Detection of interictal discharges with convolutional neural networks using discrete ordered multichannel intracranial EEG

Detection algorithms for electroencephalography (EEG) data, especially in the field of interictal epileptiform discharge (IED) detection, have traditionally employed handcrafted features which utilised specific characteristics of neural responses. Although these algorithms achieve high accuracy, mere detection of an IED holds little clinical significance. In this work, we consider deep learning for epileptic subjects to accommodate automatic feature generation from intracranial EEG data, while also providing clinical insight. Convolutional neural networks are trained in a subject independent fashion to demonstrate how meaningful features are automatically learned in a hierarchical process. We illustrate how the convolved filters in the deepest layers provide insight towards the different types of IEDs within the group, as confirmed by our expert clinicians. The morphology of the IEDs found in filters can help evaluate the treatment of a patient. To improve the learning of the deep model, moderately different score classes are utilised as opposed to binary IED and non-IED labels. The resulting model achieves state of the art classification performance and is also invariant to time differences between the IEDs. This study suggests that deep learning is suitable for automatic feature generation from intracranial EEG data, while also providing insight into the dat

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DLbased CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper

A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG Signals

Early detection and proper treatment of epilepsy is essential and meaningful to those who suffer from this disease. The adoption of deep learning (DL) techniques for automated epileptic seizure detection using electroencephalography (EEG) signals has shown great potential in making the most appropriate and fast medical decisions. However, DL algorithms have high computational complexity and suffer low accuracy with imbalanced medical data in multi seizure-classification task. Motivated from the aforementioned challenges, we present a simple and effective hybrid DL approach for epileptic seizure detection in EEG signals. Specifically, first we use a K-means Synthetic minority oversampling technique (SMOTE) to balance the sampling data. Second, we integrate a 1D Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network based on Truncated Backpropagation Through Time (TBPTT) to efficiently extract spatial and temporal sequence information while reducing computational complexity. Finally, the proposed DL architecture uses softmax and sigmoid classifiers at the classification layer to perform multi and binary seizure-classification tasks. In addition, the 10-fold cross-validation technique is performed to show the significance of the proposed DL approach. Experimental results using the publicly available UCI epileptic seizure recognition data set shows better performance in terms of precision, sensitivity, specificity, and F1-score over some baseline DL algorithms and recent state-of-the-art techniques

Automated Detection of High-Frequency Oscillations in Epilepsy Based on a Convolutional Neural Network

Epilepsy is one of the most common chronic neurological diseases. High-frequency oscillations (HFOs) have emerged as promising biomarkers for the epileptogenic zone. However, visual marking of HFOs is a time-consuming and laborious process. Several automated techniques have been proposed to detect HFOs, yet these are still far from being suitable for application in a clinical setting. Here, ripples and fast ripples from intracranial electroencephalograms were detected in six patients with intractable epilepsy using a convolutional neural network (CNN) method. This approach proved more accurate than using four other HFO detectors integrated in RIPPLELAB, providing a higher sensitivity (77.04% for ripples and 83.23% for fast ripples) and specificity (72.27% for ripples and 79.36% for fast ripples) for HFO detection. Furthermore, for one patient, the Cohen's kappa coefficients comparing automated detection and visual analysis results were 0.541 for ripples and 0.777 for fast ripples. Hence, our automated detector was capable of reliable estimates of ripples and fast ripples with higher sensitivity and specificity than four other HFO detectors. Our detector may be used to assist clinicians in locating epileptogenic zone in the future

Leveraging Artificial Intelligence to Improve EEG-fNIRS Data Analysis

La spectroscopie proche infrarouge fonctionnelle (fNIRS) est apparue comme une technique de neuroimagerie qui permet une surveillance non invasive et à long terme de l'hémodynamique corticale. Les technologies de neuroimagerie multimodale en milieu clinique permettent d'étudier les maladies neurologiques aiguës et chroniques. Dans ce travail, nous nous concentrons sur l'épilepsie - un trouble chronique du système nerveux central affectant près de 50 millions de personnes dans le monde entier prédisposant les individus affectés à des crises récurrentes. Les crises sont des aberrations transitoires de l'activité électrique du cerveau qui conduisent à des symptômes physiques perturbateurs tels que des changements aigus ou chroniques des compétences cognitives, des hallucinations sensorielles ou des convulsions de tout le corps. Environ un tiers des patients épileptiques sont récalcitrants au traitement pharmacologique et ces crises intraitables présentent un risque grave de blessure et diminuent la qualité de vie globale. Dans ce travail, nous étudions 1. l'utilité des informations hémodynamiques dérivées des signaux fNIRS dans une tâche de détection des crises et les avantages qu'elles procurent dans un environnement multimodal par rapport aux signaux électroencéphalographiques (EEG) seuls, et 2. la capacité des signaux neuronaux, dérivé de l'EEG, pour prédire l'hémodynamique dans le cerveau afin de mieux comprendre le cerveau épileptique. Sur la base de données rétrospectives EEG-fNIRS recueillies auprès de 40 patients épileptiques et utilisant de nouveaux modèles d'apprentissage en profondeur, la première étude de cette thèse suggère que les signaux fNIRS offrent une sensibilité et une spécificité accrues pour la détection des crises par rapport à l'EEG seul. La validation du modèle a été effectuée à l'aide de l'ensemble de données CHBMIT open source documenté et bien référencé avant d'utiliser notre ensemble de données EEG-fNIRS multimodal interne. Les résultats de cette étude ont démontré que fNIRS améliore la détection des crises par rapport à l'EEG seul et ont motivé les expériences ultérieures qui ont déterminé la capacité prédictive d'un modèle d'apprentissage approfondi développé en interne pour décoder les signaux d'état de repos hémodynamique à partir du spectre complet et d'une bande de fréquences neuronale codée spécifique signaux d'état de repos (signaux sans crise). Ces résultats suggèrent qu'un autoencodeur multimodal peut apprendre des relations multimodales pour prédire les signaux d'état de repos. Les résultats suggèrent en outre que des gammes de fréquences EEG plus élevées prédisent l'hémodynamique avec une erreur de reconstruction plus faible par rapport aux gammes de fréquences EEG plus basses. De plus, les connexions fonctionnelles montrent des modèles spatiaux similaires entre l'état de repos expérimental et les prédictions fNIRS du modèle. Cela démontre pour la première fois que l'auto-encodage intermodal à partir de signaux neuronaux peut prédire l'hémodynamique cérébrale dans une certaine mesure. Les résultats de cette thèse avancent le potentiel de l'utilisation d'EEG-fNIRS pour des tâches cliniques pratiques (détection des crises, prédiction hémodynamique) ainsi que l'examen des relations fondamentales présentes dans le cerveau à l'aide de modèles d'apprentissage profond. S'il y a une augmentation du nombre d'ensembles de données disponibles à l'avenir, ces modèles pourraient être en mesure de généraliser les prédictions qui pourraient éventuellement conduire à la technologie EEG-fNIRS à être utilisée régulièrement comme un outil clinique viable dans une grande variété de troubles neuropathologiques.----------ABSTRACT Functional near-infrared spectroscopy (fNIRS) has emerged as a neuroimaging technique that allows for non-invasive and long-term monitoring of cortical hemodynamics. Multimodal neuroimaging technologies in clinical settings allow for the investigation of acute and chronic neurological diseases. In this work, we focus on epilepsy—a chronic disorder of the central nervous system affecting almost 50 million people world-wide predisposing affected individuals to recurrent seizures. Seizures are transient aberrations in the brain's electrical activity that lead to disruptive physical symptoms such as acute or chronic changes in cognitive skills, sensory hallucinations, or whole-body convulsions. Approximately a third of epileptic patients are recalcitrant to pharmacological treatment and these intractable seizures pose a serious risk for injury and decrease overall quality of life. In this work, we study 1) the utility of hemodynamic information derived from fNIRS signals in a seizure detection task and the benefit they provide in a multimodal setting as compared to electroencephalographic (EEG) signals alone, and 2) the ability of neural signals, derived from EEG, to predict hemodynamics in the brain in an effort to better understand the epileptic brain. Based on retrospective EEG-fNIRS data collected from 40 epileptic patients and utilizing novel deep learning models, the first study in this thesis suggests that fNIRS signals offer increased sensitivity and specificity metrics for seizure detection when compared to EEG alone. Model validation was performed using the documented open source and well referenced CHBMIT dataset before using our in-house multimodal EEG-fNIRS dataset. The results from this study demonstrated that fNIRS improves seizure detection as compared to EEG alone and motivated the subsequent experiments which determined the predictive capacity of an in-house developed deep learning model to decode hemodynamic resting state signals from full spectrum and specific frequency band encoded neural resting state signals (seizure free signals). These results suggest that a multimodal autoencoder can learn multimodal relations to predict resting state signals. Findings further suggested that higher EEG frequency ranges predict hemodynamics with lower reconstruction error in comparison to lower EEG frequency ranges. Furthermore, functional connections show similar spatial patterns between experimental resting state and model fNIRS predictions. This demonstrates for the first time that intermodal autoencoding from neural signals can predict cerebral hemodynamics to a certain extent. The results of this thesis advance the potential of using EEG-fNIRS for practical clinical tasks (seizure detection, hemodynamic prediction) as well as examining fundamental relationships present in the brain using deep learning models. If there is an increase in the number of datasets available in the future, these models may be able to generalize predictions which would possibly lead to EEG-fNIRS technology to be routinely used as a viable clinical tool in a wide variety of neuropathological disorders

NOVEL GRAPHICAL MODEL AND NEURAL NETWORK FRAMEWORKS FOR AUTOMATED SEIZURE DETECTION, TRACKING, AND LOCALIZATION IN FOCAL EPILEPSY

Epilepsy is a heterogenous neurological disorder characterized by recurring and unprovoked seizures. It is estimated that 60% of epilepsy patients suffer from focal epilepsy, where seizures originate from one or more discrete locations within the brain. After onset, focal seizure activity spreads, involving more regions in the cortex. Diagnosis and therapeutic planning for patients with focal epilepsy crucially depends on being able to detect epileptic activity as it starts and localize its origin. Due to the subtlety of seizure activity and the complex spatio-temporal propagation patterns of seizure activity, detection and localization of seizure by visual inspection is time-consuming and must be done by highly trained neurologists. In this thesis, we detail modeling approaches to identify and capture the spatio-temporal ictal propagation of focal epileptic seizures. Through novel multi-scale frameworks, information fusion between signal paths, and hybrid architectures, models that capture the underlying seizure propagation phenomena are developed. The first half relies on graphical modeling approaches to detect seizures and track their activity through the space of EEG electrodes. A coupled hidden Markov model approach to seizure propagation is described. This model is subsequently improved through the addition of convolutional neural network based likelihood functions, removing the reliance on hand designed feature extraction. Through the inclusion of a hierarchical switching chain and localization variables, the model is revised to capture multi-scale seizure onset and spreading information. In the second half of this thesis, end-to-end neural network architectures for seizure detection and localization are developed. First, combination convolutional and recurrent neural networks are used to identify seizure activity at the level of individual EEG channels. Through novel aggregation, the network is trained to recognize seizure activity, track its evolution, and coarsely localize seizure onset from lower resolution labels. Next, a multi-scale network capable of analyzing the global and electrode level signals is developed for challenging task of end-to-end seizure localization. Onset location maps are defined for each patient and an ensemble of weakly supervised loss functions are used in a multi-task learning framework to train the architecture

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

The epileptogenic focus is a brain area that may be surgically removed to control of epileptic seizures. Locating it is an essential and crucial step prior to the surgical treatment. However, given the difficulty of determining the localization of this brain region responsible of the initial seizure discharge, many works have proposed machine learning methods for the automatic classification of focal and non-focal electroencephalographic (EEG) signals. These works use automatic classification as an analysis tool for helping neurosurgeons to identify focal areas off-line, out of surgery, during the processing of the huge amount of information collected during several days of patient monitoring. In turn, this paper proposes an automatic classification procedure capable of assisting neurosurgeons online, during the resective epilepsy surgery, to refine the localization of the epileptogenic area to be resected, if they have doubts. This goal requires a real-time implementation with as low a computational cost as possible. For that reason, this work proposes both a feature set and a classifier model that minimizes the computational load while preserving the classification accuracy at 95.5%, a level similar to previous works. In addition, the classification procedure has been implemented on a FPGA device to determine its resource needs and throughput. Thus, it can be concluded that such a device can embed the whole classification process, from accepting raw signals to the delivery of the classification results in a cost-effective Xilinx Spartan-6 FPGA device. This real-time implementation begins providing results after a 5 s latency, and later, can deliver floating-point classification results at 3.5 Hz rate, using overlapped time-windows

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions