Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions

Seizure Detection, Seizure Prediction, and Closed-Loop Warning Systems in Epilepsy

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy

Controversies in epilepsy: Debates held during the Fourth International Workshop on Seizure Prediction

Debates on six controversial topics were held during the Fourth International Workshop on Seizure Prediction (IWSP4) convened in Kansas City, KS, USA, July 4–7, 2009. The topics were (1) Ictogenesis: Focus versus Network? (2) Spikes and Seizures: Step-relatives or Siblings? (3) Ictogenesis: A Result of Hyposynchrony? (4) Can Focal Seizures Be Caused by Excessive Inhibition? (5) Do High-Frequency Oscillations Provide Relevant Independent Information? (6) Phase Synchronization: Is It Worthwhile as Measured? This article, written by the IWSP4 organizing committee and the debaters, summarizes the arguments presented during the debates

EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population

Background: Epilepsy is one of the most common neurological conditions globally, and the fourth most common in the United States. Recurrent non-provoked seizures characterize it and have huge impacts on the quality of life and financial impacts for affected individuals. A rapid and accurate diagnosis is essential in order to instigate and monitor optimal treatments. There is also a compelling need for the accurate interpretation of epilepsy due to the current scarcity in neurologist diagnosticians and a global inequity in access and outcomes. Furthermore, the existing clinical and traditional machine learning diagnostic methods exhibit limitations, warranting the need to create an automated system using deep learning model for epilepsy detection and monitoring using a huge database. Method: The EEG signals from 35 channels were used to train the deep learning-based transformer model named (EpilepsyNet). For each training iteration, 1-min-long data were randomly sampled from each participant. Thereafter, each 5-s epoch was mapped to a matrix using the Pearson Correlation Coefficient (PCC), such that the bottom part of the triangle was discarded and only the upper triangle of the matrix was vectorized as input data. PCC is a reliable method used to measure the statistical relationship between two variables. Based on the 5 s of data, single embedding was performed thereafter to generate a 1-dimensional array of signals. In the final stage, a positional encoding with learnable parameters was added to each correlation coefficient’s embedding before being fed to the developed EpilepsyNet as input data to epilepsy EEG signals. The ten-fold cross-validation technique was used to generate the model. Results: Our transformer-based model (EpilepsyNet) yielded high classification accuracy, sensitivity, specificity and positive predictive values of 85%, 82%, 87%, and 82%, respectively. Conclusion: The proposed method is both accurate and robust since ten-fold cross-validation was employed to evaluate the performance of the model. Compared to the deep models used in existing studies for epilepsy diagnosis, our proposed method is simple and less computationally intensive. This is the earliest study to have uniquely employed the positional encoding with learnable parameters to each correlation coefficient’s embedding together with the deep transformer model, using a huge database of 121 participants for epilepsy detection. With the training and validation of the model using a larger dataset, the same study approach can be extended for the detection of other neurological conditions, with a transformative impact on neurological diagnostics worldwide

The Role of Europe in World-Wide Science and Technology: Monitoring and Evaluation in a Context of Global Competition

Noyons ECM, Buter RK, van Raan AFJ, Schwechheimer H, Winterhager M, Weingart P. The Role of Europe in World-Wide Science and Technology: Monitoring and Evaluation in a Context of Global Competition. Leiden: Universiteit Leiden; 2000

Spike pattern recognition by supervised classification in low dimensional embedding space

© The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.Peer reviewedFinal Published versio

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Frontal lobe epilepsy, sleep and parasomnias.

A close relationship exists between sleep and epilepsy. While many forms of epilepsy may be influenced by the sleep-wake cycle, this phenomenon is particularly evident in frontal lobe epilepsy where affected individuals may experience seizures exclusively during sleep (nocturnal frontal lobe epilepsy, NFLE). In this thesis, three aspects of the relationship between sleep and frontal lobe epilepsy are examined. Firstly, serotonergic neurotransmission across the human sleep-wake cycle was studied using the novel PET ligand l8F-MPPF, a serotonergic 5HT)A receptor radioligand sensitive to endogenous serotonin release. Fourteen individuals with narcolepsy underwent 18F-MPPF PET scans during sleep and wakefulness. The study demonstrated a 13% increase in 18F-MPPF binding potential (p<0.01) during sleep, indicating a reduction in serotoninergic neurotransmission, in line with existing animal data. Secondly, the characterisation of benign, non-epileptic parasomnias and their distinction from nocturnal frontal lobe seizures was addressed in two studies. The first comprised an analysis of the historical features of these conditions, and included the development and validation of a clinical scale for the diagnosis of nocturnal events. The second comprised a detailed semiological analysis of a series of parasomnias recorded on video-EEG monitoring, and a statistical comparison with seizures in NFLE. Although similarities between NFLE and parasomnias were observed, the results provide an evidence base for the confident distinction of these disorders. Finally, the familial form of NFLE (autosomal dominant nocturnal frontal lobe epilepsy, ADNFLE) is associated with mutations in genes for nicotinic acetylcholine receptor subunits, but recognised mutations account for only a minority of reported cases. The last study presented here is a clinical and genetic analysis of two large families with an unusually severe ADNFLE phenotype. Affected individuals had refractory epilepsy and increased rates of mental retardation and psychiatric disorders and, in one family, linkage studies suggest a previously unrecognised underlying mechanism

Unravelling Molecular Genetic Causes and Disease Mechanisms in Landau Kleffner Syndrome

The cost of epilepsy to an individual lies not just in the burden of having recurrent seizures but also in the condition’s neurodevelopmental, cognitive, psychological and social co-morbidities. Presently, our understanding of the pathophysiological mechanisms underlying epilepsy and its neurocognitive co-morbidities remains severely limited, translating to our current lack of targeted treatment options. This PhD study aims to better understand the pathophysiological mechanisms underlying epilepsy and its neurocognitive co-morbidities through the clinical and molecular genetic study of a cohort of patients with Landau Kleffner syndrome (LKS), an epilepsy syndrome characterised by seizures, and neurodevelopmental regression in the form of loss of speech and language skills. Patients were recruited from a database of children referred for LKS at Great Ormond Street Hospital’s Developmental Epilepsy Clinic. Clinical data was extracted through case note review. As mutations in GRIN2A, a gene encoding the N2A subunit of the Nmethyl-D-Aspartate (NMDA) receptor have previously been described in 8-20% of individuals with LKS and related disorders, recruited individuals were screened for GRIN2A mutations via Sanger Sequencing and multiplex-ligation probe amplification. Functional investigations exploring gene/protein expression, protein localisation and channel function were carried out on missense GRIN2A mutations identified. Individuals who screened negative for GRIN2A variants underwent whole exome sequencing or whole genome sequencing to identify novel genes associated with LKS. This study has drawn conclusions that LKS is a neurodevelopmental disorder and clinical features influencing prognosis include age at onset of regression, non-verbal intelligence, and the presence of motor difficulties. GRIN2A mutations are likely to lead to LKS through overall NMDA receptor loss of function effects. Nonetheless, LKS may be a complex disorder with multi-factorial or oligogenic aetiology. Lastly, the long term potentiation pathway, important for learning and memory mechanisms, features strongly in the pathogenesis of LKS