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Objective. Because of the lack of highly skilled experts, automated technologies that support electroencephalogram (EEG)-based in epilepsy diagnosis are advancing. Deep convolutional neural network-based models have been used successfully for detecting epileptic spikes, one of the biomarkers, from EEG. However, a sizeable number of supervised EEG records are required for training. Approach. This study introduces the Satelight model, which uses the self-attention (SA) mechanism. The model was trained using a clinical EEG dataset labeled by five specialists, including 16 008 epileptic spikes and 15 478 artifacts from 50 children. The SA mechanism is expected to reduce the number of parameters and efficiently extract features from a small amount of EEG data. To validate the effectiveness, we compared various spike detection approaches with the clinical EEG data. Main results. The experimental results showed that the proposed method detected epileptic spikes more effectively than other models (accuracy = 0.876 and false positive rate = 0.133). Significance. The proposed model had only one-tenth the number of parameters as the other effective model, despite having such a high detection performance. Further exploration of the hidden parameters revealed that the model automatically attended to the EEG's characteristic waveform locations of interest., (Creative Commons Attribution license.)

The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential. In this study, we propose a system to classify EEG as epileptic or normal based on multiple modalities extracted from the interictal EEG. The ensemble system consists of three components: a Convolutional Neural Network (CNN)-based IED detector, a Template Matching (TM)-based IED detector, and a spectral feature-based classifier. We evaluate the system on datasets from six centers from the USA, Singapore, and India. The system yields a mean Leave-One-Institution-Out (LOIO) cross-validation (CV) area under curve (AUC) of 0.826 (balanced accuracy (BAC) of 76.1%) and Leave-One-Subject-Out (LOSO) CV AUC of 0.812 (BAC of 74.8%). The LOIO results are found to be similar to the interrater agreement (IRA) reported in the literature for epileptic EEG classification. Moreover, as the proposed system can process routine EEGs in a few seconds, it may aid the clinicians in diagnosing epilepsy efficiently.

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.

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., Competing Interests: Declaration of competing interest None of the authors has any conflict of interest to disclose., (Copyright © 2020 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Objective: The process of manually marking up epileptic spikes for simultaneous electroencephalogram (EEG) and resting state functional MRI (rsfMRI) analysis in epilepsy studies is a tedious and subjective task for a human expert. The aim of this study was to evaluate whether automatic EEG spike detection can facilitate EEG-rsfMRI analysis, and to assess its potential as a clinical tool in epilepsy., Methods: We implemented a fast algorithm for detection of uniform interictal epileptiform discharges (IEDs) in one-hour scalp EEG recordings of 19 refractory focal epilepsy datasets (from 16 patients) who underwent a simultaneous EEG-rsfMRI recording. Our method was based on matched filtering of an IED template (derived from human markup) used to automatically detect other 'similar' EEG events. We compared simultaneous EEG-rsfMRI results between automatic IED detection and standard analysis with human EEG markup only., Results: In contrast to human markup, automatic IED detection takes a much shorter time to detect IEDs and export an output text file containing spike timings. In 13/19 focal epilepsy datasets, statistical EEG-rsfMRI maps based on automatic spike detection method were comparable with human markup, and in 6/19 focal epilepsy cases automatic spike detection revealed additional brain regions not seen with human EEG markup. Additional events detected by our automated method independently revealed similar patterns of activation to a human markup. Overall, automatic IED detection provides greater statistical power in EEG-rsfMRI analysis compared to human markup in a short timeframe., Conclusions: Automatic spike detection is a simple and fast method that can reproduce comparable and, in some cases, even superior results compared to the common practice of manual EEG markup in EEG-rsfMRI analysis of epilepsy., Significance: Our study shows that IED detection algorithms can be effectively used in epilepsy clinical settings. This work further helps in translating EEG-rsfMRI research into a fast, reliable and easy-to-use clinical tool for epileptologists., (Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Long term EEG examinations, for example during epilepsy diagnosis, can be performed more efficiently with support of automated abnormality detection. Currently, these methods are usually developed based on one specific database, which limits the possibilities of generalizations. Here, we present a machine learning solution for detection of interictal abnormal EEG segments optimized on the publically available TUH Abnormal EEG Corpus. The classifier is further re-trained and tested on several combinations of publicly available data sets. The results achieved internally on the datasets are comparable to the known state of the art, while training and testing on different sources produced accuracy in the range of 67.51% to 99.50%. Lower accuracy is achieved when the training data set is highly preprocessed and relatively small.

Objective: A prerequisite for the implementation of interictal electroencephalography-correlated functional magnetic resonance imaging (EEG-fMRI) in the presurgical work-up for epilepsy surgery is straightforward processing. We propose a new semi-automatic method as alternative for the challenging and time-consuming visual spike identification., Methods: Our method starts from a patient-specific spike-template, built by averaging spikes recorded on the EEG outside the scanner. Spatiotemporal cross-correlations between the template and the EEG measured during fMRI were calculated. To minimize false-positive detections, this time course of cross-correlations was binarized by means of a spike-template-specific threshold determined in healthy controls. To inform our model for statistical parametric mapping, this binarized regressor was convolved with the canonical hemodynamic response function. We validated our "template-based" method in 21 adult patients with refractory focal epilepsy with a well-defined epileptogenic zone and interictal spikes during EEG-fMRI. Sensitivity and specificity for detecting the epileptogenic zone were calculated and represented in receiver operating characteristic (ROC) curves. Our approach was compared with a previously proposed semiautomatic "topography-based" method that used the topographic amplitude distribution of spikes as a starting point for correlation-based fitting., Results: Good diagnostic performance could be reached with our template-based method. The optimal area under the ROC curve was 0.77. Diagnostic performance of the topography-based method was overall low., Significance: Our new template-based method is more standardized and time-saving than visual spike identification on intra-scanner EEG recordings, and preserves good diagnostic performance for detecting the epileptogenic zone., (Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.)

Background: Children with benign childhood epilepsy with centro-temporal spikes (BECT) have spikes, sharps, and composite waves on their electroencephalogram (EEG). It is necessary to detect spikes to diagnose BECT clinically. The templatematchingmethod can identify spikes effectively. However, due to the individual specificity, finding representative templates to detect spikes in actual applications is often challenging. Purpose: This paper proposes a spike detection method using functional brain networks based on phase locking value (FBN-PLV) and deep learning. Methods: To obtain high detection effect, this method uses a specific template matching method and the 'peak-to-peak' phenomenon of montages to obtain a set of candidate spikes. With the set of candidate spikes, functional brain networks (FBN) are constructed based on phase locking value (PLV) to extract the features of the network structure during spike discharge with phase synchronization. Finally, the time domain features of the candidate spikes and the structural features of the FBN-PLV are input into the artificial neural network (ANN) to identify the spikes. Results: Based on FBN-PLV and ANN, the EEG data sets of four BECT cases from the Children's Hospital, Zhejiang University School of Medicine are tested with the AC of 97.6%, SE of 98.3%, and SP 96.8%. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, these analyses are manually performed by neurophysiologists and are time-consuming. Another problem is that spike identification from MEG waveforms largely depends on neurophysiologists’ skills and experiences. These problems cause poor cost-effectiveness in clinical MEG examination. To overcome these problems, we fully automated spike identification and ECD estimation using a deep learning approach fully automated AI-based MEG interictal epileptiform discharge identification and ECD estimation (FAMED). We applied a semantic segmentation method, which is an image processing technique, to identify the appropriate times between spike onset and peak and to select appropriate sensors for ECD estimation. FAMED was trained and evaluated using clinical MEG data acquired from 375 patients. FAMED training was performed in two stages: in the first stage, a classification network was learned, and in the second stage, a segmentation network that extended the classification network was learned. The classification network had a mean AUC of 0.9868 (10-fold patient-wise cross-validation); the sensitivity and specificity were 0.7952 and 0.9971, respectively. The median distance between the ECDs estimated by the neurophysiologists and those using FAMED was 0.63 cm. Thus, the performance of FAMED is comparable to that of neurophysiologists, and it can contribute to the efficiency and consistency of MEG ECD analysis. [ABSTRACT FROM AUTHOR]

Centrotemporal spike-waves (CTSWs) are typical interictal epileptiform discharges (IEDs) observed in centrotemporal regions in self-limited epilepsy with centrotemporal spikes (SLECTS). This study aims to develop a deep learning-based approach for automated detection of CTSWs in scalp electroencephalography (EEG) recordings of patients with SLECTS. To lower the substantial burden of IED annotation on clinicians, we simplified it by limiting IEDs to CTSWs because electroencephalographic patterns of CTSWs are known to be highly consistent. Two neurologists annotated 1672 CTSWs of 20 patients with SLECTS. Thereafter, we performed a two-level CTSW detection procedure: epoch-level and EEG-level. In the epoch-level detection, we constructed convolutional neural network-based classification models for CTSW and non-CTSW binary classification using the recordings of 20 patients and 20 controls. We then set the thresholds of the classification models for 100% specificity. In the EEG-level detection, we applied the threshold-adjusted classification models to the recordings of 50 patients and 50 controls that were not used in the epoch-level detection to distinguish between CTSW-positive (with one or more CTSWs) and CTSW-negative (with no CTSW) recordings based on the detection of CTSW presence. We obtained an average sensitivity, specificity, and accuracy of 99.8%, 98.4%, and 99.1%, respectively, with an average false detection rate of 0.19/hr for the controls. Our approach showed high detectability for CTSWs despite the simplified annotation process. We expect that the proposed CTSW detectors have potential clinical usefulness for efficiently reading EEGs and diagnosing SLECTS, and can significantly reduce the burden of IED annotation on clinicians. [ABSTRACT FROM AUTHOR]

Interictal epileptiform spike (referred to as spike) detected from electroencephalograms lasting only 20- to 200-ms can provide a reliable evidence-based indicator for clinical seizure type diagnosis. Recent feature representation approaches focus either on the concrete-level or abstract-level information mining of the spike, thus demonstrating suboptimal detection performance. Additionally, existing abstract-level information mining methods of the spike based deep learning networks have not realized the effective feature representation of long-term dependent distinguished information within similar waveform cycles caused by morphological heterogeneity, which affects detection performance. Thus, a multilevel feature learning method for accurate spike detection was proposed in this study. Specifically, the spatio-temporal-frequency multidomain information in concrete-level first are inferred the common mimetic properties of the spike using the multidomain feature extractors. Then, the effective feature representation of long-term dependent distinguished information within similar waveform cycles caused by morphological heterogeneity is suitably captured using the temporal convolutional network. Finally, the spatio-temporal-frequency multidomain long-term dependent feature representation of spike is calculated using the element-wise manner to fuse the feature representation in concrete- and abstract-levels. The experimental results indicate that the proposed method can achieve an accuracy of 90.62±1.38%, sensitivity of 90.38±1.52%, specificity of 91.00±1.60%, precision of 90.33±4.71%, and the false detection rate per minute is $0.148\pm 0.020\text{m}^{-1}$ , which are higher than when using the feature representation in the concrete-or abstract-level alone. Additionally, the detection results indicate that the proposed method avoids the subjectivity and inefficiency of visual inspection, and it enables a highly accurate detection of the spike. [ABSTRACT FROM AUTHOR]

To cope with the lack of highly skilled professionals, machine learning with proper signal processing is key for establishing automated diagnostic-aid technologies with which to conduct epileptic electroencephalogram (EEG) testing. In particular, frequency filtering with the appropriate passbands is essential for enhancing the biomarkers—such as epileptic spike waves—that are noted in the EEG. This paper introduces a novel class of neural networks (NNs) that have a bank of linear-phase finite impulse response filters at the first layer as a preprocessor that can behave as bandpass filters that extract biomarkers without destroying waveforms because of a linear-phase condition. Besides, the parameters of the filters are also data-driven. The proposed NNs were trained with a large amount of clinical EEG data, including 15 833 epileptic spike waveforms recorded from 50 patients, and their labels were annotated by specialists. In the experiments, we compared three scenarios for the first layer: no preprocessing, discrete wavelet transform, and the proposed data-driven filters. The experimental results show that the trained data-driven filter bank with supervised learning behaves like multiple bandpass filters. In particular, the trained filter passed a frequency band of approximately 10–30 Hz. Moreover, the proposed method detected epileptic spikes, with the area under the receiver operating characteristic curve of 0.967 in the mean of 50 intersubject validations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract The electroencephalogram (EEG) is a tool for diagnosing epilepsy; by analyzing it, neurologists can identify alterations in brain activity associated with epilepsy. However, this task is not always easy to perform because of the duration of the EEG or the subjectivity of the specialist in detecting alterations. Aim To propose the use of an epileptic spike detector based on a matched filter and a neural network for supporting the diagnosis of epilepsy through a tool capable of automatically detecting spikes in pediatric EEGs. Results Automatic detection of spikes from an EEG waveform involved the creation of an epileptic spike template. The template was used in order to detect spikes by using a matched filter, and each spike detected was confirmed by a Neural Network to improve sensitivity and specificity. Thus, the detector developed achieved a sensitivity of 99.96% which is better than the range of what has been reported in the literature (82.68% and 94.4%), and a specificity of 99.26%, improving the specificity found in the best-reviewed studies. Conclusions Considering the results obtained in the evaluation, the solution becomes a promising alternative to support the automatic identification of epileptic spikes by neurologists.

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

Epilepsy is a neurological disorder characterized by sudden and unpredictable epileptic seizures, which incurs significant negative impacts on patients’ physical, psychological and social health. A practical approach to assist with the clinical assessment and treatment planning for patients is to process magnetoencephalography (MEG) data to identify epileptogenic zones. As a widely accepted biomarker of epileptic foci, epileptic MEG spikes need to be precisely detected. Given that the visual inspection of spikes is time consuming, an automatic and efficient system with adequate accuracy for spike detection is valuable in clinical practice. However, current approaches for MEG spike autodetection are dependent on hand-engineered features. Here, we propose a novel multiview Epileptic MEG Spikes detection algorithm based on a deep learning Network (EMS-Net) to accurately and efficiently recognize the spike events from MEG raw data. The results of the leave-k-subject-out validation tests for multiple datasets (i.e., balanced and realistic datasets) showed that EMS-Net achieved state-of-the-art classification performance (i.e., accuracy: 91.82% – 99.89%; precision: 91.90% – 99.45%; sensitivity: 91.61% – 99.53%; specificity: 91.60% – 99.96%; f1 score: 91.70% – 99.48%; and area under the curve: 0.9688 – 0.9998). [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) is a tool for diagnosing epilepsy; by analyzing it, neurologists can identify alterations in brain activity associated with epilepsy. However, this task is not always easy to perform because of the duration of the EEG or the subjectivity of the specialist in detecting alterations. Aim: To propose the use of an epileptic spike detector based on a matched filter and a neural network for supporting the diagnosis of epilepsy through a tool capable of automatically detecting spikes in pediatric EEGs. Results: Automatic detection of spikes from an EEG waveform involved the creation of an epileptic spike template. The template was used in order to detect spikes by using a matched filter, and each spike detected was confirmed by a Neural Network to improve sensitivity and specificity. Thus, the detector developed achieved a sensitivity of 99.96% which is better than the range of what has been reported in the literature (82.68% and 94.4%), and a specificity of 99.26%, improving the specificity found in the best-reviewed studies. Conclusions: Considering the results obtained in the evaluation, the solution becomes a promising alternative to support the automatic identification of epileptic spikes by neurologists. [ABSTRACT FROM AUTHOR]

Patients undergoing presurgical evaluation for resective epilepsy surgery require the localization of the epileptogenic zone. The detection and analysis of interictal and ictal epileptiform spikes are of major importance for identifying this area. The “irritative zone” of the cortex with interictal spikes is usually revealed intraoperatively during acute electrocorticography (ECoG). Since ECoG recordings cannot be completely visually reviewed in a reasonable amount of time, computer algorithms for the automatic detection of seizures and spikes were developed. We proposed the spike detection algorithm based on the definition of the physical properties of the sought spikes (duration and amplitude). The parameters are easily interpreted by neurophysiologists. The detection sensitivity was 93%, and the precision was 99%. In addition, based on the number of spikes detected on individual electrodes, it is possible to draw the “frequency of spike occurrence” map. The algorithm proved to be so effective in analyzing ECoG records, as it is allowed to be used in practice. [ABSTRACT FROM AUTHOR]

Accurate automatic spike detection is highly beneficial to clinical assessment of epileptic electroencephalogram (EEG) data. In this paper, a new two-stage approach is proposed for epileptic spike detection. First, the k-point nonlinear energy operator (k-NEO) is adopted to detect all possible spike candidates, then a newly proposed spike model with slow wave features is applied to these candidates for spike classification. Experimental results show that the proposed system, using the AdaBoost classifier, outperforms the conventional method in both two- and three-class EEG pattern classification problems. The proposed system not only achieves better accuracy for spike detection, but also provides new ability to differentiate between spikes and spikes with slow waves. Though spikes with slow waves occur frequently in epileptic EEGs, they are not used in conventional spike detection. Identifying spikes with slow waves allows the proposed system to have better capability for assisting clinical neurologists in routine EEG examinations and epileptic diagnosis.

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), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment. [ABSTRACT FROM AUTHOR]

Highlights: [•] A novel spike and inverted spike detection algorithm. [•] Generalization to detect multi-channel simultaneous spikes. [•] Simultaneous occurrence of spikes and inverted spikes across focal channels are more after the seizure than during it. [ABSTRACT FROM AUTHOR]

Accurate automatic spike detection is highly beneficial to clinical assessment of epileptic electroencephalogram (EEG) data. In this paper, a new two-stage approach is proposed for epileptic spike detection. First, the k-point nonlinear energy operator (k-NEO) is adopted to detect all possible spike candidates, then a newly proposed spike model with slow wave features is applied to these candidates for spike classification. Experimental results show that the proposed system, using the AdaBoost classifier, outperforms the conventional method in both two- and three-class EEG pattern classification problems. The proposed system not only achieves better accuracy for spike detection, but also provides new ability to differentiate between spikes and spikes with slow waves. Though spikes with slow waves occur frequently in epileptic EEGs, they are not used in conventional spike detection. Identifying spikes with slow waves allows the proposed system to have better capability for assisting clinical neurologists in routine EEG examinations and epileptic diagnosis. [ABSTRACT FROM AUTHOR]

Abstract: Methods for rapid and objective quantification of interictal spikes in raw, unprocessed electroencephalogram (EEG) samples are scarce. We evaluated the accuracy of a tailored automated spike quantification algorithm. The automated quantification was compared with the quantification by two board-certified clinical neurophysiologists (gold-standard) in five steps: 1) accuracy in a single EEG channel (5 EEG samples), 2) accuracy in multiple EEG channels and across different stages of the sleep-wake cycles (75 EEG samples), 3) capacity to detect lateralization of spikes (6 EEG samples), 4) accuracy after application of a machine-learning mechanism (11 EEG samples), and 5) accuracy during wakefulness only (8 EEG samples). Our method was accurate during all stages of the sleep-wake cycle and improved after the application of the machine-learning mechanism. Spikes were correctly lateralized in all cases. Our automated method was accurate in quantifying and detecting the lateralization of interictal spikes in raw unprocessed EEG samples. [Copyright &y& Elsevier]

Abstract: Visual quantification of interictal epileptiform activity is time consuming and requires a high level of expert''s vigilance. This is especially true for overnight recordings of patient suffering from epileptic encephalopathy with continuous spike and waves during slow-wave sleep (CSWS) as they can show tens of thousands of spikes. Automatic spike detection would be attractive for this condition, but available algorithms have methodological limitations related to variation in spike morphology both between patients and within a single recording. We propose a fully automated method of interictal spike detection that adapts to interpatient and intrapatient variation in spike morphology. The algorithm works in five steps. (1) Spikes are detected using parameters suitable for highly sensitive detection. (2) Detected spikes are separated into clusters. (3) The number of clusters is automatically adjusted. (4) Centroids are used as templates for more specific spike detections, therefore adapting to the types of spike morphology. (5) Detected spikes are summed. The algorithm was evaluated on EEG samples from 20 children suffering from epilepsy with CSWS. When compared to the manual scoring of 3 EEG experts (3 records), the algorithm demonstrated similar performance since sensitivity and selectivity were 0.3% higher and 0.4% lower, respectively. The algorithm showed little difference compared to the manual scoring of another expert for the spike-and-wave index evaluation in 17 additional records (the mean absolute difference was 3.8%). This algorithm is therefore efficient for the count of interictal spikes and determination of a spike-and-wave index. [Copyright &y& Elsevier]

Purpose: The burden of reviewing long-term scalp electroencephalography (EEG) is not much alleviated by automated spike detection if thousands of events need to be inspected and mentally classified by the reviewer. This study investigated a novel technique of clustering and 24-h hyper-clustering on top of automated detection to assess whether fast review of focal interictal spike types was feasible and comparable to the spikes types observed during routine EEG review in epilepsy monitoring. Methods: Spike detection used a transformation of scalp EEG into 29 regional source activities and adaptive thresholds to increase sensitivity. Our rule-based algorithm estimated 18 parameters around each detected peak and combined multichannel detections into one event. Similarity measures were derived from equivalent location, scalp topography, and source waveform of each event to form clusters over 2-h epochs using a density-based algorithm. Similar measures were applied to all 2-h clusters to form 24-h hyper-clusters. Independent raters evaluated electroencephalography data of 50 patients with epilepsy (25 children) using traditional visual spike review and optimized hyper-cluster inspection. Congruence between visual spike types and epileptiform hyper-clusters was assessed on a sublobar level using three-dimensional (3D) peak topographies. Key Findings: Visual rating found 126 different epileptiform spike types (2.5 per patient). Independently, 129 hyper-clusters were classified as epileptiform and originating in separate sublobar regions (2.6 per patient). Ninety-one percent of visual spike types matched with hyper-clusters (temporal lobe spikes 94%, extratemporal 89%). Conversely, 11% of hyper-clusters rated epileptiform had no corresponding visual spike type. Numbers were comparable in adults and children. On average, 15 hyper-clusters had to be inspected and rated per patient with an evaluation time of around 5 min. Significance: Hyper-clustering over 24 h provides an independent tool for rapid daily evaluation of interictal spikes in long-term video-EEG monitoring. If used in addition to routine review of 2-5 min EEG per hour, sensitivity and reliability in noninvasive diagnosis of focal epilepsy increases. [ABSTRACT FROM AUTHOR]

Real signals are often corrupted by noise with a power spectrum variable over time. In applications involving these signals, it is expected that dynamically estimating and correcting for this noise would increase the amount of useful information extracted from the signal. One such application is scalp EEG monitoring in epilepsy, where electrical activity generated by cranio-facial muscles obscure the measured brainwaves. This paper presents a data-selection algorithm based on phase congruency to identify interictal spikes from background EEG; together with a novel statistical method that allows a more comprehensive trade-off based quantitative comparison of two algorithms which have been tested at a fixed threshold in the same database. Here, traditional phase congruency has been modified to incorporate a dynamic estimate of muscle activity present in the input scalp EEG signal. The proposed algorithm achieves 50% data reduction whilst detecting more than 80% of interictal spikes. This represents a significant improvement over the state-of-the-art denoising method for phase congruency. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces different classification systems based on artificial neural networks for the automatic detection of epileptic spikes in electroencephalogram records. Different multilayer perceptron networks are constructed and trained with different algorithms. The inputs of the networks consist of either raw data or extracted features. To improve the generalization performance of the classifiers, “training with noise” method is used whereby new training data is constructed by adding uncorrelated Gaussian noise to real data. The performances of the constructed classifiers are examined and compared both with each other and with other similar systems found in literature based on sensitivity, specificity and selectivity measures. [Copyright &y& Elsevier]

Many studies have focused on the nonlinear analysis of electroencephalography mainly for the characterization of epileptic brain states. The spatial and temporal dynamics of the epileptogenic process is still not clear completely especially the most challenging aspects of epileptology which is the anticipation of the seizure. Despite all the efforts we still don't know how and when and why the seizure occurs. However actual studies bring strong evidence that the interictal-ictal state transition is not an abrupt phenomena. Findings also indicate that it is possible to detect a preseizure phase. We will study the patients admitted to the epilepsy monitoring unit for the purpose of recording their seizures. These patients have their EEG signal recorded 24 hours a day for several days until they have enough number of seizures to determine eligibility for seizure surgery. Thus, preictal, ictal, and post ictal electroencephalography recordings are available on such patients for analysis. We propose to use wavelet analysis in order to investigate a case study of the electroencephalography signal and determine the localization of the seizure and its characteristics. [ABSTRACT FROM AUTHOR]

Summary: Purpose: The purpose was to quantify changes of epileptiform activity during ketogenic diet (KD) treatment in children with therapy resistant epilepsy, and evaluate how these changes are related to activity stage and to clinical effects on seizure frequency, seizure severity, attentional behaviour, quality of life (QOL), and beta-hydroxybutyrate (βOHb). Methods: Eighteen children were investigated with 24h ambulatory EEG monitoring 1 week prior to KD initiation and, after 3 months of KD treatment. Epileptiform activity was evaluated by automated spike detection. This data was compared with data presented in a previous study published in Epilepsia 2006, on sleep structure and different activity stages, clinical data on seizure frequency, seizure severity, QOL and attentional behaviour on the same children [Hallbook, T., Lundgren, J., Rosén, I., 2007. Ketogenic diet improves sleep quality in children with therapy resistant epilepsy. Epilepsia 48, 59–65]. Results: After 3 months of KD treatment the number of interictal epileptiform discharges (IEDs) was significantly reduced (p <0.001). When considering the four activity stages separately, the reduction was significant during non-rapid eye movement sleep stage 2, slow wave sleep (SWS) and rapid eye movement (REM) sleep (p =0.001, 0.001, 0.002), and not significantly so during awake (p =0.07). Beta-hydroxybutyrate was significantly increased (p <0.001). There was a significant correlation between the reduction in IEDs and clinical seizures (Spearman r =0.6, p =0.005) and between improvement in attentional behaviour and the increase in βOHb (Spearman r =0.5, p =0.03). There was no significant correlation between changes in attentional behaviour and IEDs or clinical seizures. Conclusion: This study shows that KD reduces the number of IEDs, especially during sleep. It shows a correlation between reduction in epileptiform activity and clinical seizures. There were no correlations between reduction in epileptiform activity and clinical seizures and improvement in QOL or attention. The increase in βOHb correlated with improvement in attention. [Copyright &y& Elsevier]

In this paper, a spike detection method is introduced. Traditional morphological filter is improved for extracting spikes from epileptic EEG signals and two key problems are addressed: morphological operation design and structure elements optimization. An average weighted combination of open–closing and clos–opening operation, which can eliminate statistical deflection of amplitude, is utilized to separate background EEG and spikes. Then, according to the characteristic of spike component, the structure elements are constructed with two parabolas and a new criterion is put forward to optimize the structure elements. The proposed method is evaluated using normal and epileptic EEG data recorded from 12 test subjects. A comparison between the improved morphological filter, traditional morphological filter and wavelet analysis with Mexican hat function is presented, which indicates that the improved morphological filter is superior in restraining background activities. We demonstrate that the average detection rate of the improved morphological filter is much higher than that of the other two methods, and there is no false detection for normal EEG signals with the proposed method. [Copyright &y& Elsevier]

Abstract: The ballistocardiogram (BCG) represents one of the most prominent sources of artifacts that contaminate the electroencephalogram (EEG) during functional MRI. The BCG artifacts may affect the detection of interictal epileptiform discharges (IED) in patients with epilepsy, reducing the sensitivity of the combined EEG–fMRI method. In this study we improved the BCG artifact correction using a multiple source correction (MSC) approach. On the one hand, a source analysis of the IEDs was applied to the EEG data obtained outside the MRI scanner to prevent the distortion of EEG signals of interest during the correction of BCG artifacts. On the other hand, the topographies of the BCG artifacts were defined based on the EEG recorded inside the scanner. The topographies of the BCG artifacts were then added to the surrogate model of IED sources and a combined source model was applied to the data obtained inside the scanner. The artifact signal was then subtracted without considerable distortion of the IED topography. The MSC approach was compared with the traditional averaged artifact subtraction (AAS) method. Both methods reduced the spectral power of BCG-related harmonics and enabled better detection of IEDs. Compared with the conventional AAS method, the MSC approach increased the sensitivity of IED detection because the IED signal was less attenuated when subtracting the BCG artifacts. The proposed MSC method is particularly useful in situations in which the BCG artifact is spatially correlated and time-locked with the EEG signal produced by the focal brain activity of interest. [Copyright &y& Elsevier]

Abstract: In this study, different systems based on the fuzzy C-means (FCM) clustering algorithm are utilized for the detection of epileptic spikes in electroencephalogram (EEG) records. The systems are constructed as either single or two-stages. In contrast to single-stage systems, the two-stage system comprises a pre-classifier stage realized by a neural network. The FCM based two-stage system is also compared to a similar system implemented using the K-means clustering algorithm. The results imply that an FCM based two-stage system should be preferred as the spike detection system. [Copyright &y& Elsevier]

Abstract: Objective: Interictal spikes in intracranial EEG (iEEG) may correlate with epileptogenic cortex, but review of interictal iEEG is labor intensive. Accurate automated spike detectors are necessary for understanding the role of spikes in epileptogenesis. Methods: The sensitivity, accuracy and reproducibility of three automated iEEG spike detectors were compared against two human EEG readers using iEEG segments from eight patients. A consensus set of detections was generated for detector calibration. Spike verification was calculated after both human EEG readers independently reviewed all detections. Results: Humans and two of the three automated detectors demonstrated comparable accuracy. In four patients, automated spike detection sensitivity was >70% and accuracy was >50%. In the remaining four patients, EEG background morphology resulted in poorer performance. Blinded human verification accuracy was 76.7±6.6% for computer-detected spikes, and 84.5±4.1% for human-detected spikes. Conclusions: Automated iEEG spike detectors perform comparably to humans, but sensitivity and accuracy are patient dependent. Humans verified the majority of computer-detected spikes. Significance: In some patients automated detectors may be used for mapping spike occurrences in epileptic networks. This may reveal associations between spike distribution, seizure onset, and pathology. [Copyright &y& Elsevier]

In this paper, a methodology for the automated detection and classification of transient events in electroencephalographic (EEG) recordings is presented. It is based on association rule mining and classifies transient events into four categories: epileptic spikes, muscle activity, eye blinking activity, and sharp alpha activity. The methodology involves four stages: 1) transient event detection; 2) clustering of transient events and feature extraction; 3) feature discretization and feature subset selection; and 4) association rule mining and classification of transient events. The methodology is evaluated using 25 EEG recordings, and the best obtained accuracy was 87.38%. The proposed approach combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules. [ABSTRACT FROM AUTHOR]

Summary: Purpose: We report long-term effects of vagus nerve stimulation (VNS) on epileptiform activity in 15 children, and how these changes are related to activity stage and to clinical effects on seizure reduction, seizure severity (NHS3) and quality of life (QOL). Methods: Initially, and after 3 and 9 months of VNS-treatment, 15 children were investigated with 24h ambulatory EEG monitoring for spike detection. The number of interictal epileptiform discharges (IEDs) and the inter spike intervals (ISIs) were analysed during 2h in the awake state, and 1h of rapid eye movement (REM)-, spindle- and delta-sleep, respectively. Total number and duration of electrographic seizure episodes were also analysed. Results: At 9 months the total number of IEDs was significantly reduced (p =0.04). There was a tendency of reduction in all activity stages, and significantly so in delta-sleep (p =0.008). Total electrographic seizure number was significantly reduced in the 24h EEG at 3 and 9 months (p =0.03, 0.05). There was a significant concordance in direction of changes in epileptiform activity and electrographic seizures at 9 months (p =0.04). Concordance in direction of changes was seen in 9 of 15 children between clinical seizures and IED (p >0.3), in 10 of 15 children between QOL and IED (p =0.3) and in 8 of 15 children between NHS3 and IED (p >0.3). There was no direct correlation between the extent of improvement in these clinical data and the degree of spike reduction. Conclusion: This study shows that VNS reduces IEDs especially in REM and delta sleep, as well as the number of electrographic seizures. It also shows a concordance between reduction in IEDs and electrographic seizures. [Copyright &y& Elsevier]

For algorithm developers, this review details recent approaches to the problem, compares the accuracy of various algorithms, identifies common testing issues and proposes some solutions. For the algorithm user, e.g. electroencephalograph (EEG) technician or neurologist, this review provides an estimate of algorithm accuracy and comparison to that of human experts. Manuscripts dated from 1975 are reviewed. Progress since Frost''s 1985 review of the state of the art is discussed. Twenty-five manuscripts are reviewed. Many novel methods have been proposed including neural networks and high-resolution frequency methods. Algorithm accuracy is less than that of experts, but the accuracy of experts is probably less than what is commonly believed. Larger record sets will be required for expert-level detection algorithms. [Copyright &y& Elsevier]

Artificial neural networks are widely used for pattern recognition tasks. For spike detection in electroencephalography (EEG), feedforward networks trained by the backpropagation algorithm are preferred by most authors. Opposed to this, we examined the off-line spike detection abilities of a Kohonen feature map (KFM), which is different from feedforward networks in certain aspects. The EEG data for the training set were obtained from patients with intractable partial epilepsies of mesiotemporal ( n = 2) or extratemporal ( n = 2) origin. For each patient the training set for the KFM included the same patterns of background activity and artifacts as well as the typical individual spike patterns. Three different-sized networks were examined (15 × 15 cells, 25 × 25 cells, and 60 × 60 cells in the Kohonen layer). To investigate the quality of spike detection the results obtained with the KFM were compared with the findings of two board-certified electroencephalographers. Application of a threshold based on the partial invariance of spike recognition against translation of the EEG provided an average sensitivity and selectivity of 80.2% at crossover threshold (71%–86%) depending on the networksize and noise. Multichannel EEG processing in real time will be available soon. In conclusion, pattern-based automated spike detection with a KFM is a promising approach in clinical epileptology and seems to be at least as accurate as other more-established methods of spike detection. © 2000 Biomedical Engineering Society. PAC00: 8719Nn, 8780-y [ABSTRACT FROM AUTHOR]

Abstract: Objective: We designed a study, comparing the yield of standard 16-channel longitudinal bipolar montage (double banana) versus a combined 32-channel longitudinal bipolar plus average referential montage (banana-plus), to detect epileptic abnormalities. Methods: We selected 25 consecutive routine EEG samples with a diagnosis of spike or sharp waves in the temporal regions and 25 consecutive focal slowing and 50 normal EEGs. A total of 100 samples were printed in both montages and randomized for reading. Thirty independent EEG readers blinded from the EEG diagnosis were invited to participate. Results: Twenty-two readers successfully completed the test for a total of 4400 answers collected for analysis. The average sensitivity to detect epileptiform discharges for 16 and 32-channel montages was 36.5% and 61%, respectively (P <0.001). The specificity was not significantly different for 16 and 32-channel montages, 95.4% and 92.4%, respectively (P =0.27). However more readers detected false positives on 32-channel montage compared to 16-channel montage. Conclusions: 32-Channel banana-plus montage has a better yield to detect epileptic abnormalities than the 16-channel double banana montage. Significance: Residents and EEG fellows could improve EEG-reading accuracy if taught on a combined 32-channel montage. [Copyright &y& Elsevier]