EEG Synchronization Analysis for Seizure Prediction: A Study on Data of Noninvasive Recordings

Abstract

Objective: Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting ~65 million individuals worldwide. Prediction methods, typically based on machine learning methods, require a large amount of data for training, in order to correctly classify seizures with small false alarm rates. Methods: In this work, we present a new database containing EEG scalp signals of 14 epileptic patients acquired at the Unit of Neurology and Neurophysiology of the University of Siena, Italy. Furthermore, a patient-specific seizure prediction method, based on the detection of synchronization patterns in the EEG, is proposed and tested on the data of the database. The use of noninvasive EEG data aims to explore the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. The prediction method employs synchronization measures computed over all channel pairs and a computationally inexpensive threshold-based classification approach. Results and conclusions: The experimental analysis, performed by inspection and by the proposed threshold-based classifier on all the patients of the database, shows that the features extracted by the synchronization measures are able to detect preictal and ictal states and allow the prediction of the seizures few minutes before the seizure onsets.

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

“…There are also plenty of publicly available databases for assessment of neurological status in sleep [ 267 ], or in a specific condition, such as anesthesia [ 268 ]. Special attention is also paid to the research of epilepsy, therefore there are also specific databases containing EEG signals during epileptic seizures in both adults [ 269 , 270 ] or pediatric patients [ 271 ].…”

Generative adversarial networks in time series: A survey and taxonomy

“…SynSigGAN is designed to generate different kinds of continuous physiological/biomedical signal data [57]. It is capable of generating electrocardiogram (ECG), electroencephalogram (EEG), electromyography (EMG), and photoplethysmography (PPG) from MIT-BIH Arrhythmia database [58], Siena Scalp EEG database [59] and BIDMC PPG and Respiration dataset [60]. A novel GAN architecture is proposed here that uses a bidirectional grid long short term memory (BiGridLSTM) for the generator (Figure 12) and a CNN for the discriminator.…”

Dynamic Connectivity Analysis Using Adaptive Window Size

“…Recently, various monitoring methods have been used to detect the dynamics of brain networks [ 1 , 2 ]. Theoretical considerations and empirical observations of humans, macaques, and rats using various recording methods, such as fMRI [ 3 , 4 , 5 , 6 , 7 ], blood-oxygenation-level-dependent functional magnetic resonance imaging ( BOLD-fMRI ) [ 8 , 9 ], MEG [ 10 ], and EEG [ 11 , 12 , 13 ], have been established and suggest that connectivity is time-dependent, dynamic, and is associated with rhythmic activity [ 11 , 14 , 15 , 16 ].…”

“…The most commonly used method to study the dynamics of brain networks is sliding window analysis [ 11 , 12 , 13 , 28 ]. In this method, the index of functional connectivity is calculated in a window with predefined number of samples ( N ), and then the window is moved to the next set of samples with or without overlap.…”