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Learning from Structured EEG and fMRI Data Supporting the Diagnosis of Epilepsy

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.

Date:19 Sep 2009  →  2 Jun 2014
Disciplines:Other engineering and technology
Project type:PhD project