A Hidden Markov Factor Analysis Framework for Seizure Detection in Epilepsy Patients

Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to localize the seizure activity. In this study, we developed and tested a hidden Markov factor analysis (HMFA) framework for automated seizure detection based on different features such as total effective inflow which is calculated based on connectivity measures between different sites of the brain. The algorithm was tested on long-term (2.4-7.66 days) continuous sEEG recordings from three patients and a total of 16 seizures, producing a mean sensitivity of 96.3% across all seizures, a mean specificity of 3.47 false positives per hour, and a mean latency of 3.7 seconds form the actual seizure onset. The latency was negative for a few of the seizures which implies the proposed method detects the seizure prior to its onset. This is an indication that with some extension the proposed method is capable of seizure prediction

The multifocal visual evoked cortical potential in visual field mapping: a methodological study.

The application of multifocal techniques to the visual evoked cortical potential permits objective electrophysiological mapping of the visual field. The multifocal visual evoked cortical potential (mfVECP) presents several technical challenges. Signals are small, are influenced by a number of sources of noise and waveforms vary both across the visual field and between subjects due to the complex geometry of the visual cortex. Together these factors hamper the ability to distinguish between a mfVECP response from the healthy visual pathway, and a response that is reduced or absent and is therefore representative of pathology. This thesis presents a series of methodological investigations with the aim of maximising the information available in the recorded electrophysiological response, thereby improving the performance of the mfVECP. A novel method of calculating the signal to noise ratio (SNR) of mfVECP waveform responses is introduced. A noise estimate unrelated to the response of the visual cortex to the visual stimulus is created. This is achieved by cross-correlating m-sequences which are created when the orthogonal set of m-sequences are created but are not used to control a stimulus region, with the physiological record. This metric is compared to the approach of defining noise within a delayed time window and shows good correlation. ROC analysis indicates a small improvement in the ability to distinguish between physiological waveform responses and noise. Defining the signal window as 45-250ms is recommended. Signal quality is improved by post-acquisition bandwidth filtering. A wide range of bandwidths are compared and the greatest gains are seen with a bandpass of 3 to 20Hz applied after cross-correlation. Responses evoked when stimulation is delivered using a cathode ray tube (CRT) and a liquid crystal display (LCD) projector system are compared. The mode of stimulus delivery affects the waveshape of responses. A significantly higher SNR is seen in waveforms is shown in waveforms evoked by an m=16 bit m-sequence delivered by a CRT monitor. Differences for shorter m-sequences were not statistically significant. The area of the visual field which can usefully be tested is investigated by increasing the field of view of stimulation from 20° to 40° of radius in 10° increments. A field of view of 30° of radius is shown to provide stimulation of as much of the visual field as possible without losing signal quality. Stimulation rates of 12.5 to 75Hz are compared. Slowing the stimulation rate produced increases waveform amplitudes, latencies and SNR values. The best performance was achieved with 25Hz stimulation. It is shown that a six-minute recording stimulated at 25Hz is superior to an eight-minute, 75Hz acquisition. An electrophysiology system capable of providing multifocal stimulation, synchronising with the acquisition of data from a large number of electrodes and performing cross-correlation has been created. This is a powerful system which permits the interrogation of the dipoles evoked within the complex geometry of the visual cortex from a very large number of orientations, which will improve detection ability. The system has been used to compare the performance of 16 monopolar recording channels in detecting responses to stimulation throughout the visual field. A selection of four electrodes which maximise the available information throughout the visual field has been made. It is shown that a several combinations of four electrodes provide good responses throughout the visual field, but that it is important to have them distributed on either hemisphere and above and below Oz. A series of investigations have indicated methods of maximising the available information in mfVECP recordings and progress the technique towards becoming a robust clinical tool. A powerful multichannel multifocal electrophysiology system has been created, with the ability to simultaneously acquire data from a very large number of bipolar recording channels and thereby detect many small dipole responses to stimulation of many small areas of the visual field. This will be an invaluable tool in future investigations. Performance has been shown to improve when the presence or absence of a waveform is determined by a novel SNR metric, when data is filtered post-acquisition through a 3-20Hz bandpass after cross-correlation and when a CRT is used to deliver the stimulus. The field of view of stimulation can usefully be extended to a radius of 30° when a 60-region dartboard pattern is employed. Performance can be enhanced at the same time as acquisition time is reduced by 25%, by the use of a 25Hz rate of stimulation instead of the frequently employed rate of 75Hz

Detecting event-related recurrences by symbolic analysis: Applications to human language processing

Quasistationarity is ubiquitous in complex dynamical systems. In brain dynamics there is ample evidence that event-related potentials reflect such quasistationary states. In order to detect them from time series, several segmentation techniques have been proposed. In this study we elaborate a recent approach for detecting quasistationary states as recurrence domains by means of recurrence analysis and subsequent symbolisation methods. As a result, recurrence domains are obtained as partition cells that can be further aligned and unified for different realisations. We address two pertinent problems of contemporary recurrence analysis and present possible solutions for them.Comment: 24 pages, 6 figures. Draft version to appear in Proc Royal Soc

Decoding motor neuron behavior for advanced control of upper limb prostheses

One of the main challenges in upper limb prosthesis control to date is to provide devices intuitive to use and capable to reproduce the natural movements of the arm and hand. One approach to solve this challenge is to use the same control signals for prosthesis control that our nervous system uses to control its muscles. This thesis aims to investigate the possibility of natural, intuitive prosthesis control using neural information obtained with available surface EMG decomposition methods. In order to explore all aspects of such a novel approach, a series of five studies were performed with the final goal of implementing a proof of concept and comparing its performance with state of the art myoelectric control. The performed investigations revealed important insights in motor unit physiology after targeted muscle reinnervation, EMG decomposition in dynamic voluntary contractions of the forearm, and the properties and challenges of neural information based prosthesis control. The main outcome of the thesis is that neural information based prosthesis control is capable to outperform myoelectric approaches in pattern recognition, linear regression and nonlinear regression, as determined by offline performance comparisons. The final proof of concept for this novel approach was a robust regression method based on neuromusculoskeletal modeling. The kinematics estimation of the proposed approach outperformed EMG-based nonlinear regression in both able-bodied subjects and patients with limb deficiency, indicating that using neural information is a promising avenue for advanced myoelectric control.2017-11-3