Automatic Detection and Classification of Neural Signals in Epilepsy

The success of an epilepsy treatment, such as resective surgery, relies heavily on the accurate identification and localization of the brain regions involved in epilepsy for which patients undergo continuous intracranial electroencephalogram (EEG) monitoring. The prolonged EEG recordings are screened for two main biomarkers of epilepsy: seizures and interictal spikes. Visual screening and quantitation of these two biomarkers in voluminous EEG recordings is highly subjective, labor-intensive, tiresome and expensive. This thesis focuses on developing new techniques to detect and classify these events in the EEG to aid the review of prolonged intracranial EEG recordings. It has been observed in the literature that reliable seizure detection can be made by quantifying the evolution of seizure EEG waveforms. This thesis presents three new computationally simple non-patient-specific (NPS) seizure detection systems that quantify the temporal evolution of seizure EEG. The first method is based on the frequency-weighted-energy, the second method on quantifying the EEG waveform sharpness, while the third method mimics EEG experts. The performance of these new methods is compared with that of three state-of-the-art NPS seizure detection systems. The results show that the proposed systems outperform these state-of-the-art systems. Epilepsy therapies are individualized for numerous reasons, and patient-specific (PS) seizure detection techniques are needed not only in the pre-surgical evaluation of prolonged EEG recordings, but also in the emerging neuro-responsive therapies. This thesis proposes a new model-based PS seizure detection system that requires only the knowledge of a template seizure pattern to derive the seizure model consisting of a set of basis functions necessary to utilize the statistically optimal null filters (SONF) for the detection of the subsequent seizures. The results of the performance evaluation show that the proposed system provides improved results compared to the clinically-used PS system. Quantitative analysis of the second biomarker, interictal spikes, may help in the understanding of epileptogenesis, and to identify new epileptic biomarkers and new therapies. However, such an analysis is still done manually in most of the epilepsy centers. This thesis presents an unsupervised spike sorting system that does not require a priori knowledge of the complete spike data

Haemodynamic correlates of interictal and ictal epileptic discharges and ictal semiology using simultaneous scalp video-EEG-fMRI and intracranial EEG-fMRI

Interictal and ictal epileptic discharges are produced by focal and widespread dysfunctional neuronal networks. Identification and characterization of epileptic discharges underlie the diagnosis and the choice of treatment for epilepsy patients. A better knowledge of the generation, propagation and localisation of epileptic discharges, and their interaction with the physiological and pathological brain networks can be very helpful in planning epilepsy surgery and minimizing the risk of damaging the physiological brain networks. This work describes a number of methodological developments and novel applications investigating the epileptic networks in humans using EEG-fMRI. First, I implemented synchronized video recording inside the MRI-scanner during simultaneous EEG-fMRI studies, which did not deteriorate the imaging and EEG data quality. Secondly, I used video recordings to identify physiological activities to be modelled as confounds in the functional imaging data analysis for interictal activity, thus increasing the sensitivity of video-EEG-fMRI. Thirdly, I applied this modelling approach to investigate seizure related functional networks in patients with focal epilepsy. Video recordings allowed partitioning seizures into phases separating the ictal onset related functional networks from propagation related networks. Localisation of the ictal onset related networks may be useful in the planning for epilepsy surgery in a selected group of patients, as demonstrated by their comparison with intracranial-EEG recordings. Further, I investigated haemodynamic changes during preictal period which suggested recruitment of an inhibitory followed by an excitatory network prior to the ictal onset on scalp EEG. In the next step, I used simultaneous intracranial-EEG-fMRI in patients undergoing invasive evaluation, demonstrating that local and remote networks associated with very focal interictal discharges recorded on intracranial-EEG may predict the surgical outcome. Finally, I investigated the interaction of epileptic discharges with the working memory, using scalp video-EEG-fMRI, showing that the presence of epileptic activity may alter the working memory related networks. Methodological constraints, clinical applications and future perspectives are discussed

Critical account of clinical and physiological studies in Rett syndrome

Rett syndrome is the manifestation of an X linked, mainly female, genetic, neurodevelopmental disorder that usually produces profound intellectual and physical disabilities including abnormal muscle tone, with a tendency to develop limb contractures, scoliosis, epilepsy and irregular respiration. There is characteristic hand stereotypy with· poor voluntary hand use, locomotion is compromised and speech is rare. Although the disorder is not progressive many sequele shorten life especially in the most severely affected. Subtle abnormalities, present from birth, are frequently overlooked because there is some developmental progress until a period of regression at around one year of age when speech and hand use diminish. This thesis gives an account of clinical, physiological and genetic studies carried out between 1982 and 2005 with the aim of recording the natural history of the disorder and understanding its clinical manifestations. The subjects of these studies have been people of all ages, mainly from the British Isles, reported to have Rett syndrome by their physicians and families or carers (British Isles Survey, n=l228). Most have been examined and recorded on video by myself, many repeatedly. Fully informed parental consent and appropriate ethical approval has been given for all procedures. The early manifestations of the disorder were investigated from developmental histories and donated videos (78) taken by families before they were aware of the problem. The abnormal respiratory rhythms were investigated and characterised, using non-invasive measures of respiratory rhythm, carbon dioxide, oxygen, heart rate and blood pressure. The poor control of voluntary movement was investigated using electromagnetic stimulation of the cortex to record conduction in the motor pathways. Stereotyped hand movements were analysed from three-dimensional live recording and informal two-dimensional video. The prevalence of a toe anomaly was estimated, visual evoked potentials were recorded and a reported increase in urinary neopterin was investigated. The health of people in the British Survey was monitored longitudinally from family and physician reports and direct clinical examinations, data being stored on computer. Simple scores were generated to indicate separately the severity of the condition and health of the individual. The survey data has been used to estimate the prevalence of the disorder (I in 10,000 females), natural history from birth to death, the predictive value of the earliest signs, survival at different levels of severity, the impact of scoliosis surgery on health and has provided a foundation for studies relating clinical manifestations to specific mutations on the affected gene MECP 2 (Xq28). The studies have indicated the nature of the Rett disorder to be developmental and non-progressive, with primary impact on the processing functions of the brain, probably beginning in the brain stem before birth