SynchroLINNce: Toolbox for Neural Synchronization and Desynchronization Assessment in Epilepsy Animal Models

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

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.

Towards fast and reliable simultaneous EEG-fMRI analysis of epilepsy with automatic spike detection

Performance of automatic spike-detection algorithms and interrater reliability of human EEG reviewers were investigated previously by using scalp EEG recordings. However, it is not known, whether the findings of these studies hold for intracranial recordings. To address this question, we analyzed spike detection in intracranial recordings by two human reviewers and three automatic systems covering major lines in the development of automatic spike-detection systems. Intracranial recordings from subdural and intrahippocampal depth electrode contacts in seven patients were analyzed by two reviewers and three spike-detection systems: (a) The rule-based system by Gotman, (b) a two-stage system consisting of a linear predictor and a second rule-based stage, and (c) a system using wavelet coefficients of the intracranial EEG data. Agreement between the two human reviewers with respect to spike identification was <50%. The automatic systems achieved agreements of 24% (Gotman), 26% (wavelet detector), and 32% (two-stage system) with the individual human reviewers. In spite of the small proportion of agreements, the same anatomic regions were identified by human and automatic EEG analysis as generators for the majority of spikes. The poor agreement between the human EEG reviewers suggests that the definition of spikes and spike-like episodes in intracranial electrodes is far from unequivocal. Nevertheless, localizing information is highly consistent by either visual or automatic spike detection, independent of the algorithm used for automatic spike detection.

Automatic detection of epileptiform spikes in the electrocorticogram: a comparison of two algorithms

Fast parametric curve matching (FPCM) for automatic spike detection

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

Recognition of spike foci has proved to be a difficult field for EEG automation, and although many methods have been described, progress has been slow. In this chapter, the authors present a review of the major methods used for spike detection. It is only valuable if the threshold values are set for each patient on the basis of previous recordings, thus limiting its use to seizure monitoring. Lieb, Woods, Siccardi, Crandall, Walter, and Leake (1978) used an automatic spike-detection system (COUNTR) based on the second derivative technique. Lopes da Silva et al. and Lopes da Silva, Van Hulten, Lom-men, Storm Van Leeuwen, Van Vellen, and Viegenthart introduced another interesting approach for the detection of epileptic spikes with an automatic spike-detection program. Combinations of several parameters, such as amplitude, duration, and sharpness, maintain the same level of accurate detection, but decrease the number of false positives.

People with epilepsy (PWE) are more likely to develop depression and both these complex chronic diseases greatly affect health-related quality of life (QOL). This comorbidity contributes to the deterioration of the QOL further than increasing the severity of epilepsy worsening prognosis. Strong scientific evidence suggests the presence of shared pathogenic mechanisms. The correct identification and management of these factors are crucial in order to improve patients’ QOL. This review article discusses recent original research on the most common pathogenic mechanisms of depression in PWE and highlights the effects of antidepressant drugs (ADs) against seizures in PWE and animal models of seizures and epilepsy. Newer ADs, such as selective serotonin reuptake inhibitors (SRRI) or serotonin-noradrenaline reuptake inhibitors (SNRI), particularly sertraline, citalopram, mirtazapine, reboxetine, paroxetine, fluoxetine, escitalopram, fluvoxamine, venlafaxine, duloxetine may lead to improvements in epilepsy severity whereas the use of older tricyclic antidepressant (TCAs) can increase the occurrence of seizures. Most of the data demonstrate the acute effects of ADs in animal models of epilepsy while there is a limited number of studies about the chronic antidepressant effects in epilepsy and epileptogenesis or on clinical efficacy. Much longer treatments are needed in order to validate the effectiveness of these new alternatives in the treatment and the development of epilepsy, while further clinical studies with appropriate protocols are warranted in order to understand the real potential contribution of these drugs in the management of PWE (besides their effects on mood).

Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes

Epilepsy is one of the most common neurological disorders that greatly disturb patients' daily lives. Automatic spike detection of EEG of epileptic patients have a great influence on assisting doctors to quickly and easily diagnose whether the patient is epileptic. Nowadays, there are several existing methods and software that can recognize seizure-related EEG signals and epileptic spikes to help doctors to release their burdensome work, but hardly can we find the research of accuracy of software for automatic epileptic spikes detection. Therefore, we propose to study a comparison on the number of omission, false positives, accuracy and other norms between automatic detection and highly trained clinicians detection. From our comparative analysis, we conclude that automatic epileptic spike detection software often make an inaccurate detection and then we analyze the reasons. As a result, our research gives basis to optimize clinical diagnosis and automatic spike detection methods

Automatic EEG spike detection provide valuable information for diagnosis of epilepsy. In the past 30 years, a number of algorithms were proposed. However, the basic idea of most algorithms is to identify spike activities. We have developed an algorithm based on identify non-spike activities for off-line analysis. In this paper, we improved the algorithm for online application using real-time template. Template method is a typical spike detection method. It was not widely employed because of the difficulty of making template. Our results suggested that multi-channel template is meaningful and effective for real-time detection. Spike activities can be detected with a delay of about 2 s. We also designed an simple user-friendly monitor interface for monitor and review spike events in real-time.

Identification of spikes in the electroencephalogram (EEG) plays an important role during the diagnosis of neurological disorders such as epilepsy. Automatic spike detection (ASD) is attractive because it reduces the diagnostic time and improves objectivity of the scoring. Unfortunately, automatic detection is sometimes confounded by artifacts, particularly motion artifacts which can be frequent in ambulatory recording, in the ICU, when recording from restless patients or children, etc. EEG systems have recently been improved by using active electrodes and driven-right-leg circuits (DRL) to reduce motion artifacts. However, the performances of ASD algorithms, both with unimproved and improved EEG systems, are difficult to quantify in patients because of poor reproducibility of the results. In this paper, a test set-up was used to evaluate the performance of active electrodes and DRL and assess if they can be complemented or substituted by a spike detection algorithm in avoiding motion artifact. Results show that motion artifacts can largely degrade spike detection when a traditional EEG system is used, whereas an EEG fitted with active electrodes and a DRL allows high quality detection. When using a traditional EEG, the choice of a spike detection algorithm has a large influence on detection quality.

Automatic spike detection via an artificial neural network using raw EEG data: effects of data preparation and implications in the limitations of online recognition

The recording of seizures and spikes is of primary importance in the evaluation of epileptic patients. This is not always an easy process because these events can be rare and are usually unpredictable. Since the earliest days of computer analysis of the EEG, researchers have developed methods for the automatic detection of spikes and, more recently, of seizures. The problems are complex because spikes and seizures are not clearly defined and have extremely varied morphologies. Nevertheless, it has been possible to develop automatic detection methods that can be of great assistance during long-term monitoring of epileptic patients. No method is absolutely fail-safe and all require human validation, but they save a considerable amount of time in the interpretation of long recordings. Recent developments include detection of the patterns specific to newborns, and the possibility of warning a patient or observer that a seizure is starting.