Computerized epileptiform transient detection in the scalp electroencephalogram: Obstacles to progress and the example of computerized ECG interpretation

vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data

To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this study proposes a multimodal method, vEpiNet, that leverages video and electroencephalogram (EEG) data. Datasets comprise 24 931 IED (from 484 patients) and 166 094 non-IED 4-second video-EEG segments. The video data is processed by the proposed patient detection method, with frame difference and Simple Keypoints (SKPS) capturing patients' movements. EEG data is processed with EfficientNetV2. The video and EEG features are fused via a multilayer perceptron. We developed a comparative model, termed nEpiNet, to test the effectiveness of the video feature in vEpiNet. The 10-fold cross-validation was used for testing. The 10-fold cross-validation showed high areas under the receiver operating characteristic curve (AUROC) in both models, with a slightly superior AUROC (0.9902) in vEpiNet compared to nEpiNet (0.9878). Moreover, to test the model performance in real-world scenarios, we set a prospective test dataset, containing 215 h of raw video-EEG data from 50 patients. The result shows that the vEpiNet achieves an area under the precision-recall curve (AUPRC) of 0.8623, surpassing nEpiNet's 0.8316. Incorporating video data raises precision from 70% (95% CI, 69.8%-70.2%) to 76.6% (95% CI, 74.9%-78.2%) at 80% sensitivity and reduces false positives by nearly a third, with vEpiNet processing one-hour video-EEG data in 5.7 min on average. Our findings indicate that video data can significantly improve the performance and precision of IED detection, especially in prospective real clinic testing. It suggests that vEpiNet is a clinically viable and effective tool for IED analysis in real-world applications.

Artificial intelligence/machine learning for epilepsy and seizure diagnosis

Accurate seizure and epilepsy diagnosis remains a challenging task due to the complexity and variability of manifestations, which can lead to delayed or missed diagnosis. Machine learning (ML) and artificial intelligence (AI) is a rapidly developing field, with growing interest in integrating and applying these tools to aid clinicians facing diagnostic uncertainties. ML algorithms, particularly deep neural networks, are increasingly employed in interpreting electroencephalograms (EEG), neuroimaging, wearable data, and seizure videos. This review discusses the development and testing phases of AI/ML tools, emphasizing the importance of generalizability and interpretability in medical applications, and highlights recent publications that demonstrate the current and potential utility of AI to aid clinicians in diagnosing epilepsy. Current barriers of AI integration in patient care include dataset availability and heterogeneity, which limit studies' quality, interpretability, comparability, and generalizability. ML and AI offer substantial promise in improving the accuracy and efficiency of epilepsy diagnosis. The growing availability of diverse datasets, enhanced processing speed, and ongoing efforts to standardize reporting contribute to the evolving landscape of AI applications in clinical care.

AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals

Within the domain of cardiovascular diseases, arrhythmia is one of the leading anomalies causing sudden deaths. These anomalies, including arrhythmia, are detectable through the electrocardiogram, a pivotal component in the analysis of heart diseases. However, conventional methods like electrocardiography encounter challenges such as subjective analysis and limited monitoring duration. In this work, a novel hybrid model, AttBiLFNet, was proposed for precise arrhythmia detection in ECG signals, including imbalanced class distributions. AttBiLFNet integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with a convolutional neural network (CNN) and incorporates an attention mechanism using the focal loss function. This architecture is capable of autonomously extracting features by harnessing BiLSTM's bidirectional information flow, which proves advantageous in capturing long-range dependencies. The attention mechanism enhances the model's focus on pertinent segments of the input sequence, which is particularly beneficial in class imbalance classification scenarios where minority class samples tend to be overshadowed. The focal loss function effectively addresses the impact of class imbalance, thereby improving overall classification performance. The proposed AttBiLFNet model achieved 99.55% accuracy and 98.52% precision. Moreover, performance metrics such as MF1, K score, and sensitivity were calculated, and the model was compared with various methods in the literature. Empirical evidence showed that AttBiLFNet outperformed other methods in terms of both accuracy and computational efficiency. The introduced model serves as a reliable tool for the timely identification of arrhythmias.

Deep-Learning-Based Automated Anomaly Detection of EEGs in Intensive Care Units

An intensive care unit (ICU) is a special ward in the hospital for patients who require intensive care. It is equipped with many instruments monitoring patients' vital signs and supported by the medical staff. However, continuous monitoring demands a massive workload of medical care. To ease the burden, we aim to develop an automatic detection model to monitor when brain anomalies occur. In this study, we focus on electroencephalography (EEG), which monitors the brain electroactivity of patients continuously. It is mainly for the diagnosis of brain malfunction. We propose the gated-recurrent-unit-based (GRU-based) model for detecting brain anomalies; it predicts whether the spike or sharp wave happens within a short time window. Based on the banana montage setting, the proposed model exploits characteristics of multiple channels simultaneously to detect anomalies. It is trained, validated, and tested on separated EEG data and achieves more than 90% testing performance on sensitivity, specificity, and balanced accuracy. The proposed anomaly detection model detects the existence of a spike or sharp wave precisely; it will notify the ICU medical staff, who can provide immediate follow-up treatment. Consequently, it can reduce the medical workload in the ICU significantly.

Comparison of Automated Spike Detection Software in Detecting Epileptiform Abnormalities on Scalp-EEG of Genetic Generalized Epilepsy Patients

PURPOSE

Despite availability of commercial EEG software for automated epileptiform detection, validation on real-world EEG datasets is lacking. Performance evaluation of two software packages on a large EEG dataset of patients with genetic generalized epilepsy was performed.

METHODS

Three epileptologists labelled IEDs manually of EEGs from three centres. All Interictal epileptiform discharge (IED) markings predicted by two commercial software (Encevis 1.11 and Persyst 14) were reviewed individually to assess for suspicious missed markings and were integrated into the reference standard if overlooked during manual annotation during a second phase. Sensitivity, precision, specificity, and F1-score were used to assess the performance of the software packages against the adjusted reference standard.

RESULTS

One hundred and twenty-five routine scalp EEG recordings from different subjects were included (total recording time, 310.7 hours). The total epileptiform discharge reference count was 5,907 (including spikes and fragments). Encevis demonstrated a mean sensitivity for detection of IEDs of 0.46 (SD 0.32), mean precision of 0.37 (SD 0.31), and mean F1-score of 0.43 (SD 0.23). Using the default medium setting, the sensitivity of Persyst was 0.67 (SD 0.31), with a precision of 0.49 (SD 0.33) and F1-score of 0.51 (SD 0.25). Mean specificity representing non-IED window identification and classification was 0.973 (SD 0.08) for Encevis and 0.968 (SD 0.07) for Persyst.

CONCLUSIONS

Automated software shows a high degree of specificity for detection of nonepileptiform background. Sensitivity and precision for IED detection is lower, but may be acceptable for initial screening in the clinical and research setting. Clinical caution and continuous expert human oversight are recommended with all EEG recordings before a diagnostic interpretation is provided based on the output of the software.

Ultrafast review of ambulatory EEGs with deep learning

OBJECTIVE

Interictal epileptiform discharges (IED) are hallmark biomarkers of epilepsy which are typically detected through visual analysis. Deep learning has shown potential in automating IED detection, which could reduce the burden of visual analysis in clinical practice. This is particularly relevant for ambulatory electroencephalograms (EEGs), as these entail longer review times.

METHODS

We applied a previously trained neural network to an independent dataset of 100 ambulatory EEGs (average duration 20.6 h). From these, 42 EEGs contained IEDs, 25 were abnormal without IEDs and 33 were normal. The algorithm flagged 2 second epochs that it considered IEDs. The EEGs were provided to an expert, who used NeuroCenter EEG to review the recordings. The expert concluded if each recording contained IEDs, and was timed during the process.

RESULTS

The conclusion of the reviewer was the same as the EEG report in 97% of the recordings. Three EEGs contained IEDs that were not detected based on the flagged epochs. Review time for the 100 EEGs was approximately 4 h, with half of the recordings taking <2 minutes to review.

CONCLUSIONS

Our network can be used to reduce time spent on visual analysis in the clinic by 50-75 times with high reliability.

SIGNIFICANCE

Given the large time reduction potential and high success rate, this algorithm can be used in the clinic to aid in visual analysis.

Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023

Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography (ECG) still represents the benchmark approach for identifying cardiac irregularities. Automatic detection of abnormalities from the ECG can aid in the early detection, diagnosis, and prevention of cardiovascular diseases. Deep Learning (DL) architectures have been successfully employed for arrhythmia detection and classification and offered superior performance to traditional shallow Machine Learning (ML) approaches. This survey categorizes and compares the DL architectures used in ECG arrhythmia detection from 2017-2023 that have exhibited superior performance. Different DL models such as Convolutional Neural Networks (CNNs), Multilayer Perceptrons (MLPs), Transformers, and Recurrent Neural Networks (RNNs) are reviewed, and a summary of their effectiveness is provided. This survey provides a comprehensive roadmap to expedite the acclimation process for emerging researchers willing to develop efficient algorithms for detecting ECG anomalies using DL models. Our tailored guidelines bridge the knowledge gap allowing newcomers to align smoothly with the prevailing research trends in ECG arrhythmia detection. We shed light on potential areas for future research and refinement in model development and optimization, intending to stimulate advancement in ECG arrhythmia detection and classification.

Magnetoencephalography-based approaches to epilepsy classification

Epilepsy is a chronic central nervous system disorder characterized by recurrent seizures. Not only does epilepsy severely affect the daily life of the patient, but the risk of premature death in patients with epilepsy is three times higher than that of the normal population. Magnetoencephalography (MEG) is a non-invasive, high temporal and spatial resolution electrophysiological data that provides a valid basis for epilepsy diagnosis, and used in clinical practice to locate epileptic foci in patients with epilepsy. It has been shown that MEG helps to identify MRI-negative epilepsy, contributes to clinical decision-making in recurrent seizures after previous epilepsy surgery, that interictal MEG can provide additional localization information than scalp EEG, and complete excision of the stimulation area defined by the MEG has prognostic significance for postoperative seizure control. However, due to the complexity of the MEG signal, it is often difficult to identify subtle but critical changes in MEG through visual inspection, opening up an important area of research for biomedical engineers to investigate and implement intelligent algorithms for epilepsy recognition. At the same time, the use of manual markers requires significant time and labor costs, necessitating the development and use of computer-aided diagnosis (CAD) systems that use classifiers to automatically identify abnormal activity. In this review, we discuss in detail the results of applying various different feature extraction methods on MEG signals with different classifiers for epilepsy detection, subtype determination, and laterality classification. Finally, we also briefly look at the prospects of using MEG for epilepsy-assisted localization (spike detection, high-frequency oscillation detection) due to the unique advantages of MEG for functional area localization in epilepsy, and discuss the limitation of current research status and suggestions for future research. Overall, it is hoped that our review will facilitate the reader to quickly gain a general understanding of the problem of MEG-based epilepsy classification and provide ideas and directions for subsequent research.

Spike height improves prediction of future seizure risk

OBJECTIVE

We evaluated whether interictal epileptiform discharge (IED) rate and morphological characteristics predict seizure risk.

METHODS

We evaluated 10 features from automatically detectable IEDs in a stereotyped population with self-limited epilepsy with centrotemporal spikes (SeLECTS). We tested whether the average value or the most extreme values from each feature predicted future seizure risk in cross-sectional and longitudinal models.

RESULTS

10,748 individual centrotemporal IEDs were analyzed from 59 subjects at 81 timepoints. In cross-sectional models, increases in average spike height, spike duration, slow wave rising slope, slow wave falling slope, and the most extreme values of slow wave rising slope each improved prediction of an increased risk of a future seizure compared to a model with age alone (p < 0.05, each). In longitudinal model, spike rising height improved prediction of future seizure risk compared to a model with age alone (p = 0.04) CONCLUSIONS: Spike height improves prediction of future seizure risk in SeLECTS. Several other morphological features may also improve prediction and should be explored in larger studies.

SIGNIFICANCE

Discovery of a relationship between novel IED features and seizure risk may improve clinical prognostication, visual and automated IED detection strategies, and provide insights into the underlying neuronal mechanisms that contribute to IED pathology.

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.

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.

Moving the field forward: detection of epileptiform abnormalities on scalp electroencephalography using deep learning—clinical application perspectives

The application of deep learning approaches for the detection of interictal epileptiform discharges is a nascent field, with most studies published in the past 5 years. Although many recent models have been published demonstrating promising results, deficiencies in descriptions of data sets, unstandardized methods, variation in performance evaluation and lack of demonstrable generalizability have made it difficult for these algorithms to be compared and progress to clinical validity. A few recent publications have provided a detailed breakdown of data sets and relevant performance metrics to exemplify the potential of deep learning in epileptiform discharge detection. This review provides an overview of the field and equips computer and data scientists with a synopsis of EEG data sets, background and epileptiform variation, model evaluation parameters and an awareness of the performance metrics of high impact and interest to the trained clinical and neuroscientist EEG end user. The gold standard and inter-rater disagreements in defining epileptiform abnormalities remain a challenge in the field, and a hierarchical proposal for epileptiform discharge labelling options is recommended. Standardized descriptions of data sets and reporting metrics are a priority. Source code-sharing and accessibility to public EEG data sets will increase the rigour, quality and progress in the field and allow validation and real-world clinical translation.

Automated spike detection: Which software package?

PURPOSE

We assessed three commercial automated spike detection software packages (Persyst, Encevis and BESA) to see which had the best performance.

METHODS

Thirty prolonged EEG records from people aged at least 16 years were collected and 30-minute representative epochs were selected. Interictal epileptiform discharges (IEDs) were marked by three human experts and by all three software packages. For each 30-minutes selection and for each 10-second epoch we measured whether or not IEDs had occurred. We defined the gold standard as the combined detections of the experts. Kappa scores, sensitivity and specificity were estimated for each software package.

RESULTS

Sensitivity for Persyst in the default setting was 95% for 30-minute selections and 82% for 10-second epochs. Sensitivity for Encevis was 86% (30-minute selections) and 61% (10-second epochs). The specificity for both packages was 88% for 30-minute selections and 96%-99% for the 10-second epochs. Interrater agreement between Persyst and Encevis and the experts was similar than between experts (0.67-0.83 versus 0.63-0.67). Sensitivity for BESA was 40% and specificity 100%. Interrater agreement (0.25) was low.

CONCLUSIONS

IED detection by the Persyst automated software is better than the Encevis and BESA packages, and similar to human review, when reviewing 30-minute selections and 10-second epochs. This findings may help prospective users choose a software package.

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.

[Automatic detection of epileptiform potentials and seizures in the EEG]

Automatic computer-based algorithms for the detection of epileptiform potentials and seizure patterns on EEG facilitate a time-saving, objective method of quantitative EEG interpretation which is available 7/24. For the automatic detection of interictal epileptiform potentials sensitivities range from 65 to 99% with false positive detections of 0,09 to 13,4 per minute. Recent studies documented equal or even better performance of automatic spike detection programs compared with experienced human EEG readers. The seizure detection problem-one of the major problems in clinical epileptology-consists of the fact that the majority of focal onset seizures with impaired awareness and of seizures arising out of sleep occur unnoticed by patients and their caregivers. Automatic seizure detection systems could facilitate objective seizure documentation and thus help to solve the seizure detection problem. Furthermore, seizure detection systems may help to prevent seizure-related injuries and sudden unexpected death in epilepsy (SUDEP), and could be an integral part of novel, seizure-triggered on-demand therapies in epilepsy. During long-term video-EEG monitoring seizure detection systems could improve patient safety, provide a time-saving objective and reproducible analysis of seizure patterns and facilitate automatic computer-based patient testing during seizures. Sensitivities of seizure detection systems range from 75 to 90% with extratemporal seizures being more difficult to detect than temporal seizures. The false positive alarm rate ranges from 0,1 to 5 per 24 hours. Finally, machine learning algorithms, especially deep learning approaches, open a new highly promising era in automatic spike and seizure detection.

Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis

Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free versus IED EEGs. In this study, we evaluate features that may provide reliable IED detection and EEG classification. Specifically, we investigate the IED detector based on convolutional neural network (ConvNet) with different input features (temporal, spectral, and wavelet features). We explore different ConvNet architectures and types, including 1D (one-dimensional) ConvNet, 2D (two-dimensional) ConvNet, and noise injection at various layers. We evaluate the EEG classification performance on five independent datasets. The 1D ConvNet with preprocessed full-frequency EEG signal and frequency bands (delta, theta, alpha, beta) with Gaussian additive noise at the output layer achieved the best IED detection results with a false detection rate of 0.23/min at 90% sensitivity. The EEG classification system obtained a mean EEG classification Leave-One-Institution-Out (LOIO) cross-validation (CV) balanced accuracy (BAC) of 78.1% (area under the curve (AUC) of 0.839) and Leave-One-Subject-Out (LOSO) CV BAC of 79.5% (AUC of 0.856). Since the proposed classification system only takes a few seconds to analyze a 30-min routine EEG, it may help in reducing the human effort required for epilepsy diagnosis.

Activation patterns of interictal epileptiform discharges in relation to sleep and seizures: An artificial intelligence driven data analysis

OBJECTIVE

To quantify effects of sleep and seizures on the rate of interictal epileptiform discharges (IED) and to classify patients with epilepsy based on IED activation patterns.

METHODS

We analyzed long-term EEGs from 76 patients with at least one recorded epileptic seizure during monitoring. IEDs were detected with an AI-based algorithm and validated by visual inspection. We then used unsupervised clustering to characterize patient sub-cohorts with similar IED activation patterns regarding circadian rhythms, deep sleep activation, and seizure occurrence.

RESULTS

Five sub-cohorts with similar IED activation patterns were found: "Sporadic" (14%, n = 10) without or few IEDs, "Continuous" (32%, n = 23) with weak circadian/deep sleep or seizure modulation, "Nighttime & seizure activation" (23%, n = 17) with high IED rates during normal sleep times and after seizures but without deep sleep modulation, "Deep sleep" (19%, n = 14) with strong IED modulation during deep sleep, and "Seizure deactivation" (12%, n = 9) with deactivation of IEDs after seizures. Patients showing "Deep sleep" IED pattern were diagnosed with temporal lobe epilepsy in 86%, while 80% of the "Sporadic" cluster were extratemporal.

CONCLUSIONS

Patients with epilepsy can be characterized by using temporal relationships between rates of IEDs, circadian rhythms, deep sleep and seizures.

SIGNIFICANCE

This work presents the first approach to data-driven classification of epilepsy patients based on their fully validated temporal pattern of IEDs.

Determining the Spike-Wave Index Using Automated Detection Software

PURPOSE

The spike-wave index (SWI) is a key feature in the diagnosis of electrical status epilepticus during slow-wave sleep. Estimating the SWI manually is time-consuming and is subject to interrater and intrarater variability. Use of automated detection software would save time. Thereby, this software will consistently detect a certain EEG phenomenon as epileptiform and is not influenced by human factors. To determine noninferiority in calculating the SWI, we compared the performance of a commercially available spike detection algorithm (P13 software, Persyst Development Corporation, San Diego, CA) with human expert consensus.

METHODS

The authors identified all prolonged EEG recordings for the diagnosis or follow-up of electrical status epilepticus during slow-wave sleep carried out from January to December 2018 at an epilepsy tertiary referral center. The SWI during the first 10 minutes of sleep was estimated by consensus of two human experts. This was compared with the SWI calculated by the automated spike detection algorithm using the three available sensitivity settings: "low," "medium," and "high." In the software, these sensitivity settings are denoted as perception values.

RESULTS

Forty-eight EEG recordings from 44 individuals were analyzed. The SWIs estimated by human experts did not differ from the SWIs calculated by the automated spike detection algorithm in the "low" perception mode (P = 0.67). The SWIs calculated in the "medium" and "high" perception settings were, however, significantly higher than the human expert estimated SWIs (both P < 0.001).

CONCLUSIONS

Automated spike detection (P13) is a useful tool in determining SWI, especially when using the "low" sensitivity setting. Using such automated detection tools may save time, especially when reviewing larger epochs.

Incorporating Uncertainty in Data Labeling into Automatic Detection of Interictal Epileptiform Discharges from Concurrent Scalp-EEG via Multi-way Analysis

Interictal epileptiform discharges (IEDs) are elicited from an epileptic brain, whereas they can also be due to other neurological abnormalities. The diversity in their morphologies, their strengths, and their sources within the brain cause a great deal of uncertainty in their labeling by clinicians. The aim of this study is therefore to exploit and incorporate this uncertainty (the probability of the waveform being an IED) in the IED detection system which combines spatial component analysis (SCA) with the IED probabilities referred to as SCA-IEDP-based method. For comparison, we also propose and study SCA-based method in which probability of the waveform being an IED is ignored. The proposed models are employed to detect IEDs in two different classification approaches: (1) subject-dependent and (2) subject-independent classification approaches. The proposed methods are compared with two other state-of-the-art methods namely, time-frequency features and tensor factorization methods. The proposed SCA-IEDP model has achieved superior performance in comparison with the traditional SCA and other competing methods. It achieved 79.9% and 63.4% accuracy values in subject-dependent and subject-independent classification approaches, respectively. This shows that considering the IED probabilities in designing an IED detection system can boost its performance.

Efficient use of clinical EEG data for deep learning in epilepsy

OBJECTIVE

Automating detection of Interictal Epileptiform Discharges (IEDs) in electroencephalogram (EEG) recordings can reduce the time spent on visual analysis for the diagnosis of epilepsy. Deep learning has shown potential for this purpose, but the scarceness of expert annotated data creates a bottleneck in the process.

METHODS

We used EEGs from 50 patients with focal epilepsy, 49 patients with generalized epilepsy (IEDs were visually labeled by experts) and 67 controls. The data was filtered, downsampled and cut into two second epochs. We increased the number of input samples containing IEDs through temporal shifting and using different montages. A VGG C convolutional neural network was trained to detect IEDs.

RESULTS

Using the dataset with more samples, we reduced the false positive rate from 2.11 to 0.73 detections per minute at the intersection of sensitivity and specificity. Sensitivity increased from 63% to 96% at 99% specificity. The model became less sensitive to the position of the IED in the epoch and montage.

CONCLUSIONS

Temporal shifting and use of different EEG montages improves performance of deep neural networks in IED detection.

SIGNIFICANCE

Dataset augmentation can reduce the need for expert annotation, facilitating the training of neural networks, potentially leading to a fundamental shift in EEG analysis.

Automatische Erkennung von epilepsietypischen Potenzialen und Anfällen im EEG

Die Elektroenzephalografie (EEG) ist der wichtigste apparative Eckpfeiler in der Diagnostik und Therapieführung bei Epilepsien. Die visuelle EEG-Befundung stellt dabei nach wie vor den Goldstandard dar. Automatische computerunterstützte Methoden zur Detektion und Quantifizierung von interiktalen epilepsietypischen Potenzialen und Anfällen unterstützen eine zeitsparende, objektive, rasch und jederzeit verfügbare quantitative EEG-Befundung.

Automated Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms by Convolutional Neural Networks

Visual evaluation of electroencephalogram (EEG) for Interictal Epileptiform Discharges (IEDs) as distinctive biomarkers of epilepsy has various limitations, including time-consuming reviews, steep learning curves, interobserver variability, and the need for specialized experts. The development of an automated IED detector is necessary to provide a faster and reliable diagnosis of epilepsy. In this paper, we propose an automated IED detector based on Convolutional Neural Networks (CNNs). We have evaluated the proposed IED detector on a sizable database of 554 scalp EEG recordings (84 epileptic patients and 461 nonepileptic subjects) recorded at Massachusetts General Hospital (MGH), Boston. The proposed CNN IED detector has achieved superior performance in comparison with conventional methods with a mean cross-validation area under the precision-recall curve (AUPRC) of 0.838[Formula: see text]±[Formula: see text]0.040 and false detection rate of 0.2[Formula: see text]±[Formula: see text]0.11 per minute for a sensitivity of 80%. We demonstrated the proposed system to be noninferior to 30 neurologists on a dataset from the Medical University of South Carolina (MUSC). Further, we clinically validated the system at National University Hospital (NUH), Singapore, with an agreement accuracy of 81.41% with a clinical expert. Moreover, the proposed system can be applied to EEG recordings with any arbitrary number of channels.

Deep Learning for Interictal Epileptiform Spike Detection from scalp EEG frequency sub bands

Epilepsy diagnosis through visual examination of interictal epileptiform discharges (IEDs) in scalp electroencephalogram (EEG) signals is a challenging problem. Deep learning methods can be an automated way to perform this task. In this work, we present a new approach based on convolutional neural network (CNN) to detect IEDs from EEGs automatically. The input to CNN is a combination of raw EEG and frequency sub-bands, namely delta, theta, alpha and, beta arranged as a vector for one-dimensional (1D) CNN or matrix for two-dimensional (2D) CNN. The proposed method is evaluated on 554 scalp EEGs. The database consists of 18,164 IEDs marked by two neurologists. Five-fold cross-validation was performed to assess the IED detectors. The resulting 1D CNN based IED detector with multiple sub-bands achieved a false positive rate per minute of 0.23 and a precision of 0.79 at 90% sensitivity. Further, the proposed system is evaluated on datasets from three other clinics, and the features extracted from CNN outputs could significantly discriminate (p-values <; 0.05) the EEGs with and without IEDs. We have proposed an optimized method with better performance than the literature that could aid clinicians to diagnose epilepsy expeditiously, and thereby devise proper treatment.

Using sampled visual EEG review in combination with automated detection software at the EMU

PURPOSE

Complete visual review of prolonged video-EEG recordings at an EMU (Epilepsy Monitoring Unit) is time consuming and can cause problems in times of paucity of educated personnel. In this study we aimed to show non inferiority for electroclinical diagnosis using sampled review in combination with EEG analysis softreferware (P13 software, Persyst Corporation), in comparison to complete visual review.

METHOD

Fifty prolonged video-EEG recordings in adults were prospectively evaluated using sampled visual EEG review in combination with automated detection software of the complete EEG record. Visually assessed samples consisted of one hour during wakefulness, one hour during sleep, half an hour of wakefulness after wake-up and all clinical events marked by the individual and/or nurses. The final electro-clinical diagnosis of this new review approach was compared with the electro-clinical diagnosis after complete visual review as presently used.

RESULTS

The electro-clinical diagnosis based on sampled visual review combined with automated detection software did not differ from the diagnosis based on complete visual review. Furthermore, the detection software was able to detect all records containing epileptiform abnormalities and epileptic seizures.

CONCLUSION

Sampled visual review in combination with automated detection using Persyst 13 is non-inferior to complete visual review for electroclinical diagnosis of prolonged video-EEG at an EMU setting, which makes this approach promising.

An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard

OBJECTIVE

To validate an artificial intelligence-based computer algorithm for detection of epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy.

METHODS

We developed an algorithm for automatic detection of EDs, based on a novel deep learning method that requires a low amount of labeled EEG data for training. Detected EDs are automatically grouped into clusters, consisting of the same type of EDs, for rapid visual inspection. We validated the algorithm on an independent dataset of 100 patients with sharp transients in their EEG recordings (54 with epilepsy and 46 with non-epileptic paroxysmal events). The diagnostic gold standard was derived from the video-EEG recordings of the patients' habitual events.

RESULTS

The algorithm had a sensitivity of 89% for identifying EEGs with EDs recorded from patients with epilepsy, a specificity of 70%, and an overall accuracy of 80%.

CONCLUSIONS

Automated detection of EDs using an artificial intelligence-based computer algorithm had a high sensitivity. Human (expert) supervision is still necessary for confirming the clusters of detected EDs and for describing clinical correlations. Further studies on different patient populations will be needed to confirm our results.

SIGNIFICANCE

The automated algorithm we describe here is a useful tool, assisting neurophysiologist in rapid assessment of EEG recordings.

Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic–ischemic encephalopathy (HIE). Studies of neuroprotection in animal models consistently suggest that treatment must be started as early as possible in the first 6 h after hypoxia–ischemia (HI), the so-called latent phase before secondary deterioration, to improve outcomes. We have shown in preterm sheep that EEG biomarkers of injury, in the form of high-frequency micro-scale spike transients, develop and evolve in this critical latent phase after severe asphyxia. Real-time automatic identification of such events is important for the early and accurate detection of HI injury, so that the right treatment can be implemented at the right time. We have previously reported successful strategies for accurate identification of EEG patterns after HI. In this study, we report an alternative high-performance approach based on the fusion of spectral Fourier analysis and Type-I fuzzy classifiers (FFT-Type-I-FLC). We assessed its performance in over 2520 min of latent phase EEG recordings from seven asphyxiated in utero preterm fetal sheep exposed to a range of different occlusion periods. The FFT-Type-I-FLC classifier demonstrated 98.9 ± 1.0% accuracy for identification of high-frequency spike transients in the gamma frequency band (namely 80–120 Hz) post-HI. The spectral-based approach (FFT-Type-I-FLC classifier) has similar accuracy to our previous reverse biorthogonal wavelets rbio2.8 basis function and type-1 fuzzy classifier (rbio-WT-Type-1-FLC), providing competitive performance (within the margin of error: 0.89%), but it is computationally simpler and would be readily adapted to identify other potentially relevant EEG waveforms.

Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalogram

Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.

Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning

OBJECTIVE

Develop a high-performing algorithm to detect mesial temporal lobe (mTL) epileptiform discharges on intracranial electrode recordings.

METHODS

An epileptologist annotated 13,959 epileptiform discharges from a dataset of intracranial EEG recordings from 46 epilepsy patients. Using this dataset, we trained a convolutional neural network (CNN) to recognize mTL epileptiform discharges from a single intracranial bipolar channel. The CNN outputs from multiple bipolar channel inputs were averaged to generate the final detector output. Algorithm performance was estimated using a nested 5-fold cross-validation.

RESULTS

On the receiver-operating characteristic curve, our algorithm achieved an area under the curve (AUC) of 0.996 and a partial AUC (for specificity > 0.9) of 0.981. AUC on a precision-recall curve was 0.807. A sensitivity of 84% was attained at a false positive rate of 1 per minute. 35.9% of the false positive detections corresponded to epileptiform discharges that were missed during expert annotation.

CONCLUSIONS

Using deep learning, we developed a high-performing, patient non-specific algorithm for detection of mTL epileptiform discharges on intracranial electrodes.

SIGNIFICANCE

Our algorithm has many potential applications for understanding the impact of mTL epileptiform discharges in epilepsy and on cognition, and for developing therapies to specifically reduce mTL epileptiform activity.

[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.

EEG CLassification Via Convolutional Neural Network-Based Interictal Epileptiform Event Detection

Diagnosis of epilepsy based on visual inspection of electroencephalogram (EEG) abnormalities is an inefficient, time-consuming, and expert-centered process. Moreover, the diagnosis based on ictal epileptiform events is challenging as the ictal patterns are infrequent. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential. The interictal epileptiform discharges (IEDs) are recurring patterns that are highly suggestive of epilepsy. In this paper, we propose an epileptic EEG classification system based on IED detection. The proposed system comprises of three modules: pre-processing, waveform-level classification, and EEG-level classification. We employ a Convolutional Neural Network (CNN) for waveform-level classification and a Support Vector Machine (SVM) for EEG-level classification. We evaluated the proposed system on a dataset of 156 EEGs recorded at Massachusetts General Hospital (MGH), Boston. The system achieved a mean 4-fold classification accuracy of 83.86% for classifying EEGs with and without IEDs.

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.

Scalp recorded spike ripples predict seizure risk in childhood epilepsy better than spikes

In the past decade, brief bursts of fast oscillations in the ripple range have been identified in the scalp EEG as a promising non-invasive biomarker for epilepsy. However, investigation and clinical application of this biomarker have been limited because standard approaches to identify these brief, low amplitude events are difficult, time consuming, and subjective. Recent studies have demonstrated that ripples co-occurring with epileptiform discharges ('spike ripple events') are easier to detect than ripples alone and have greater pathological significance. Here, we used objective techniques to quantify spike ripples and test whether this biomarker predicts seizure risk in childhood epilepsy. We evaluated spike ripples in scalp EEG recordings from a prospective cohort of children with a self-limited epilepsy syndrome, benign epilepsy with centrotemporal spikes, and healthy control children. We compared the rate of spike ripples between children with epilepsy and healthy controls, and between children with epilepsy during periods of active disease (active, within 1 year of seizure) and after a period of sustained seizure-freedom (seizure-free, >1 year without seizure), using semi-automated and automated detection techniques. Spike ripple rate was higher in subjects with active epilepsy compared to healthy controls (P = 0.0018) or subjects with epilepsy who were seizure-free ON or OFF medication (P = 0.0018). Among epilepsy subjects with spike ripples, each month seizure-free decreased the odds of a spike ripple by a factor of 0.66 [95% confidence interval (0.47, 0.91), P = 0.021]. Comparing the diagnostic accuracy of the presence of at least one spike ripple versus a classic spike event to identify group, we found comparable sensitivity and negative predictive value, but greater specificity and positive predictive value of spike ripples compared to spikes (P = 0.016 and P = 0.006, respectively). We found qualitatively consistent results using a fully automated spike ripple detector, including comparison with an automated spike detector. We conclude that scalp spike ripple events identify disease and track with seizure risk in this epilepsy population, using both semi-automated and fully automated detection methods, and that this biomarker outperforms analysis of spikes alone in categorizing seizure risk. These data provide evidence that spike ripples are a specific non-invasive biomarker for seizure risk in benign epilepsy with centrotemporal spikes and support future work to evaluate the utility of this biomarker to guide medication trials and tapers in these children and predict seizure risk in other at-risk populations.

Latent Phase Detection of Hypoxic-Ischemic Spike Transients in the EEG of Preterm Fetal Sheep Using Reverse Biorthogonal Wavelets & Fuzzy Classifier

Hypoxic-ischemic (HI) studies in preterms lack reliable prognostic biomarkers for diagnostic tests of HI encephalopathy (HIE). Our group's observations from in utero fetal sheep models suggest that potential biomarkers of HIE in the form of developing HI micro-scale epileptiform transients emerge along suppressed EEG/ECoG background during a latent phase of 6-7h post-insult. However, having to observe for the whole of the latent phase disqualifies any chance of clinical intervention. A precise automatic identification of these transients can help for a well-timed diagnosis of the HIE and to stop the spread of the injury before it becomes irreversible. This paper reports fusion of Reverse-Biorthogonal Wavelets with Type-1 Fuzzy classifiers, for the accurate real-time automatic identification and quantification of high-frequency HI spike transients in the latent phase, tested over seven in utero preterm sheep. Considerable high performance of 99.78 ± 0.10% was obtained from the Rbio-Wavelet Type-1 Fuzzy classifier for automatic identification of HI spikes tested over 42h of high-resolution recordings (sampling-freq:1024Hz). Data from post-insult automatic time-localization of high-frequency HI spikes reveals a promising trend in the average rate of the HI spikes, even in the animals with shorter occlusion periods, which highlights considerable higher number of transients within the first 2h post-insult.

Interictal Epileptiform Discharge Detection in EEG in Different Practice Settings

OBJECTIVE

The goal of the study was to measure the performance of academic and private practice (PP) neurologists in detecting interictal epileptiform discharges in routine scalp EEG recordings.

METHODS

Thirty-five EEG scorers (EEGers) participated (19 academic and 16 PP) and marked the location of ETs in 200 30-second EEG segments using a web-based EEG annotation system. All participants provided board certification status, years of Epilepsy Fellowship Training (EFT), and years in practice. The Persyst P13 automated IED detection algorithm was also run on the EEG segments for comparison.

RESULTS

Academic EEGers had an average of 1.66 years of EFT versus 0.50 years of EFT for PP EEGers (P < 0.0001) and had higher rates of board certification. Inter-rater agreement for the 35 EEGers was fair. There was higher performance for EEGers in academics, with at least 1.5 years of EFT, and with American Board of Clinical Neurophysiology and American Board of Psychiatry and Neurology-E specialty board certification. The Persyst P13 algorithm at its default setting (perception value = 0.4) did not perform as well at the EEGers, but at substantially higher perception value settings, the algorithm performed almost as well human experts.

CONCLUSIONS

Inter-rater agreement among EEGers in both academic and PP settings varies considerably. Practice location, years of EFT, and board certification are associated with significantly higher performance for IED detection in routine scalp EEG. Continued medical education of PP neurologists and neurologists without EFT is needed to improve routine scalp EEG interpretation skills. The performance of automated detection algorithms is approaching that of human experts.

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%.

Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN

Epilepsy is a neurological disorder that affects millions of people worldwide. Monitoring the brain activities and identifying the seizure source which starts with spike detection are important steps for epilepsy treatment. Magnetoencephalography (MEG) is an emerging epileptic diagnostic tool with high-density sensors; this makes manual analysis a challenging task due to the vast amount of MEG data. This paper explores the use of eight statistical features and genetic programing (GP) with the K-nearest neighbor (KNN) for interictal spike detection. The proposed method is comprised of three stages: preprocessing, genetic programming-based feature generation, and classification. The effectiveness of the proposed approach has been evaluated using real MEG data obtained from 28 epileptic patients. It has achieved a 91.75% average sensitivity and 92.99% average specificity.

Interictal epileptiform discharge characteristics underlying expert interrater agreement

OBJECTIVE

The presence of interictal epileptiform discharges (IED) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. However, inter-rater agreement (IRA) regarding the presence of IED is imperfect, leading to incorrect and delayed diagnoses. An improved understanding of which IED attributes mediate expert IRA might help in developing automatic methods for IED detection able to emulate the abilities of experts. Therefore, using a set of IED scored by a large number of experts, we set out to determine which attributes of IED predict expert agreement regarding the presence of IED.

METHODS

IED were annotated on a 5-point scale by 18 clinical neurophysiologists within 200 30-s EEG segments from recordings of 200 patients. 5538 signal analysis features were extracted from the waveforms, including wavelet coefficients, morphological features, signal energy, nonlinear energy operator response, electrode location, and spectrogram features. Feature selection was performed by applying elastic net regression and support vector regression (SVR) was applied to predict expert opinion, with and without the feature selection procedure and with and without several types of signal normalization.

RESULTS

Multiple types of features were useful for predicting expert annotations, but particular types of wavelet features performed best. Local EEG normalization also enhanced best model performance. As the size of the group of EEGers used to train the models was increased, the performance of the models leveled off at a group size of around 11.

CONCLUSIONS

The features that best predict inter-rater agreement among experts regarding the presence of IED are wavelet features, using locally standardized EEG. Our models for predicting expert opinion based on EEGer's scores perform best with a large group of EEGers (more than 10).

SIGNIFICANCE

By examining a large group of EEG signal analysis features we found that wavelet features with certain wavelet basis functions performed best to identify IEDs. Local normalization also improves predictability, suggesting the importance of IED morphology over amplitude-based features. Although most IED detection studies in the past have used opinion from three or fewer experts, our study suggests a "wisdom of the crowd" effect, such that pooling over a larger number of expert opinions produces a better correlation between expert opinion and objectively quantifiable features of the EEG.

Deviations from Critical Dynamics in Interictal Epileptiform Activity

The framework of criticality provides a unifying perspective on neuronal dynamics from in vitro cortical cultures to functioning human brains. Recent findings suggest that a healthy cortex displays critical dynamics, giving rise to scale-free spatiotemporal cascades of activity, termed neuronal avalanches. Pharmacological manipulations of the excitation-inhibition balance (EIB) in cortical cultures were previously shown to result in deviations from criticality and from the power law scaling of avalanche size distribution. To examine the sensitivity of neuronal avalanche metrics to altered EIB in humans, we focused on epilepsy, a neurological disorder characterized by hyperexcitable networks. Using magnetoencephalography, we quantitatively assessed deviations from criticality in the brain dynamics of patients with epilepsy during interictal (between-seizures) activity. Compared with healthy control subjects, epilepsy patients tended to exhibit a higher neural gain and larger avalanches, particularly during interictal epileptiform activity. Moreover, deviations from scale-free behavior were exclusively connected to brief intervals at epileptiform discharges, strengthening the association between deviations from criticality and the instantaneous changes in EIB. The avalanches collected during interictal epileptiform activity had not only a stereotypical size range but also involved particular spatial patterns of activations, as expected for periods of epileptic network dominance. Overall, the neuronal avalanche metrics provide a quantitative novel description of interictal brain activity of patients with epilepsy.

SIGNIFICANCE STATEMENT

Healthy brain dynamics requires a delicate balance between excitatory and inhibitory processes. Several brain disorders, such as epilepsy, are associated with altered excitation-inhibition balance, but assessing this balance using noninvasive tools is still challenging. In this study, we apply the framework of critical brain dynamics to data from epilepsy patients, which were recorded between seizures. We show that metrics of criticality provide a sensitive tool for noninvasive assessment of changes in the balance. Specifically, brain activity of epilepsy patients deviates from healthy critical brain dynamics, particularly during abnormal epileptiform activity. The study offers a novel quantitative perspective on epilepsy and its relation to healthy brain dynamics.

Surface and intracranial EEG spike detection based on discrete wavelet decomposition and random forest classification

Epilepsy is a neurological disorder for which the electroencephalogram (EEG) is the most important diagnostic tool. In particular, this diagnosis heavily depends on the detection of interictal (between seizures) paroxysmal epileptic discharges (IPED) in the EEG. This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist visual inspections of human readers. We present a new method, which allows automatic detection of IPED based on discrete wavelet decomposition and a random forest classifier. The algorithm was trained and cross validated using 17 subjects with scalp EEG and 10 subjects with intracranial EEG. The performance of this method reached 62% recall and 26% precision for surface EEG subjects and 63% recall and 53% precision for intracranial EEG subjects. Thus, the method hereby proposed has great potential for diagnosis support in clinical environments.

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.

Characteristics of EEG Interpreters Associated With Higher Interrater Agreement

PURPOSE

The goal of the project is to determine characteristics of academic neurophysiologist EEG interpreters (EEGers), which predict good interrater agreement (IRA) and to determine the number of EEGers needed to develop an ideal standardized testing and training data set for epileptiform transient (ET) detection algorithms.

METHODS

A three-phase scoring method was used. In phase 1, 19 EEGers marked the location of ETs in two hundred 30-second segments of EEG from 200 different patients. In phase 2, EEG events marked by at least 2 EEGers were annotated by 18 EEGers on a 5-point scale to indicate whether they were ETs. In phase 3, a third opinion was obtained from EEGers on any inconsistencies between phase 1 and phase 2 scoring.

RESULTS

The IRA for the 18 EEGers was only fair. A select group of the EEGers had good IRA and the other EEGers had low IRA. Board certification by the American Board of Clinical Neurophysiology was associated with better IRA performance but other board certifications, years of fellowship training, and years of practice were not. As the number of EEGers used for scoring is increased, the amount of change in the consensus opinion decreases steadily and is quite low as the group size approaches 10.

CONCLUSIONS

The IRA among EEGers varies considerably. The EEGers must be tested before use as scorers for ET annotation research projects. The American Board of Clinical Neurophysiology certification is associated with improved performance. The optimal size for a group of experts scoring ETs in EEG is probably in the 6 to 10 range.

Robust Wavelet Stabilized 'Footprints of Uncertainty' for Fuzzy System Classifiers to Automatically Detect Sharp Waves in the EEG after Hypoxia Ischemia

Currently, there are no developed methods to detect sharp wave transients that exist in the latent phase after hypoxia-ischemia (HI) in the electroencephalogram (EEG) in order to determine if these micro-scale transients are potential biomarkers of HI. A major issue with sharp waves in the HI-EEG is that they possess a large variability in their sharp wave profile making it difficult to build a compact 'footprint of uncertainty' (FOU) required for ideal performance of a Type-2 fuzzy logic system (FLS) classifier. In this paper, we develop a novel computational EEG analysis method to robustly detect sharp waves using over 30[Formula: see text]h of post occlusion HI-EEG from an equivalent, in utero, preterm fetal sheep model cohort. We demonstrate that initial wavelet transform (WT) of the sharp waves stabilizes the variation in their profile and thus permits a highly compact FOU to be built, hence, optimizing the performance of a Type-2 FLS. We demonstrate that this method leads to higher overall performance of [Formula: see text] for the clinical [Formula: see text] sampled EEG and [Formula: see text] for the high resolution [Formula: see text] sampled EEG that is improved upon over conventional standard wavelet [Formula: see text] and [Formula: see text], respectively, and fuzzy approaches [Formula: see text] and [Formula: see text], respectively, when performed in isolation.

Spike pattern recognition by supervised classification in low dimensional embedding space

Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts' manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min-1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.

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.