•Epilepsy surgery is a safe and effective form of treatment to improve neuropsychological outcomes in patients with drug resistant EE/DEE-SWAS.•Hemispherectomies and focal resections are similarly ...effective in achieving good seizure control and SWAS resolution.•Surgery can achieve immediate termination of seizures and SWAS pattern on EEG.•Patients undergoing focal resections showed the best neuropsychological outcome.•Clinicians should consider early surgical intervention for patients with drug resistant EE/DEE-SWAS.
Epileptic Encephalopathy / Developmental Epileptic Encephalopathy with spike-and-wave activation during sleep (EE/DEE-SWAS) is a self-limiting childhood epilepsy syndrome but may cause permanent neurocognitive impairment. Surgical interventions have been controversial in the treatment of EE/DEE-SWAS. This systematic review aims to evaluate the efficacy of various surgical procedures on the outcomes of EE/DEE-SWAS.
A systematic review was performed per the PRISMA guidelines. A total of 14 retrospective studies were identified, comprising 131 cases of EE/DEE-SWAS treated with epilepsy surgery. The review analyzed presurgical data, surgical interventions, as well as outcomes related to seizures, EEG, and neuropsychological assessments.
Epilepsy surgery was successfully performed in 131 cases with minor complications. The average age was 2.6 years at seizure onset and 5.0 years at diagnosis of SWAS. Excellent seizure control (Engel I and II) was achieved in 80.6 %, 78.6 %, 77.4 % and 27.2 % of patients receiving hemispherectomies, focal resections, multiple subpial transections (MSTs), and corpus callosotomies (CCTs), respectively. EEG SWAS resolution was seen in 79.7 % of hemispherectomy cases, 78.6 % in focal resections, 63.9 % in MSTs, and 8.3 % in CCTs. Neurocognitive and behavioral improvement was noted in 58.0 %, 71.4 %, 58.3 % and 16.7 % for patients receiving hemispherectomies, focal resections, MSTs, and CCTs, respectively. A correlation between improved seizure control and SWAS resolution was observed with improved neuropsychological outcomes.
Epilepsy surgery is a safe and effective treatment for carefully selected children with drug-resistant EE/DEE-SWAS. Patients who underwent epilepsy surgery had reduction of seizure burden, SWAS resolution and improvements in neurocognitive and behavioral function.
Abstract
Epilepsy is a chronic brain condition characterized by an uncontrollable electric blast in the brain, manifested as Epilepsy seizures. More than one percent of the world population is ...affected by Epilepsy seizures. This framework employs a few corporal boundaries, such as temperature, pulse, and movement limits. The device measures the electrical activity of the brain by using an EEG sensor and alerts the patient according to the electrical brain activity. This paper focuses on the development of early detection of seizures by EEG to alert the patient to changes in his/her body.
Objective. Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even ...commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals and in understanding the information it may contain for brain functionality. The robust automatic classification of these signals is an important step towards making the use of EEG more practical in many applications and less reliant on trained professionals. Towards this goal, a systematic review of the literature on deep learning applications to EEG classification was performed to address the following critical questions: (1) Which EEG classification tasks have been explored with deep learning? (2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks? Approach. A systematic literature review of EEG classification using deep learning was performed on Web of Science and PubMed databases, resulting in 90 identified studies. Those studies were analyzed based on type of task, EEG preprocessing methods, input type, and deep learning architecture. Main results. For EEG classification tasks, convolutional neural networks, recurrent neural networks, deep belief networks outperform stacked auto-encoders and multi-layer perceptron neural networks in classification accuracy. The tasks that used deep learning fell into five general groups: emotion recognition, motor imagery, mental workload, seizure detection, event related potential detection, and sleep scoring. For each type of task, we describe the specific input formulation, major characteristics, and end classifier recommendations found through this review. Significance. This review summarizes the current practices and performance outcomes in the use of deep learning for EEG classification. Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.
The scalp electroencephalogram (EEG), a non-invasive measure of brain's electrical activity, is commonly used ancillary test to aide in the diagnosis of epilepsy. Usually, neurologists employ direct ...visual inspection to identify epileptiform abnormalities. Therefore, electroencephalograms have been an essential integral to the researches which aim to automatically detect epilepsy. However, it is difficult because seizure manifestations on scalp EEG are extremely variable between patients, even the same patient. In addition, scalp EEG is usually composed of large number of noise signals which might cover the real features of seizure. To this challenge, we construct an 18-layer Long-Term recurrent convolutional network (LRCN) to automatic epileptogenic zone recognition and localization on scalp EEG. As far as we know, we are the first to train a deep learning classifier to identify seizures through the EEG images, just like neurologists direct visual inspection to identify epileptiform abnormalities. Furthermore, unlike the traditionally methods extracted features from channels manually, which neglected the association of brain's epileptiform abnormalities electrical transmission, seizures is considered as a continuous brain's abnormal electrical activity in our algorithm, from produce at one or several channels, transmission between channels, to flat again after seizures. The method was evaluated in 23 patients with a total of 198 seizures. The classifier shows reasonably good results, with 84% for sensitivity, 99% for specificity, and 99% for accuracy. False Positive Rate per hours exceeds significantly previous results obtained on cross-patient classifiers, with 0.2/h.
Sleep deprivation and poor sleep quality are significant societal challenges that negatively impact individuals' health. The interaction between subjective sleep quality, objective sleep measures, ...physical and cognitive performance, and their day-to-day variations remains poorly understood. Our year-long study of 20 healthy individuals, using subcutaneous electroencephalography, aimed to elucidate these interactions, assessing data stability and participant satisfaction, usability, well-being and adherence. In the study, 25 participants were fitted with a minimally invasive subcutaneous electroencephalography lead, with 20 completing the year of subcutaneous electroencephalography recording. Signal stability was measured using covariance of variation. Participant satisfaction, usability and well-being were measured with questionnaires: Perceived Ease of Use questionnaire, System Usability Scale, Headache questionnaire, Major Depression Inventory, World Health Organization 5-item Well-Being Index, and interviews. The subcutaneous electroencephalography signals remained stable for the entire year, with an average participant adherence rate of 91%. Participants rated their satisfaction with the subcutaneous electroencephalography device as easy to use with minimal or no discomfort. The System Usability Scale score was high at 86.3 ± 10.1, and interviews highlighted that participants understood how to use the subcutaneous electroencephalography device and described a period of acclimatization to sleeping with the device. This study provides compelling evidence for the feasibility of longitudinal sleep monitoring during everyday life utilizing subcutaneous electroencephalography in healthy subjects, showcasing excellent signal stability, adherence and user experience. The amassed subcutaneous electroencephalography data constitutes the largest dataset of its kind, and is poised to significantly advance our understanding of day-to-day variations in normal sleep and provide key insights into subjective and objective sleep quality.
Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly ...interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. Objective. In this work, we review 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations. Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to (1) the data, (2) the preprocessing methodology, (3) the DL design choices, (4) the results, and (5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. Results. Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours, while the number of samples seen during training by a network varies from a few dozens to several millions, depending on how epochs are extracted. Interestingly, we saw that more than half the studies used publicly available data and that there has also been a clear shift from intra-subject to inter-subject approaches over the last few years. About of the studies used convolutional neural networks (CNNs), while used recurrent neural networks (RNNs), most often with a total of 3-10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies and emphasize the need for more reproducible research. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly. A planned follow-up to this work will be an online public benchmarking portal listing reproducible results.
Epilepsy syndromes have been recognized for >50 years, as distinct electroclinical phenotypes with therapeutic and prognostic implications. Nonetheless, no formally accepted International League ...Against Epilepsy (ILAE) classification of epilepsy syndromes has existed. The ILAE Task Force on Nosology and Definitions was established to reach consensus regarding which entities fulfilled criteria for an epilepsy syndrome and to provide definitions for each syndrome. We defined an epilepsy syndrome as “a characteristic cluster of clinical and electroencephalographic features, often supported by specific etiological findings (structural, genetic, metabolic, immune, and infectious).” The diagnosis of a syndrome in an individual with epilepsy frequently carries prognostic and treatment implications. Syndromes often have age‐dependent presentations and a range of specific comorbidities. This paper describes the guiding principles and process for syndrome identification in both children and adults, and the template of clinical data included for each syndrome. We divided syndromes into typical age at onset, and further characterized them based on seizure and epilepsy types and association with developmental and/or epileptic encephalopathy or progressive neurological deterioration. Definitions for each specific syndrome are contained within the corresponding position papers.
The intelligent recognition of electroencephalogram (EEG) signals has become an important approach to the detection of epilepsy. Among existing intelligent identification methods, fuzzy logic systems ...(FLSs) have shown a distinctive advantage in identifying epileptic EEG signals because of their strong learning abilities and interpretability. Like many conventional intelligent methods for recognizing EEG signals, in the training of FLS, it is assumed that the training dataset and test dataset are drawn from data that are identically distributed. However, this assumption is not necessarily valid in practice as it is not uncommon for the two datasets to have different distributions. To overcome this problem, a strategy is presented in this paper to construct a Takagi-Sugeno-Kang (TSK) FLS based on transductive transfer learning for identifying epileptic EEG signals. Two novel objective functions, achieved by integrating the transductive transfer learning mechanism, are proposed for the training of the TSK FLS. As regression and binary classification are two common approaches to multiclass classification, the TSK transfer learning FLS algorithms for regression and binary classification are developed, respectively, to construct the corresponding TSK FLS. Both algorithms are further used to perform a multiclass classification to recognize epileptic EEG signals. Their performance in the epileptic EEG datasets indicates promise in dealing with situations where the training and test datasets differ with regard to data distribution.
Epilepsy the disorder of the central nervous system has its worldwide presence in roughly 50 million people as estimated by the World Health Organization. Electroencephalogram (EEG) is one of the ...most common and non-invasive ways of analyzing and studying the subtle changes in neuronal activity of the brain during an epileptic seizure attack. These changes can be analyzed for developing an automated system that would assert the chances of an impending seizure. As changeable nature of seizure affects the patients from having a normal life, hence progress in developing new methods will improve the quality of life and also provide assistance in the medical sector. Objective of the proposed method is to avoid EEG channel selection and use all input EEG channel features to design a generalized epileptic seizure detection framework.
In this work, a long short-term memory network has been proposed that is not complex and has the capability of effectively detecting epileptic seizures from both non-invasive and invasive electroencephalogram recordings. The proposed framework is simple and effective and designed in such capacity that raw electroencephalogram signals can be used to detect seizures. Also, a generalized approach has been followed that is channel independent such that EEG signals belonging to any hemisphere of the brain can be detected effectively by the proposed architecture.
The automated seizure detection system achieved high seizure detection sensitivity of 99.9%, and a low false-positive rate of 0.003 per hour for the Children's Hospital Boston-Massachusetts Institute of Technology dataset. While for the Sleep-Wake-Epilepsy-Center of the University Department of Neurology at the Inselspital Bern dataset, the sensitivity is 99.4% and false-positive rate of 0.006 per hour. Convergence analysis of the proposed model provides a significant amount of reliability and correctness in the efficient detection of epileptic seizures.
Assessment of the proposed framework on non-invasive as well as invasive EEG signals showed that the framework worked well for different type of EEG recordings as different metrics gave satisfactory results. As the framework is simple and did not require any additional parameter optimization techniques, it reduced the processing overheads without affecting the accuracy. Hence, it can be used as an efficient method for monitoring epileptic seizures.
•A novel framework for analyzing brain activity during epileptic seizure is proposed.•Better results have been achieved as compared to recent studies with respect to sensitivity, accuracy and false positive rate.•No additional requirement of parameter optimization or extraction of features.•Analysis of EEG signal sequence by LSTM network helped in detecting subtle changes that occur during seizure attacks.•Seizures are detected with 99.9% sensitivity and false positive rate of 0.003/h.
Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of intuitive upper extremity imagery for ...multi-directional arm reaching tasks in three-dimensional (3D) environments. We designed and implemented an experimental environment in which electroencephalogram (EEG) signals can be acquired for movement execution and imagery. Fifteen subjects participated in our experiments. We proposed a multi-directional convolution neural network-bidirectional long short-term memory network (MDCBN)-based deep learning framework. The decoding performances for six directions in 3D space were measured by the correlation coefficient (CC) and the normalized root mean square error (NRMSE) between predicted and baseline velocity profiles. The grand-averaged CCs of multi-direction were 0.47 and 0.45 for the execution and imagery sessions, respectively, across all subjects. The NRMSE values were below 0.2 for both sessions. Furthermore, in this study, the proposed MDCBN was evaluated by two online experiments for real-time robotic arm control, and the grand-averaged success rates were approximately 0.60 (±0.14) and 0.43 (±0.09), respectively. Hence, we demonstrate the feasibility of intuitive robotic arm control based on EEG signals for real-world environments.
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