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

Classification of De novo post-operative and persistent atrial fibrillation using multi-channel ECG recordings

Atrial fibrillation (AF) is the most sustained arrhythmia in the heart and also the most common complication developed after cardiac surgery. Due to its progressive nature, timely detection of AF is important. Currently, physicians use a surface electrocardiogram (ECG) for AF diagnosis. However, when the patient develops AF, its various development stages are not distinguishable for cardiologists based on visual inspection of the surface ECG signals. Therefore, severity detection of AF could start from differentiating between short-lasting AF and long-lasting AF. Here, de novo post-operative AF (POAF) is a good model for short-lasting AF while long-lasting AF can be represented by persistent AF. Therefore, we address in this paper a binary severity detection of AF for two specific types of AF. We focus on the differentiation of these two types as de novo POAF is the first time that a patient develops AF. Hence, comparing its development to a more severe stage of AF (e.g., persistent AF) could be beneficial in unveiling the electrical changes in the atrium. To the best of our knowledge, this is the first paper that aims to differentiate these different AF stages. We propose a method that consists of three sets of discriminative features based on fundamentally different aspects of the multi-channel ECG data, namely based on the analysis of RR intervals, a greyscale image representation of the vectorcardiogram, and the frequency domain representation of the ECG. Due to the nature of AF, these features are able to capture both morphological and rhythmic changes in the ECGs. Our classification system consists of a random forest classifier, after a feature selection stage using the ReliefF method. The detection efficiency is tested on 151 patients using 5-fold cross-validation. We achieved 89.07% accuracy in the classification of de novo POAF and persistent AF. The results show that the features are discriminative to reveal the severity of AF. Moreover, inspection of the most important features sheds light on the different characteristics of de novo post-operative and persistent AF.</p

Learning from Structured EEG and fMRI Data Supporting the Diagnosis of Epilepsy (Leren van gestructureerde EEG en fMRI data voor ondersteuning van de diagnose van epilepsie)

Epilepsy is a neurological condition that manifests in epileptic seizures as a result of an abnormal, synchronous activity of a large group of neurons. Depending on the affected brain regions, seizures produce various severe clinical symptoms. Epilepsy cannot be cured and in many cases is not controlled by medication either. Surgical resection of the regionresponsible for generating the epileptic seizures might offer remedy for these patients. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measure the changes of brain activity in time over different locations of the brain. As such, they provide valuable information on the nature, the timing and the spatial origin of the epileptic activity. Unfortunately, both techniques record activity of different brain and artefact sources as well. Hence, EEG and fMRI signals are characterised by low signal to noise ratio. Data quality and the vast amount of recordings make the visual interpretation of these signals impractical.Therefore, this thesis aims at developing automated analysis techniques which can support the accurate diagnosis of the epilepsy syndrome. The fundamental principle behind the proposed approaches is to exploit the characteristic spatiotemporal structure underlying epileptic brain signals. With this mindset, we identify problems and offer solutions for three crucial aspects of presurgical evaluation.First, an automated seizure detection algorithm is developed. While traditional detectors analyse each EEG channel separately, our solution incorporates spatial information from the multichannel EEG data. To this end, we apply a regularisation scheme using nuclear norm, a penaltyterm inducing low-rank structure. It is shown that the proposed approach improves detection performance compared to traditional solutions, evenif less seizure information is available for training.Once a seizure occurrence is identified, the next step in the diagnostic procedure is to determine the seizure onset zone (SOZ) based on the EEG. Blind source separation (BSS) techniques can help visual interpretation by removing artefacts contaminating the seizure pattern, or can extract the clean seizure source itself. As each method uses different model assumptions, their use is appropriate in certain situations and are limited in others. In this thesis a novel tensor based technique, namely Block Term Decomposition (BTD) is applied to extract sources from the EEG data. Depending on the chosen tensor representation, this formulation allows to model seizures as a sum of exponentially damped sinusoids or as oscillatoryphenomena which evolve in frequency or spread to remote brain regions over time.Although seizure activity patterns provide important localising information, due to the rare occurrence of seizures this is a time consuming procedure. Alternatively, localising the epileptic networkbased on interictal fMRI recordings can offer a surrogate. EEG-correlated fMRI analysis has already proven useful for this purpose, however, a purely fMRI based approach would be invaluable in case no reliable EEG information is available. To this end, independent component analysis (ICA) is applied to extract spatially independent components from the fMRI time series. It is demonstrated that ICA can extract epileptic sources which substantially overlap with the SOZ. Finally, a method is developed which selects the epileptic source blinded to all other clinical information. As a result, the spatial map corresponding to the selected epileptic component can localise the SOZ.Presurgical evaluation relies on multidisciplinary consensus. A surgery is planned in case concordant data are obtained from all clinical examinations and imaging modalities.The techniques proposed in this thesis can contribute to the current procedure by extending the applicability of existing techniques and providing precise information in a time effective way.status: publishe

Classifying the Auditory P300 using mobile EEG recordings without calibration phase

One of the major drawbacks in mobile EEG Brain Computer Interfaces (BCI) is the need for subject specific training data to train a classifier. By removing the need for supervised classification and calibration phase, new users could start immediate interaction with a BCI. We propose a solution to exploit the structural difference by means of canonical polyadic decomposition (CPD) for three-class auditory oddball data without the need for subject-specific information. We achieve this by adding average event-related-potential (ERP) templates to the CPD model. This constitutes a novel similarity measure between single-trial pairs and known-templates, which results in a fast and interpretable classifier. These results have similar accuracy to those of the supervised and cross-validated stepwise LDA approach but without the need for having subject-dependent data. Therefore the described CPD method has a significant practical advantage over the traditional and widely used approach.status: publishe

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 scenario's 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.journal_title: Journal of Neural Engineering article_type: paper article_title: Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks copyright_information: © 2016 IOP Publishing Ltd date_received: 2016-01-26 date_accepted: 2016-06-07 date_epub: 2016-06-28status: publishe

Tensor-Based Classification of Auditory Mobile BCI without Subject-Specific Calibration Phase

OBJECTIVE: One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. APPROACH: We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. MAIN RESULTS: The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. SIGNIFICANCE: The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.journal_title: Journal of Neural Engineering article_type: paper article_title: Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase copyright_information: © 2016 IOP Publishing Ltd date_received: 2015-07-17 date_accepted: 2015-12-23 date_epub: 2016-02-01status: publishe

Testing for the presence of correlation changes in a multivariate time series: A permutation based approach

Detecting abrupt correlation changes in multivariate time series is crucial in many application fields such as signal processing, functional neuroimaging, climate studies, and financial analysis. To detect such changes, several promising correlation change tests exist, but they may suffer from severe loss of power when there is actually more than one change point underlying the data. To deal with this drawback, we propose a permutation based significance test for Kernel Change Point (KCP) detection on the running correlations. Given a requested number of change points K, KCP divides the time series into K + 1 phases by minimizing the within-phase variance. The new permutation test looks at how the average within-phase variance decreases when K increases and compares this to the results for permuted data. The results of an extensive simulation study and applications to several real data sets show that, depending on the setting, the new test performs either at par or better than the state-of-the art significance tests for detecting the presence of correlation changes, implying that its use can be generally recommended.status: publishe

Alpha and low-beta oscillatory patterns extracted with Canonical PolyadicDecomposition relate to LDA classifier performance in real-life Mobile

status: publishe