Interictal Spike Detection Research Articles

Sensor selection and miniaturization limits for detection of interictal epileptiform discharges with wearable EEG

Objective. The goal of this paper is to investigate the limits of electroencephalography (EEG) sensor miniaturization in a set-up consisting of multiple galvanically isolated EEG units to record interictal epileptiform discharges (IEDs), referred to as ‘spikes’, in people with epilepsy. Approach. A dataset of high-density EEG recordings (257 channels) was used to emulate local EEG sensor units with short inter-electrode distances. A computationally efficient sensor selection and interictal spike detection algorithm was developed and used to assess the influence of the inter-electrode distance and the number of such EEG units on spike detection performance. Signal-to-noise ratio, correlation with a clinical-grade IEDs detector and Cohen’s kappa coefficient of agreement were used to quantify performance. Bayesian statistics were used to confirm the statistical significance of the observed results. Main results. We found that EEG recording equipment should be specifically designed to measure the small signal power at short inter-electrode distance by providing an input referred noise 300 nV. We also found that an inter-electrode distance of minimum 5 cm between electrodes in a setup with a minimum of two EEG units is required to obtain near equivalent performance in interictal spike detection to standard EEG. Significance. These findings provide design guidelines for miniaturizing EEG systems for long term ambulatory monitoring of interictal spikes in epilepsy patients.

Fast Detection and Quantification of Interictal Spikes and Seizures in a Rodent Model of Epilepsy Using an Automated Algorithm.

The electroencephalogram (EEG) is a powerful tool for analyzing neural activity in various neurological disorders, both in animals and in humans. This technology has enabled researchers to record the brain's abrupt changes in electrical activity with high resolution, thus facilitating efforts to understand the brain's response to internal and external stimuli. The EEG signal acquired from implanted electrodes can be used to precisely study the spiking patterns that occur during abnormal neural discharges. These patterns can be analyzed in conjunction with behavioral observations and serve as an important means for accurate asses sment and quantification of behavioral and electrographic seizures. Numerous algorithms have been developed for the automated quantification of EEG data; however, many of these algorithms were developed with outdated programming languages and require robust computational hardware to run effectively. Additionally, some of these programs require substantial computation time, reducing the relative benefits of automation. Thus, we sought to develop an automated EEG algorithm that was programmed using a familiar programming language (MATLAB), and that could run efficiently without extensive computational demands. This algorithm was developed to quantify interictal spikes and seizures in mice that were subjected to traumatic brain injury. Although the algorithm was designed to be fully automated, it can be operated manually, and all the parameters for EEG activity detection can be easily modified for broad data analysis. Additionally, the algorithm is capable of processing months of lengthy EEG datasets in the order of minutes to hours, reducing both analysis time and errors introduced through manual-based processing.

Data-driven approach for the delineation of the irritative zone in epilepsy in MEG.

The reliable identification of the irritative zone (IZ) is a prerequisite for the correct clinical evaluation of medically refractory patients affected by epilepsy. Given the complexity of MEG data, visual analysis of epileptiform neurophysiological activity is highly time consuming and might leave clinically relevant information undetected. We recorded and analyzed the interictal activity from seven patients affected by epilepsy (Vectorview Neuromag), who successfully underwent epilepsy surgery (Engel > = II). We visually marked and localized characteristic epileptiform activity (VIS). We implemented a two-stage pipeline for the detection of interictal spikes and the delineation of the IZ. First, we detected candidate events from peaky ICA components, and then clustered events around spatio-temporal patterns identified by convolutional sparse coding. We used the average of clustered events to create IZ maps computed at the amplitude peak (PEAK), and at the 50% of the peak ascending slope (SLOPE). We validated our approach by computing the distance of the estimated IZ (VIS, SLOPE and PEAK) from the border of the surgically resected area (RA). We identified 25 spatiotemporal patterns mimicking the underlying interictal activity (3.6 clusters/patient). Each cluster was populated on average by 22.1 [15.0-31.0] spikes. The predicted IZ maps had an average distance from the resection margin of 8.4 ± 9.3 mm for visual analysis, 12.0 ± 16.5 mm for SLOPE and 22.7 ±. 16.4 mm for PEAK. The consideration of the source spread at the ascending slope provided an IZ closer to RA and resembled the analysis of an expert observer. We validated here the performance of a data-driven approach for the automated detection of interictal spikes and delineation of the IZ. This computational framework provides the basis for reproducible and bias-free analysis of MEG recordings in epilepsy.

Fast parametric curve matching (FPCM) for automatic spike detection

Objective. Epilepsy is a widely spread neurological disease, whose treatment often requires resection of the pathological cortical tissue. Interictal spike analysis observed in the non-invasively collected EEG or MEG data offers an attractive way to localize epileptogenic cortical structures for surgery planning purposes. Interictal spike detection in lengthy multichannel data is a daunting task that is still often performed manually. This frequently limits such an analysis to a small portion of the data which renders the appropriate risks of missing the potentially epileptogenic region. While a plethora of automatic spike detection techniques have been developed each with its own assumptions and limitations, none of them is ideal and the best results are achieved when the output of several automatic spike detectors are combined. This is especially true in the low signal-to-noise ratio conditions. To this end we propose a novel biomimetic approach for automatic spike detection based on a constrained mixed spline machinery that we dub as fast parametric curve matching (FPCM). Approach. Using the peak-wave shape parametrization, the constrained parametric morphological model is constructed and convolved with the observed multichannel data to efficiently determine mixed spline parameters corresponding to each time-point in the dataset. Then the logical predicates that directly map to verbalized text-book like descriptions of the expected interictal event morphology allow us to accomplish the spike detection task. Main results. The results of simulations mimicking typical low SNR scenarios show the robustness and high receiver operating characteristic AUC values of the FPCM method as compared to the spike detection performed using more conventional approaches such as wavelet decomposition, template matching or simple amplitude thresholding. Applied to the real MEG and EEG data from the human patients and to rat ECoG data, the FPCM technique demonstrates reliable detection of the interictal events and localization of epileptogenic zones concordant with independent conclusions made by the epileptologist. Significance. Since the FPCM is computationally light, tolerant to high amplitude artifacts and flexible to accommodate verbalized descriptions of an arbitrary target morphology, it is likely to complement the existing arsenal of means for analysis of noisy interictal datasets.

Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches.

Epileptic seizures occur due to brain abnormalities that can indirectly affect patient's health. It occurs abruptly without any symptoms and thus increases the mortality rate of humans. Almost 1% of world's population suffers from epileptic seizures. Prediction of seizures before the beginning of onset is beneficial for preventing seizures by medication. Nowadays, modern computational tools, machine learning, and deep learning methods have been used to predict seizures using EEG. However, EEG signals may get corrupted with background noise, and artifacts such as eye blinks and physical movements of muscles may lead to “pops” in the signal, resulting in electrical interference, which is cumbersome to detect through visual inspection for longer duration recordings. These limitations in automatic detection of interictal spikes and epileptic seizures are preferred, which is an essential tool for examining and scrutinizing the EEG recording more precisely. These restrictions bring our attention to present a review of automated schemes that will help neurologists categorize epileptic and nonepileptic signals. While preparing this review paper, it is observed that feature selection and classification are the main challenges in epilepsy prediction algorithms. This paper presents various techniques depending on various features and classifiers over the last few years. The methods presented will give a detailed understanding and ideas about seizure prediction and future research directions.

Multilevel Feature Learning Method for Accurate Interictal Epileptiform Spike Detection.

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 [Formula: see text], 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.

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.

QuPWM: Feature Extraction Method for Epileptic Spike Classification

Epilepsy is a neurological disorder ranked as the second most serious neurological disease known to humanity, after stroke. Inter-ictal spiking is an abnormal neuronal discharge after an epileptic seizure. This abnormal activity can originate from one or more cranial lobes, often travels from one lobe to another, and interferes with normal activity from the affected lobe. The common practice for Inter-ictal spike detection of brain signals is via visual scanning of the recordings, which is a subjective and a very time-consuming task. Motivated by that, this article focuses on using machine learning for epileptic spikes classification in magnetoencephalography (MEG) signals. First, we used the Position Weight Matrix (PWM) method combined with a uniform quantizer to generate useful features from time domain and frequency domain through a Fast Fourier Transform (FFT) of the framed raw MEG signals. Second, the extracted features are fed to standard classifiers for inter-ictel spikes classification. The proposed technique shows great potential in spike classification and reducing the feature vector size. Specifically, the proposed technique achieved average sensitivity up to 87% and specificity up to 97% using 5-folds cross-validation applied to a balanced dataset. These samples are extracted from nine epileptic subjects using a sliding frame of size 95 samples-points with a step-size of 8 sample-points.

Detection of Spikes With Defined Parameters in the ECoG Signal

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.

Automating Interictal Spike Detection: Revisiting A Simple Threshold Rule.

Interictal spikes (IIS) are bursts of neuronal depolarization observed electrographically between periods of seizure activity in epilepsy patients. However, IISs are difficult to characterize morphologically and their effects on neurophysiology and cognitive function are poorly understood. Currently, IIS detection requires laborious manual assessment and marking of electroencephalography (EEG/iEEG) data. This practice is also subjective as the clinician has to select the mental threshold that EEG activity must exceed in order to be considered a spike. The work presented here details the development and implementation of a simple automated IIS detection algorithm. This preliminary study utilized intracranial EEG recordings collected from 7 epilepsy patients, and IISs were marked by a single physician for a total of 1339 IISs across 68 active electrodes. The proposed algorithm implements a simple threshold rule that scans through iEEG data and identifies IISs using various normalization techniques that eliminate the need for a more complex detector. The efficacy of the algorithm was determined by evaluating the sensitivity and specificity of the detector across a range of thresholds, and an approximate optimal threshold was determined using these results. With an average true positive rate of over 98% and a false positive rate of below 2%, the accuracy of this algorithm speaks to its use as a reliable diagnostic tool to detect IISs, which has direct applications in localizing where seizures start, detecting when seizures start, and in understanding cognitive impairment due to IISs. Furthermore, due to its speed and simplicity, this algorithm can be used for real-time detection of IIS that will ultimately allow physicians to study their clinical implications with high temporal resolution and individual adaptation.

Modular framework for detection of inter-ictal spikes in iEEG.

In this paper, we present a new modular approach for detection of inter-ictal spikes in intracranial iEEG recordings from patients that are suffering from pharmaco-resistant form of epilepsy. This new approach is presented in the form of a detection framework consisting of three primary modules: first level detector, second level feature extractor, and third level detection classifier, where each module is responsible for a specific functionality. This detection framework can be perceived as a three slot system, where modules can be easily plugged in their slots and replaced by a different module or implementation on demand, in order to adapt the quality of detection (measured in terms of sensitivity, precision or inter-recording adaptability) and computational cost. Using complex real-world data sets it was confirmed that the proposed framework provides highly sensitive and precise detection, while it also significantly reduces the computation time.

Multichannel interictal spike activity detection using time–frequency entropy measure

Localization of interictal spikes is an important clinical step in the pre-surgical assessment of pharmacoresistant epileptic patients. The manual selection of interictal spike periods is cumbersome and involves a considerable amount of analysis workload for the physician. The primary focus of this paper is to automate the detection of interictal spikes for clinical applications in epilepsy localization. The epilepsy localization procedure involves detection of spikes in a multichannel EEG epoch. Therefore, a multichannel Time-Frequency (T-F) entropy measure is proposed to extract features related to the interictal spike activity. Least squares support vector machine is used to train the proposed feature to classify the EEG epochs as either normal or interictal spike period. The proposed T-F entropy measure, when validated with epilepsy dataset of 15 patients, shows an interictal spike classification accuracy of 91.20%, sensitivity of 100% and specificity of 84.23%. Moreover, the area under the curve of Receiver Operating Characteristics plot of 0.9339 shows the superior classification performance of the proposed T-F entropy measure. The results of this paper show a good spike detection accuracy without any prior information about the spike morphology.

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

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.

Localization of Interictal Epileptic Spikes With MEG: Optimization of an Automated Beamformer Screening Method (SAMepi) in a Diverse Epilepsy Population.

To describe and optimize an automated beamforming technique followed by identification of locations with excess kurtosis (g2) for efficient detection and localization of interictal spikes in patients with medically refractory epilepsy. Synthetic aperture magnetometry with g2 averaged over a sliding time window (SAMepi) was performed in seven patients with focal epilepsy and five healthy volunteers. The effect of varied window lengths on detection of spiking activity was evaluated. Sliding window lengths of 0.5 to 10 seconds performed similarly, with 0.5- and 1-second windows detecting spiking activity in 1 of the 3 virtual sensor locations with highest kurtosis. These locations were concordant with the region of eventual surgical resection in these seven patients who remained seizure-free at 1 year. Average g2 values increased with increasing sliding window length in all subjects. In healthy volunteers, kurtosis values stabilized in data sets longer than 2 minutes. SAMepi using g2 averaged over 1-second sliding time windows in data sets of at least 2 minutes of duration reliably identified interictal spiking and the presumed seizure focus in these seven patients. Screening the five locations with highest kurtosis values for spiking activity is an efficient and accurate technique for localizing interictal activity using magnetoencephalography. SAMepi should be applied using the parameter values and procedure described for optimal detection and localization of interictal spikes. Use of this screening procedure could significantly improve the efficiency of magnetoencephalography analysis if clinically validated.

Application of cross-correlated delay shift rule in spiking neural networks for interictal spike detection.

This study proposes a Cross-Correlated Delay Shift (CCDS) supervised learning rule to train neurons with associated spatiotemporal patterns to classify spike patterns. The objective of this study was to evaluate the feasibility of using the CCDS rule to automate the detection of interictal spikes in electroencephalogram (EEG) data on patients with epilepsy. Encoding is the initial yet essential step for spiking neurons to process EEG patterns. A new encoding method is utilized to convert the EEG signal into spike patterns. The simulation results show that the proposed algorithm identified 69 spikes out of 82 spikes, or 84% detection rate, which is quite high considering the subtleties of interictal spikes and the tediousness of monitoring long EEG records. This CCDS rule is also benchmarked by ReSuMe on the same task.

A novel genetic programming approach for epileptic seizure detection

The human brain is a delicate mix of neurons (brain cells), electrical impulses and chemicals, known as neurotransmitters. Any damage has the potential to disrupt the workings of the brain and cause seizures. These epileptic seizures are the manifestations of epilepsy. The electroencephalograph (EEG) signals register average neuronal activity from the cerebral cortex and label changes in activity over large areas. A detailed analysis of these electroencephalograph (EEG) signals provides valuable insights into the mechanisms instigating epileptic disorders. Moreover, the detection of interictal spikes and epileptic seizures in an EEG signal plays an important role in the diagnosis of epilepsy. Automatic seizure detection methods are required, as these epileptic seizures are volatile and unpredictable. This paper deals with an automated detection of epileptic seizures in EEG signals using empirical mode decomposition (EMD) for feature extraction and proposes a novel genetic programming (GP) approach for classifying the EEG signals. Improvements in the standard GP approach are made using a Constructive Genetic Programming (CGP) in which constructive crossover and constructive subtree mutation operators are introduced. A hill climbing search is integrated in crossover and mutation operators to remove the destructive nature of these operators. A new concept of selecting the Globally Prime offspring is also presented to select the best fitness offspring generated during crossover. To decrease the time complexity of GP, a new dynamic fitness value computation (DFVC) is employed to increase the computational speed. We conducted five different sets of experiments to evaluate the performance of the proposed model in the classification of different mixtures of normal, interictal and ictal signals, and the accuracies achieved are outstandingly high. The experimental results are compared with the existing methods on same datasets, and these results affirm the potential use of our method for accurately detecting epileptic seizures in an EEG signal.

Magnetic source imaging in posterior cortex epilepsies.

Posterior cortex epilepsies (PCE) are characterized by less satisfying postoperative results than temporal lobe epilepsies and are thus challenging for non-invasive presurgical investigations. The objective of this study was to evaluate the performance of magnetic source imaging (MSI) in PCE, validating the results by the SEEG (stereoelectroencephalography) definition of irritative and epileptogenic zones (IZ and EZ). Fourteen PCE surgery candidates were investigated using MSI and SEEG. LCMV (Linearly Constrained Minimum Variance) and MUSIC algorithms were used. IZ was quantified using a semi-automatic detection of interictal spikes. EZ was quantified using the epileptogenicity index (EI) method that accounts for both the propensity of a brain area to generate rapid discharges and the time for this area to get involved in the seizure. EI values range from 0 (no epileptogenicity) to 1 (maximal epileptogenicity). Levels of concordance between MSI and IZ, MSI and EZ were determined as follows: A=localized on MSI and SEEG for the site of value 1 (IZ and EZ quantification), B=localized on MSI and SEEG for a part of the IZ or a structure involved in the EZ (without the maximal value 1), C=localized on MSI and not SEEG, D=localized on SEEG and not MSI, E=localized on MSI and SEEG, discordant for site. Five PCE cases were characterized by focal IZ, nine by distributed IZ between several distant brain areas. MSI allowed to determinate IZ in 4/5 focal IZ cases. In case of distributed IZ, levels of concordance were A (2 cases), B (4 cases) and D (3 cases). In most distributed cases, MSI allowed to localize only a part of the IZ. Medial temporal involvement in the IZ was frequent (9/12 cases) and not evidenced by MSI. The brain area that demonstrated the maximal value of EI was shown by MSI in four out of five (80%) focal IZ cases, in two out of nine (22%) distributed cases. MSI results depend on IZ characteristics. A distributed IZ organization presents difficulties for MSI and highlights the need for further methodological approaches.

P395: Switching from bimanual to unimanual tapping reveals two distinct post-switching beta synchronization processes

P395: Switching from bimanual to unimanual tapping reveals two distinct post-switching beta synchronization processes

Automatic detection of prominent interictal spikes in intracranial EEG: Validation of an algorithm and relationsip to the seizure onset zone

ObjectiveTo develop an algorithm for the automatic quantitative description and detection of spikes in the intracranial EEG and quantify the relationship between prominent spikes and the seizure onset zone. MethodsAn algorithm was developed for the quantification of time–frequency properties of spikes (upslope, instantaneous energy, downslope) and their statistical representation in a univariate generalized extreme value distribution. Its performance was evaluated in comparison to expert detection of spikes in intracranial EEG recordings from 10 patients. It was subsequently used in 18 patients to detect prominent spikes and quantify their spatial relationship to the seizure onset area. ResultsThe algorithm displayed an average sensitivity of 63.4% with a false detection rate of 3.2 per minute for the detection of individual spikes and an average sensitivity of 88.6% with a false detection rate of 1.4% for the detection of intracranial EEG contacts containing the most prominent spikes. Prominent spikes occurred closer to the seizure onset area than less prominent spikes but they overlapped with it only in a minority of cases (3/18). ConclusionsAutomatic detection and quantification of the morphology of spikes increases their utility to localize the seizure onset area. Prominent spikes tend to originate mostly from contacts located in the close vicinity of the seizure onset area rather than from within it. SignificanceQuantitative analysis of time–frequency characteristics and spatial distribution of intracranial spikes provides complementary information that may be useful for the localization of the seizure-onset zone.