arXiv research

Binary and multiclass epilepsy detection methods using EEG features.

Novel hybrid bilinear model improves epilepsy diagnosis accuracy.

Machine learning detects epilepsy development from EEG before seizures.

Epilepsy is the fourth most common neurological disorder, affecting about 1% of the population at all ages. As many as 60% of people with epilepsy experience focal seizures which originate in a certain brain area and are limited to part of one cerebral hemisphere. In focal epilepsy patients, a precise surgical removal …

Neural memory networks improve seizure type classification.

Bidirectional LSTM predicts seizures with 84% accuracy.

Model predicts epileptic seizures by detecting preictal state using wavelet transform and PCA.

Deep learning models predict epileptic seizures with high accuracy.

Paper proposes a k-NN classifier for detecting spike-and-wave seizures in EEG.

Novel method detects spike-and-wave patterns in EEG signals.

Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient's uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary…

New method detects spike-and-wave epileptiform discharges using Kendall's Tau-b.

Efficient method classifies locally stationary time series based on second-order characteristics.

Path signatures help predict seizures from brain activity.

Hippocampal dentate granule cells are among the few neuronal cell types generated throughout adult life in mammals. In the normal brain, new granule cells are generated from progenitors in the subgranular zone and integrate in a typical fashion. During the development of epilepsy, granule cell integration is profoundly…

In this paper, a genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to …

Novel NDL framework improves spike detection accuracy and channel localization in EEG/MEG data.

Model predicts epileptic seizures with high accuracy using EEG signals.

Study proposes a new early-warning framework for high-dimensional complex systems.

Identifying epileptic seizures through analysis of the electroencephalography (EEG) signal becomes a standard method for the diagnosis of epilepsy. Manual seizure identification on EEG by trained neurologists is time-consuming, labor-intensive and error-prone, and a reliable automatic seizure/non-seizure classification…

Resource-efficient oblique trees reduce neural signal classification costs.

Developing a Brain-Computer Interface~(BCI) for seizure prediction can help epileptic patients have a better quality of life. However, there are many difficulties and challenges in developing such a system as a real-life support for patients. Because of the nonstationary nature of EEG signals, normal and seizure patter…

Hybrid pipeline detects spike-and-wave discharges in long-term EEG recordings.

Deep learning improves automated detection of epileptic seizures.

Study of recurrences in earthquakes, climate, financial time-series, etc. is crucial to better forecast disasters and limit their consequences. However, almost all the previous phenomenological studies involved only a long-ranged autocorrelation function, or disregarded the multi-scaling properties induced by potential…

New model detects gradual changes in processes more accurately.

Electroencephalography (EEG) is an extensively-used and well-studied technique in the field of medical diagnostics and treatment for brain disorders, including epilepsy, migraines, and tumors. The analysis and interpretation of EEGs require physicians to have specialized training, which is not common even among most do…

Driven by the multi-level structure of human intracranial electroencephalogram (iEEG) recordings of epileptic seizures, we introduce a new variant of a hierarchical Dirichlet Process---the multi-level clustering hierarchical Dirichlet Process (MLC-HDP)---that simultaneously clusters datasets on multiple levels. Our sei…

Preventing early progression of epilepsy and so the severity of seizures requires an effective diagnosis. Epileptic transients indicate the ability to develop seizures but humans overlook such brief events in an electroencephalogram (EEG) what compromises patient treatment. Traditionally, training of the EEG event dete…

DPI quantifies phase differences in 1D and multidimensional signals using Riesz transform.

Develops a method to estimate network difference in high-dimensional time series data.

Proposes a method for valid inference in GPLSIMs with longitudinal data.

Model detects epileptic seizures in EEG with high sensitivity.

Recent literature on unsupervised learning focused on designing structural priors with the aim of learning meaningful features, but without considering the description length of the representations. In this thesis, first we introduce the $\varphi$ metric that evaluates unsupervised models based on their reconstruction …

Method detects critical events in complex systems by learning latent causal structure.

Implantable, closed-loop devices for automated early detection and stimulation of epileptic seizures are promising treatment options for patients with severe epilepsy that cannot be treated with traditional means. Most approaches for early seizure detection in the literature are, however, not optimized for implementati…

Previous literature on unsupervised learning focused on designing structural priors with the aim of learning meaningful features. However, this was done without considering the description length of the learned representations which is a direct and unbiased measure of the model complexity. In this paper, first we intro…

Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of events---something not previously studied quantitatively---could yield important insights into the nature and intrinsic dynamics of seizures. A…

Detects changes in brain signal topology to predict epileptic seizures.

Epilepsy is a neurological disorder classified as the second most serious neurological disease known to humanity, after stroke. Localization of the epileptogenic zone is an important step for epileptic patient treatment, which starts with epileptic spike detection. The common practice for spike detection of brain signa…

Scoping review finds EEG key in MCI research, identifying ERP/EEG, QEEG, and machine learning.

Novel approach constructs differential causal networks from EEG data.

Decision forests (Forests), in particular random forests and gradient boosting trees, have demonstrated state-of-the-art accuracy compared to other methods in many supervised learning scenarios. In particular, Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the s…

Bayesian method improves deep learning for noisy EEG seizure detection.

Electroencephalography (EEG) is a complex signal and can require several years of training 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 …

Dual-stage sEMG classification improves gesture recognition accuracy.

A novel method for classification with rejection using ensemble of cost-sensitive classifiers.

The number of possible methods of generalizing binary classification to multi-class classification increases exponentially with the number of class labels. Often, the best method of doing so will be highly problem dependent. Here we present classification software in which the partitioning of multi-class classification…