Model-based robust suppression of epileptic seizures without sensory measurements

The co-activation patterns of multiple brain regions in Juvenile Myoclonic Epilepsy

Juvenile myoclonic epilepsy (JME) as an idiopathic generalized epilepsy has been studied by many advanced neuroimaging techniques to elucidate its neuroanatomical basis and pathophysiological mechanisms. In this paper, we used co-activation patterns (CAPs) to explore the differences of dynamic brain activity changes in resting state between JME patients and healthy controls. 27 cases JME patients and 27 cases healthy of fMRI data were collected. The structural image data of the subjects were analyzed by voxel-based morphological analysis, and the regions with gray matter volume atrophy and high voxel were selected as the regions of interest. Further, the mean disease duration was used as boundary to divide the patients' data into the below-average time and the above-average time groups, which were defined as patient disease duration groups. And these data were used to construct CAPs and to compare changes in brain dynamics. It was found that the number of patterns occurrences and the possibility of switching between patterns were smaller than those in the healthy control, which indicated patients with damage to brain regions. For the patient time control group, the number of patterns occurrences and the possibility of switching between patterns were similar, while there was linear regression between the three values and disease duration. Collectively, this study provides important evidence for revealing the key brain regions of JME by studying the transformation between CAPs. Future studies could investigate the effects of receiving treatment on patient dynamic brain activity.

Classification of the Epileptic Seizure Onset Zone Based on Partial Annotation

Epilepsy is a chronic disorder caused by excessive electrical discharges. Currently, clinical experts identify the seizure onset zone (SOZ) channel through visual judgment based on long-time intracranial electroencephalogram (iEEG), which is a very time-consuming, difficult and experience-based task. Therefore, there is a need for high-accuracy diagnostic aids to reduce the workload of clinical experts. In this article, we propose a method in which, the iEEG is split into the 20-s segment and for each patient, we ask clinical experts to label a part of the data, which is used to train a model and classify the remaining iEEG data. In recent years, machine learning methods have been successfully applied to solve some medical problems. Filtering, entropy and short-time Fourier transform (STFT) are used for extracting features. We compare them to wavelet transform (WT), empirical mode decomposition (EMD) and other traditional methods with the aim of obtaining the best possible discriminating features. Finally, we look for their medical interpretation, which is important for clinical experts. We achieve high-performance results for SOZ and non-SOZ data classification by using the labeled iEEG data and support vector machine (SVM), fully connected neural network (FCNN) and convolutional neural network (CNN) as classification models. In addition, we introduce the positive unlabeled (PU) learning to further reduce the workload of clinical experts. By using PU learning, we can learn a binary classifier with a small amount of labeled data and a large amount of unlabeled data. This can greatly reduce the amount and difficulty of annotation work by clinical experts. All together, we show that using 105 minutes of labeled data we achieve a classification result of 91.46% on average for multiple patients.

Closed-loop seizure modulation via extreme learning machine based extended state observer

Neuromodulation is a promising way in clinical treatment of epilepsy, but the existing methods cannot adjust stimulations according to patients' real-time reactions. Therefore, it is necessary to acquire a systematic and a scientific regulation method based on patients' real-time reactions. The linear active disturbance rejection control can adapt to complex epileptic dynamics and improve the epilepsy regulation, even if little model information is available, and various uncertainties and external disturbances exist. However, a linear extended state observer estimates the time-varying total disturbance with a steady-state error. To improve regulation, it is crucial to estimate the total disturbance in a more accurate manner. An extreme learning machine is capable of approximating any nonlinear function. Its initial parameter generation is more convenient, adjustable parameters are fewer, and learning speed is faster. Thus, a nonlinear time-varying function can be estimated more timely and accurately. Then, an extreme learning machine based extended state observer is proposed to get a more satisfactory total disturbance estimation and more desired closed-loop regulation. The convergence of the extreme learning machine based extended state observer is verified and the stability of the closed-loop system is analyzed. Numerical results show that the proposed extended state observer is much better than a linear extended state observer in estimating the total disturbance. It guarantees a more satisfied closed-loop neuromodulation.

Generalizable epileptic seizures prediction based on deep transfer learning

Predicting seizures before they happen can help prevent them through medication. In this research, first, a total of 22 features were extracted from 5-s segmented EEG signals. Second, tensors were developed as inputs for different deep transfer learning models to find the best model for predicting epileptic seizures. The effect of Pre-ictal state duration was also investigated by selecting four different intervals of 10, 20, 30, and 40 min. Then, nine models were created by combining three ImageNet convolutional networks with three classifiers and were examined for predicting seizures patient-dependently. The Xception convolutional network with a Fully Connected (FC) classifier achieved an average sensitivity of 98.47% and a False Prediction Rate (FPR) of 0.031 h-1 in a 40-min Pre-ictal state for ten patients from the European database. The most promising result of this study was the patient-independent prediction of epileptic seizures; the MobileNet-V2 model with an FC classifier was trained with one patient's data and tested on six other patients, achieving a sensitivity rate of 98.39% and an FPR of 0.029 h-1 for a 40-min Pre-ictal scheme.

Deep-layer motif method for estimating information flow between EEG signals

Accurate identification for the information flow between epileptic seizure signals is the key to construct the directional epileptic brain network which can be used to localize epileptic focus. In this paper, our concern is on how to improve the direction identification of information flow and also investigate how it can be cut off or weakened. In view of this, we propose the deep-layer motif method. Based on the directional index (DI) estimation using permutation conditional mutual information, the effectiveness of the proposed deep-layer motif method is numerically assessed with the coupled mass neural model. Furthermore, we investigate the robustness of this method in considering the interference of autaptic coupling, time delay and short-term plasticity. Results show that compared to the simple 1-layer motif method, the 2nd- and 3rd-layer motif methods have the dominant enhancement effects for the direction identification. In particular, deep-layer motif method possesses good anti-jamming performance and good robustness in calculating DI. In addition, we investigate the effect of deep brain stimulation (DBS) on the information flow. It is found that this deep-layer motif method is still superior to the single-layer motif method in direction identification and is robust to weak DBS. However, the high-frequency strong DBS can effectively decrease the DI suggesting the weakened information flow. These results may give new insights into the seizure detection and control.

Epileptic seizures in a heterogeneous excitatory network with short-term plasticity

Epilepsy involves a diverse group of abnormalities, including molecular and cellular disorders. These abnormalities prove to be associated with the changes in local excitability and synaptic dynamics. Correspondingly, the epileptic processes including onset, propagation and generalized seizure may be related with the alterations of excitability and synapse. In this paper, three regions, epileptogenic zone (EZ), propagation area and normal region, were defined and represented by neuronal population model with heterogeneous excitability, respectively. In order to describe the synaptic behavior that the strength was enhanced and maintained at a high level for a short term under a high frequency spike train, a novel activity-dependent short-term plasticity model was proposed. Bifurcation analysis showed that the presence of hyperexcitability could increase the seizure susceptibility of local area, leading to epileptic discharges first seen in the EZ. Meanwhile, recurrent epileptic activities might result in the transition of synaptic strength from weak state to high level, augmenting synaptic depolarizations in non-epileptic neurons as the experimental findings. Numerical simulation based on a full-connected weighted network could qualitatively demonstrate the epileptic process that the propagation area and normal region were successively recruited by the EZ. Furthermore, cross recurrence plot was used to explore the synchronization between neuronal populations, and the global synchronization index was introduced to measure the global synchronization. Results suggested that the synchronization between the EZ and other region was significantly enhanced with the occurrence of seizure. Interestingly, the desynchronization phenomenon was also observed during seizure initiation and propagation as reported before. Therefore, heterogeneous excitability and short-term plasticity are believed to play an important role in the epileptic process. This study may provide novel insights into the mechanism of epileptogenesis.