NEED: Neuromed Epilepsy EEG Database. A Large EEG Database of Epilepsy Patients for Research Community.

Overview

For one-third of patients with drug-resistant epilepsy alternative approaches must be investigated in order to improve the quality of their life. A possible approach is to find automatic methods to detect/predict seizures, in order to adopt interventional actions to stop or abort the seizure or to limit its side effect. The main problem in this case is to evaluate the reproducibility of such methods and to standardize them, because there is a lack of availability of long-term electroencephalography (EEG) data. In this study we want to create a large long-term EEG database, called NEED (Neuromed Epilepsy EEG Database), whos aim is to give researchers a way to test their method in a large collection of data. The database will contain long-term EEG recordings of 200 patients as well as extensive metadata and standardized annotation of the data sets and will be made freely available for the download to the research community.

About 50 million people worldwide are affected by epilepsy, which is one of the most frequent neurological diseases. It is characterized by unpredictable and sudden seizures which could led to loss of consciousness and uncontrolled motor reactions, severely impacting the quality of life of epilepsy patients. Only two-thirds of epilepsy patients can control seizures with anti-epileptic drugs or through epilepsy surgery. For the remaining patients, other therapeutics approaches should be considered. These approaches include the development of interventional closed-loop device, able to detect seizures and trigger an interventional operation, such as administer anti-epileptic drugs or electrically stimulate the epileptogenic focus to abort the seizure. In order to provide efficient therapeutic approaches to these patients, in the last decades many efforts have been made to develop automatic methods to find reliable markers in electroencephalographic (EEG) signal, which is the gold standard for epilepsy diagnosis, able to predict or early detect seizures in EEG. Such methods are based on mathematical or computational approaches for EEG signal analysis, whose aim is to extract complex measures (the so-called "features") from EEG signal, which are not recognizable with classical visual inspection of EEG signals made by epileptologists during the diagnosis of epilepsy, in order to use such features as precursors of incoming seizures (seizure prediction) or as indicators of an ongoing seizure (seizure detection). Many methods have been proposed in the last years, using linear or non-linear, to extract features from EEG signal. Recently, some studies used also electrocardiographic signal (ECG), which is normally recorded together with EEG in epilepsy monitoring, in order to extract promising features from it. Although these studies showed promising results, nevertheless they suffer of many limitations. Among them, the use of a limited number of patients and seizures and the use of only recordings belonging to the phase preceding the seizures (the so - called pre-ictal period). Such limitations do not allow to determine, for example, the specificity of such algorithms, because they don't consider also recordings acquired in time intervals far from the seizures (inter-ictal data) and could led to a overfitting of the seizure prediction/detection model. The only thing on which all the researchers on epilepsy agree is the existence of a pre-ictal phase, which is the phase preceding the seizure, the ictal phase, where the seizure is "active" and inter-ictal phase, which is a period temporarily far from the seizure. Normally, automatic methods for seizure prediction/detection consist of three different phases: the pre-processing (artifact removal, band-pass filtering, data segmentation, …) of EEG signals, feature extraction and feature selection and classification. This last step usually consists in the use of machine-learning and statistical methods in order to make decision about the prediction/detection. Basically, these models should be able to classify each EEG instance in two classes, "seizure" or "no seizure", using the features extracted from EEG signal. The efficacy of these models highly depends on how efficiently they are trained and normally the more are the data used to trained them, the more they are able to take the right decision. Therefore, the availability of large database of data could allow to develop efficient models for epilepsy detection/prediction. The possibility for researchers to have access to large database of continuous and long-term EEG data of epilepsy patients could be a big opportunity to develop efficient and reliable automatic seizure prediction/detection methods. For these reasons, in the last years some research groups have proposed and shared public EEG database with researchers who wants to test their automated seizure detection/prediction models. In particular, such database have been proposed by Epilepsy Center of University of Bonn and Freiburg and also by Children's Hospital of Boston and have been made available for free downloading to researchers. These databases contain long-term EEG recordings acquired during pre-surgical monitor of epilepsy patients. The number of patients included in these databases is quite low (from a minimum of 5 to a maximum of 23 patients) and also the number of seizures is limited (from a minimum of 59 to a maximum of 189). Furthermore, the duration of the recordings ranges from 40 minutes to 142 hours and the number of metadata (other information about patients and seizures) is very low. The last database has been proposed in 2008 in the framework of a EU-founded project (EPILEPSIAE), in which 6 different partners (hospitals, universities, companies) of 4 different countries (Germany, Italy, France, Portugal) have been involved. This database is not free but is made available for the download upon payment in 2012. Nowadays, it is the largest epilepsy EEG database available worldwide (http://epilepsy-database.eu/). It contains data from 275 patients, including EEG and ECG recordings, metadata, clinical and technical annotations on the data and clinical information about the patients. Actually, only 60 out of 275 datasets are available for the download. The aim of this study is to create a long-term EEG database acquired on epilepsy patients during the non-invasive presurgical monitor at Epilepsy Surgery Unit at IRCCS Neuromed. The database will include, besides EEG and ECG recordings, clinical and technical annotations on the data made by expert epileptologists and also clinical information about the patient, including neuropsychological evaluations. All the data will be first anonymized, crypted and then made available for the free download. The database will include data from 200 epilepsy patients underwent non-invasive presurgical epilepsy monitoring at Epilepsy Surgery Unit at IRCCS Neuromed. At the end of non-invasive EEG monitoring, two expert epileptologists will inspect EEG/ECG data in order to identify the seizures, in particular the channels where the seizure starts and the time, and everything could be of interest for the project. All the recordings will be exported in ASCII (American Standard Code for Information Interchange) format using the DMS Data Management System (Nihon Kohden Europe Gmbh) software, version 2.9.8 and stored locally. At the same time, clinical and demographic (only gender and age) data will be acquired for each patient. In particular, for each patient the following data will be available: * Demographic data (gender and age); * Clinical information (epilepsy type, seizure frequency,…); * Neuropsychological data; * EEG data acquired according to international 10-20 system; * ECG data; * Information about the recordings and the seizures (start and end time, start and end time of each seizure, …) All data from all the patients will be included in a single database, and each patient will be stored in a single compressed archive. The database will be made available after the completation of a request which can be forwarded by each researcher using a dedicated URL, in which the researcher will fill in a form when it will be asked to provide the following data: * Information about the applicant (name, address, affiliation, …) * GDPR consent Once the request will be received, the compressed archive containing the whole database will be protected with an ad-hoc alphanumeric password. Such password will consist of two parts: the first part will be sent by email to the applicant, the second part will be sent using the regular mail service (two-way authentication).