Nonconvulsive Epileptic Seizure Detection in Scalp EEG Using Multiway Data Analysis

Nonconvulsive status epilepticus is a condition where the patient is exposed to abnormally prolonged epileptic seizures without evident physical symptoms. Since these continuous seizures may cause permanent brain damage, it constitutes a medical emergency. This paper proposes a method to detect nonconvulsive seizures for a further nonconvulsive status epilepticus diagnosis. To differentiate between the normal and seizure electroencephalogram (EEG), a K-Nearest Neighbor, a Radial Basis Support Vector Machine, and a Linear Discriminant Analysis classifier are used. The classifier features are obtained from the Canonical Polyadic Decomposition (CPD) and Block Term Decomposition (BTD) of the EEG data represented as third order tensor. To expand the EEG into a tensor, Wavelet or Hilbert-Huang transform are used. The algorithm is tested on a scalp EEG database of 139 seizures of different duration. The experimental results suggest that a Hilbert-Huang tensor representation and the CPD analysis provide the most suitable framework for nonconvulsive seizure detection. The Radial Basis Support Vector Machine classifier shows the best performance with sensitivity, specificity, and accuracy values over 98%. A rough comparison with other methods proposed in the literature shows the superior performance of the proposed method for nonconvulsive epileptic seizure detection

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data privacy-focused method that trains a global model by aggregating local models from hospitals. Existing FL techniques adopt a central server-based network topology, where the server assembles the local models trained in each hospital to create a global model. However, the server could be a point of failure, and models trained in FL usually have worse performance than those trained in the centralized learning manner when the patient's data are not independent and identically distributed (Non-IID) in the hospitals. This paper presents a decentralized FL framework, including training with adaptive ensemble learning and a deployment phase using knowledge distillation. The adaptive ensemble learning step in the training phase leads to the acquisition of a specific model for each hospital that is the optimal combination of local models and models from other available hospitals. This step solves the non-IID challenges in each hospital. The deployment phase adjusts the model's complexity to meet the resource constraints of wearable systems. We evaluated the performance of our approach on edge computing platforms using EPILEPSIAE and TUSZ databases, which are public epilepsy datasets.RYC2021-032853-

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

Higher-order tensor decomposition based scalp-to-intracranial EEG projection for detection of interictal epileptiform discharges

Objective. Interictal epileptiform discharges (IEDs) occur between two seizures onsets. IEDs are mainly captured by intracranial recordings and are often invisible over the scalp. This study proposes a model based on tensor factorization to map the time-frequency (TF) features of scalp EEG (sEEG) to the TF features of intracranial EEG (iEEG) in order to detect IEDs from over the scalp with high sensitivity. Approach. Continuous wavelet transform is employed to extract the TF features. Time, frequency, and channel modes of IED segments from iEEG recordings are concatenated into a four-way tensor. Tucker and CANDECOMP/PARAFAC decomposition techniques are employed to decompose the tensor into temporal, spectral, spatial, and segmental factors. Finally, TF features of both IED and non-IED segments from scalp recordings are projected onto the temporal components for classification. Main results. The model performance is obtained in two different approaches: within- and between-subject classification approaches. Our proposed method is compared with four other methods, namely a tensor-based spatial component analysis method, TF-based method, linear regression mapping model, and asymmetric-symmetric autoencoder mapping model followed by convolutional neural networks. Our proposed method outperforms all these methods in both within- and between-subject classification approaches by respectively achieving 84.2% and 72.6% accuracy values. Significance. The findings show that mapping sEEG to iEEG improves the performance of the scalp-based IED detection model. Furthermore, the tensor-based mapping model outperforms the autoencoder- and regression-based mapping models

Frontal lobe epilepsy, sleep and parasomnias.

A close relationship exists between sleep and epilepsy. While many forms of epilepsy may be influenced by the sleep-wake cycle, this phenomenon is particularly evident in frontal lobe epilepsy where affected individuals may experience seizures exclusively during sleep (nocturnal frontal lobe epilepsy, NFLE). In this thesis, three aspects of the relationship between sleep and frontal lobe epilepsy are examined. Firstly, serotonergic neurotransmission across the human sleep-wake cycle was studied using the novel PET ligand l8F-MPPF, a serotonergic 5HT)A receptor radioligand sensitive to endogenous serotonin release. Fourteen individuals with narcolepsy underwent 18F-MPPF PET scans during sleep and wakefulness. The study demonstrated a 13% increase in 18F-MPPF binding potential (p<0.01) during sleep, indicating a reduction in serotoninergic neurotransmission, in line with existing animal data. Secondly, the characterisation of benign, non-epileptic parasomnias and their distinction from nocturnal frontal lobe seizures was addressed in two studies. The first comprised an analysis of the historical features of these conditions, and included the development and validation of a clinical scale for the diagnosis of nocturnal events. The second comprised a detailed semiological analysis of a series of parasomnias recorded on video-EEG monitoring, and a statistical comparison with seizures in NFLE. Although similarities between NFLE and parasomnias were observed, the results provide an evidence base for the confident distinction of these disorders. Finally, the familial form of NFLE (autosomal dominant nocturnal frontal lobe epilepsy, ADNFLE) is associated with mutations in genes for nicotinic acetylcholine receptor subunits, but recognised mutations account for only a minority of reported cases. The last study presented here is a clinical and genetic analysis of two large families with an unusually severe ADNFLE phenotype. Affected individuals had refractory epilepsy and increased rates of mental retardation and psychiatric disorders and, in one family, linkage studies suggest a previously unrecognised underlying mechanism

Identification of brain epileptiform discharges from electroencephalograms

Brain interictal epileptiform discharges (IEDs), as the fundamental indicators of seizure, are transient events occurring between two or before seizure onsets, captured using electroencephalogram (EEG). For epilepsy diagnosis and localization of seizure sources, both interictal and ictal recordings are extremely informative. Accurate detection of IEDs from over the scalp helps faster diagnosis of epilepsy. The scalp EEG (sEEG) suffers from a low signal-to-noise ratio and high attenuation of IEDs due to the high skull electrical impedance. On the other hand, the intracranial EEG (iEEG) recorded using implanted electrodes enjoys high temporal-spatial resolution and enables capturing most IEDs. Therefore, in this thesis, the focus is on the identification of IEDs from the concurrent scalp and intracranial EEGs. Multi-way analysis provides an opportunity to jointly analyse the data in different domains. IEDs may share some features within and between the segments. We have developed methods based on multi-way analysis and tensor factorization to detect the IEDs from the concurrent sEEG in both segmented and real-time approaches. The diversities in IED morphology, strength, and source location within the brain cause a great deal of uncertainty in their labeling by clinicians. We have exploited and incorporated this uncertainty (the probability of the waveform being an IED) in an IED detection system. Furthermore, IEDs are naturally sparse. We have benefited from the sparsity of IED waveforms in developing an algorithm to exploit sparse common features among the IED segments, referred to as sparse common feature analysis. By mapping sEEG to iEEG, the sEEG quality is improved. In this thesis, the proposed tensor factorization maps the time-frequency features of sEEG to those of iEEG to detect the IEDs from over the scalp with high sensitivity. We have concatenated time, frequency, and channel modes of iEEG recordings into a tensor. After decomposing the tensor into temporal, spectral, and spatial components, the EEG time-frequency features have been extracted and projected onto the temporal components. Furthermore, we have developed two novel algorithms based on generative adversarial networks to map the raw sEEG to iEEG. As a result of this work, the visibility of IEDs from sEEG has over 4-fold improvement. Additionally, the outcome paves the path for future research in epilepsy prediction, seizure source localisation, and modeling the brain seizure pathways