Borbála Hunyadi

Incorporating structural information from the multichannel EEG improves patient-specific seizure detection.

OBJECTIVE A novel patient-specific seizure detection algorithm is presented. As the spatial distribution of the ictal pattern is characteristic for a patients seizures, this work incorporates such information into the data representation and provides a learning algorithm exploiting it. METHODS The proposed training algorithm uses nuclear norm regularization to convey structural information of the channel-feature matrices extracted from the EEG. This method is compared to two existing approaches utilizing the same feature set, but integrating the multichannel information in a different manner. The performances of the detectors are demonstrated on a publicly available dataset containing 131 seizures recorded in 892 h of scalp EEG from 22 pediatric patients. RESULTS The proposed algorithm performed significantly better compared to the reference approaches (p=0.0170 and p=0.0002). It reaches a median performance of 100% sensitivity, 0.11h(-1) false detection rate and 7.8s alarm delay, outperforming a method in the literature using the same dataset. CONCLUSION The strength of our method lies within conveying structural information from the multichannel EEG. Such formulation automatically includes crucial spatial information and improves detection performance. SIGNIFICANCE Our solution facilitates accurate classification performance for small training sets, therefore, it potentially reduces the time needed to train the detector before starting monitoring.

ICA extracts epileptic sources from fMRI in EEG-negative patients: a retrospective validation study.

Simultaneous EEG-fMRI has proven to be useful in localizing interictal epileptic activity. However, the applicability of traditional GLM-based analysis is limited as interictal spikes are often not seen on the EEG inside the scanner. Therefore, we aim at extracting epileptic activity purely from the fMRI time series using independent component analysis (ICA). To our knowledge, we show for the first time that ICA can find sources related to epileptic activity in patients where no interictal spikes were recorded in the EEG. The epileptic components were identified retrospectively based on the known localization of the ictal onset zone (IOZ). We demonstrate that the selected components truly correspond to epileptic activity, as sources extracted from patients resemble significantly better the IOZ than sources found in healthy controls. Furthermore, we show that the epileptic components in patients with and without spikes recorded inside the scanner resemble the IOZ in the same degree. We conclude that ICA of fMRI has the potential to extend the applicability of EEG-fMRI for presurgical evaluation in epilepsy.

A prospective fMRI-based technique for localising the epileptogenic zone in presurgical evaluation of epilepsy.

There is growing evidence for the benefits of simultaneous EEG-fMRI as a non-invasive localising tool in the presurgical evaluation of epilepsy. However, many EEG-fMRI studies fail due to the absence of interictal epileptic discharges (IEDs) on EEG. Here we present an algorithm which makes use of fMRI as sole modality to localise the epileptogenic zone (EZ). Recent studies using various model-based or data-driven fMRI analysis techniques showed that it is feasible to find activation maps which are helpful in the detection of the EZ. However, there is lack of evidence that these techniques can be used prospectively, due to (a) their low specificity, (b) selecting multiple activation maps, or (c) a widespread epileptic network indicated by the selected maps. In the current study we present a method based on independent component analysis and a cascade of classifiers that exclusively detects a single map related to interictal epileptic brain activity. In order to establish the sensitivity and specificity of the proposed method, it was evaluated on a group of 18 EEG-negative patients with a single well-defined EZ and 13 healthy controls. The results show that our method provides maps which correctly indicate the EZ in several (N=4) EEG-negative cases but at the same time maintaining a high specificity (92%). We conclude that our fMRI-based approach can be used in a prospective manner, and can extend the applicability of fMRI to EEG-negative cases.

Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment

Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG.

Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks.

OBJECTIVE In the past few years there has been a growing interest in studying brain functioning in natural, real-life situations. Mobile EEG allows to study the brain in real unconstrained environments but it faces the intrinsic challenge that it is impossible to disentangle observed changes in brain activity due to increase in cognitive demands by the complex natural environment or due to the physical involvement. In this work we aim to disentangle the influence of cognitive demands and distractions that arise from such outdoor unconstrained recordings. APPROACH We evaluate the ERP and single trial characteristics of a three-class auditory oddball paradigm recorded in outdoor scenarios while peddling on a fixed bike or biking freely around. In addition we also carefully evaluate the trial specific motion artifacts through independent gyro measurements and control for muscle artifacts. MAIN RESULTS A decrease in P300 amplitude was observed in the free biking condition as compared to the fixed bike conditions. Above chance P300 single-trial classification in highly dynamic real life environments while biking outdoors was achieved. Certain significant artifact patterns were identified in the free biking condition, but neither these nor the increase in movement (as derived from continuous gyrometer measurements) can explain the differences in classification accuracy and P300 waveform differences with full clarity. The increased cognitive load in real-life scenarios is shown to play a major role in the observed differences. SIGNIFICANCE Our findings suggest that auditory oddball results measured in natural real-life scenarios are influenced mainly by increased cognitive load due to being in an unconstrained environment.

The quest for single trial correlations in multimodal EEG-fMRI data

In the past decade, technological advances have made it possible to reliably measure brain activity using simultaneous EEG-fMRI recordings inside an MR scanner. The main challenge then became to investigate the coupling between the EEG and fMRI signals in order to benefit from the simultaneously integrated temporal and spatial resolution. Although it is crucial to know when features in EEG and fMRI are expected to correlate with each other before the identification of common sources from multimodal data is possible, it is still a matter of debate. In this study, we address this question by analysing EEG and fMRI data separately from a face processing task. We show that we are able to reliably estimate single trial (ST) dynamics of face processing in EEG and fMRI data separately in four subjects. However, no correlation is found between the modalities. This implies that in this task modality-specific information is larger than the information that is shared by the modalities.

Tensor decompositions and data fusion in epileptic electroencephalography and functional magnetic resonance imaging data

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) record a mixture of ongoing neural processes, physiological and nonphysiological noise. The pattern of interest, such as epileptic activity, is often hidden within this noisy mixture. Therefore, blind source separation (BSS) techniques, which can retrieve the activity pattern of each underlying source, are very useful. Tensor decomposition techniques are very well suited to solve the BSS problem, as they provide a unique solution under mild constraints. Uniqueness is crucial for an unambiguous interpretation of the components, matching them to true neural processes and characterizing them using the component signatures. Moreover, tensors provide a natural representation of the inherently multidimensional EEG and fMRI, and preserve the structural information defined by the interdependencies among the various modes such as channels, time, patients, etc. Despite the well‐developed theoretical framework, tensor‐based analysis of real, large‐scale clinical datasets is still scarce. Indeed, the application of tensor methods is not straightforward. Finding an appropriate tensor representation, suitable tensor model, and interpretation are application dependent choices, which require expertise both in neuroscience and in multilinear algebra. The aim of this paper is to provide a general guideline for these choices and illustrate them through successful applications in epilepsy. WIREs Data Mining Knowl Discov 2017, 7:e1197. doi: 10.1002/widm.1197

Automated analysis of brain activity for seizure detection in zebrafish models of epilepsy

BACKGROUND Epilepsy is a chronic neurological condition, with over 30% of cases unresponsive to treatment. Zebrafish larvae show great potential to serve as an animal model of epilepsy in drug discovery. Thanks to their high fecundity and relatively low cost, they are amenable to high-throughput screening. However, the assessment of seizure occurrences in zebrafish larvae remains a bottleneck, as visual analysis is subjective and time-consuming. NEW METHOD For the first time, we present an automated algorithm to detect epileptic discharges in single-channel local field potential (LFP) recordings in zebrafish. First, candidate seizure segments are selected based on their energy and length. Afterwards, discriminative features are extracted from each segment. Using a labeled dataset, a support vector machine (SVM) classifier is trained to learn an optimal feature mapping. Finally, this SVM classifier is used to detect seizure segments in new signals. RESULTS We tested the proposed algorithm both in a chemically-induced seizure model and a genetic epilepsy model. In both cases, the algorithm delivered similar results to visual analysis and found a significant difference in number of seizures between the epileptic and control group. COMPARISON WITH EXISTING METHODS Direct comparison with multichannel techniques or methods developed for different animal models is not feasible. Nevertheless, a literature review shows that our algorithm outperforms state-of-the-art techniques in terms of accuracy, precision and specificity, while maintaining a reasonable sensitivity. CONCLUSION Our seizure detection system is a generic, time-saving and objective method to analyze zebrafish LPF, which can replace visual analysis and facilitate true high-throughput studies.

Online Automated Seizure Detection in Temporal Lobe Epilepsy Patients Using Single-lead ECG

Automated seizure detection in a home environment has been of increased interest the last couple of decades. The electrocardiogram is one of the signals that is suited for this application. In this paper, a new method is described that classifies different heart rate characteristics in order to detect seizures from temporal lobe epilepsy patients. The used support vector machine classifier is trained on data from other patients, so that the algorithm can be used directly from the start of each new recording. The algorithm was tested on a dataset of more than 918[Formula: see text]h of data coming from 17 patients containing 127 complex partial and generalized partial seizures. The algorithm was able to detect 81.89% of the seizures, with on average 1.97 false alarms per hour. These results show a strong drop in the number of false alarms of more than 50% compared to other heart rate-based patient-independent algorithms from the literature, at the expense of a slightly higher detection delay of 17.8s on average.

Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring.

Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS) and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP), assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + ϵ. SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP) with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first 3 days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen saturation from the NIRS measurements, facilitating the use of NIRS as a surrogate measure for cerebral blood flow (CBF). The second case study used data from a 3-years old infant under Extra Corporeal Membrane Oxygenation (ECMO), here SIDE-ObSP decomposed cerebral/peripheral tissue oxygenation, as a sum of the partial contributions from different systemic variables, facilitating the comparison between the effects of each systemic variable on the cerebral/peripheral hemodynamics.