resection in 60% of datasets in patients who underwent successful epilepsy surgery. Morphometric abnormalities beyond the mesiotemporal structures contributed to subtype characterisation. We conclude that subject-specific morphometric information is in agreement with expert image analysis and scalp EEG in the majority of cases. However, automated image analysis may provide non-invasive additional information in cases with equivocal radiological and neurophysiological findings.

Research on Consumers' Brain Activations by Means of Electroencephalography Method

Marketers are excited about two main reasons that are concerned with neuroelectrical brain imaging. The first reason is that they expect neuroimaging will offer a more efficient exchange between cost and benefit. This expectation is based on the assumption that consumers don’t want to express their preferences explicitly or that it is not possible for them to do this when their preferences are asked; or that consumer' s brain, which is related to real preferences, contains confidential information. Such information is used in theory to influence purchasing behavior. Thus, the cost of performing neuroimaging studies against improved product design and increased sales benefit is outweigh. As the second reason, marketers expect that they will be able to obtain an accurate marketing research method that can be applied even before an existing product. The purpose of this study is to determine the rational influence activation of consumers and to emphasize the deficiency of using traditional method of questionnaire based solely on declaration to understand consumers. Accordingly, volunteer subjects watched an advertisement film and their brain activities were tried to be measured while they were watching the advertisement. This measurement was performed with a 10 channel EEG device. During the research, the emotional responses of participants and their brain activities were measured and EEG patterns were obtained. The obtained data were interpreted by academicians and EEG experts.

Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach

Aim: To develop a method that segments preterm EEG into bursts and inter-bursts by extracting and combining multiple EEG features. Methods: Two EEG experts annotated bursts in individual EEG channels for 36 preterm infants with gestational age < 30 weeks. The feature set included spectral, amplitude, and frequency-weighted energy features. Using a consensus annotation, feature selection removed redundant features and a support vector machine combined features. Area under the receiver operator characteristic (AUC) and Cohen’s kappa (κ) evaluated performance within a cross-validation procedure. Results: The proposed channel-independent method improves AUC by 4–5% over existing methods (p < 0.001, n=36), with median (95% confidence interval) AUC of 0.989 (0.973–0.997) and sensitivity–specificity of 95.8–94.4%. Agreement rates between the detector and experts’ annotations, κ=0.72 (0.36–0.83) and κ=0.65 (0.32–0.81), are comparable to inter-rater agreement, κ=0.60 (0.21–0.74). Conclusions: Automating the visual identification of bursts in preterm EEG is achievable with a high level of accuracy. Multiple features, combined using a data-driven approach, improves on existing single-feature methods.

Rapid annotation of interictal epileptiform discharges via template matching under Dynamic Time Warping

BackgroundEEG interpretation relies on experts who are in short supply. There is a great need for automated pattern recognition systems to assist with interpretation. However, attempts to develop such systems have been limited by insufficient expert-annotated data. To address these issues, we developed a system named NeuroBrowser for EEG review and rapid waveform annotation. New methodsAt the core of NeuroBrowser lies on ultrafast template matching under Dynamic Time Warping, which substantially accelerates the task of annotation. ResultsOur results demonstrate that NeuroBrowser can reduce the time required for annotation of interictal epileptiform discharges by EEG experts by 20–90%, with an average of approximately 70%. Comparison with existing method(s)In comparison with conventional manual EEG annotation, NeuroBrowser is able to save EEG experts approximately 70% on average of the time spent in annotating interictal epileptiform discharges. We have already extracted 19,000+ interictal epileptiform discharges from 100 patient EEG recordings. To our knowledge this represents the largest annotated database of interictal epileptiform discharges in existence. ConclusionNeuroBrowser is an integrated system for rapid waveform annotation. While the algorithm is currently tailored to annotation of interictal epileptiform discharges in scalp EEG recordings, the concepts can be easily generalized to other waveforms and signal types.

Detectability of Fast Ripples (>250Hz) on the Scalp EEG: A Proof-of-Principle Study with Subdermal Electrodes.

To evaluate the possibility of detecting fast ripples (FRs) on the surface EEG of patients with focal pharmacoresistant epilepsy, and to investigate the relationship between scalp FRs and localization of the seizure onset zone (SOZ). We included 10 patients undergoing combined surface-intracranial EEG with ≥10 spikes in the surface EEG during the first 30 consecutive minutes of N3 sleep. FRs (≥4 consecutive oscillations above 250Hz with an amplitude clearly exceeding that of the background) on the surface EEG (F3-C3, C3-P3, Fz-Cz, Cz-Pz, F4-C4, C4-P4) were visually marked, and verified by two EEG experts. FRs were categorized as related to the SOZ, if localized in the brain lobe of the SOZ. Low-amplitude FRs with a rate of 0.09/min were found in 6/10 patients: two exhibited events related to the SOZ, three showed no relationship with the SOZ, and in one patient the SOZ was not identified. It may be possible to detect FRs with surface EEG using subdermal electrodes in patients with focal epilepsy. The relationship between surface FRs and the SOZ remains unclear. Future studies aiming at a higher spatial EEG coverage are needed to elucidate their significance.