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

Merging Deep Learning with Expert Knowledge for Seizure Onset Zone localization from rs-fMRI in Pediatric Pharmaco Resistant Epilepsy

Surgical disconnection of Seizure Onset Zones (SOZs) at an early age is an effective treatment for Pharmaco-Resistant Epilepsy (PRE). Pre-surgical localization of SOZs with intra-cranial EEG (iEEG) requires safe and effective depth electrode placement. Resting-state functional Magnetic Resonance Imaging (rs-fMRI) combined with signal decoupling using independent component (IC) analysis has shown promising SOZ localization capability that guides iEEG lead placement. However, SOZ ICs identification requires manual expert sorting of 100s of ICs per patient by the surgical team which limits the reproducibility and availability of this pre-surgical screening. Automated approaches for SOZ IC identification using rs-fMRI may use deep learning (DL) that encodes intricacies of brain networks from scarcely available pediatric data but has low precision, or shallow learning (SL) expert rule-based inference approaches that are incapable of encoding the full spectrum of spatial features. This paper proposes DeepXSOZ that exploits the synergy between DL based spatial feature and SL based expert knowledge encoding to overcome performance drawbacks of these strategies applied in isolation. DeepXSOZ is an expert-in-the-loop IC sorting technique that a) can be configured to either significantly reduce expert sorting workload or operate with high sensitivity based on expertise of the surgical team and b) can potentially enable the usage of rs-fMRI as a low cost outpatient pre-surgical screening tool. Comparison with state-of-art on 52 children with PRE shows that DeepXSOZ achieves sensitivity of 89.79%, precision of 93.6% and accuracy of 84.6%, and reduces sorting effort by 6.7-fold. Knowledge level ablation studies show a pathway towards maximizing patient outcomes while optimizing the machine-expert collaboration for various scenarios.Comment: This paper is currently under review in IEEE Journa

Effects of intracranial stimulation and the involvement of the human parahippocampal cortex in perception

How the human brain translates photons hitting the retina into conscious perception remains an open question. Throughout the medial temporal lobe (MTL), there are neurons (called concept cells) that change their firing rate when that neuron's preferred concept, e.g., a specific person or object, is seen. The firing rate of concept cells is correlated with perception. Nevertheless, it remains unclear whether or to what extent concept cells are involved in perceptogenesis, i.e., the creation of conscious percepts. Inferring from studies in monkeys, concept-specific neurons involved in perceptogenesis would be expected along the ventral and dorsal stream of visual processing (also called the what and where pathway, respectively). Various regions that are part of the dorsal stream are connected to the parahippocampal cortex (PHC), a region within the MTL. Compared to other MTL regions, lower selectivity, the absence of multimodal responses, and especially the shorter response latencies do not exclude an involvement of the PHC in perceptogenesis. In fact, damage to the parahippocampal place area (PPA, a part of the PHC) results in topographical disorientation. The goal of this thesis is to test the involvement of the PHC in perception by using electrical stimulation during a forced-choice categorization task involving landscapes versus animals. First, we determined effective parameters for intracranial stimulation of brain tissue in epilepsy patients implanted with depth-electrodes for seizure monitoring. We investigated the effects of amplitude, phase width, frequency, and pulse-train duration on neuronal firing, the local field potential (LFP), and behavioral responses to evoked percepts. Frequency and charge per phase were the most influential parameters on all three signals. Both parameters showed a positive effect on event-related potentials (ERPs) in the LFP. Higher frequencies (especially around 200 Hz) lead to a short-term inhibition of neuronal firing, while higher charge per phase can have an inhibitory or excitatory effect on neuronal firing. All parameters had a positive effect on the reports of evoked percepts; on reports of phosphenes in response to stimulating close to the optic radiation as well as on reports of auditory verbal hallucinations in response to stimulating Heschl's gyrus. Using functional magnetic resonance imaging (fMRI), we found that the PPA, i.e., the part of the PHC that is most selective towards images of landscapes, is rather small (up to 1‰ of total brain volume per hemisphere) with varying degrees of hemispheric laterality. Stimulating the PHC outside of the PPA - using a 100 ms high-frequency pulse train delivered at the natural response latency of the PHC - had no effect on categorizing landscapes. However, stimulating inside the PPA, close to the peak activation of the fMRI cluster, resulted in a 7% to 10% increase in landscape responses to ambiguous stimuli. Furthermore, stimulating the PPA also led to an increase in behavioral response time, especially to images with a predominant landscape component. None of our patients reported visual hallucinations of places or scenes in response to our stimulation protocols. Our data suggests that the PPA is involved in the perceptogenesis of landscapes at a stage that does not reach awareness, while the rest of the PHC is unlikely to be involved in perceptogenesis, at least not as it pertains to the perception of landscapes or animals. We also developed an online spike sorting algorithm and an adaptive screening procedure for concept cells to pave the way for new paradigms involving informed feedback

Algorithms for Neural Prosthetic Applications

abstract: In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods.Dissertation/ThesisDoctoral Dissertation Bioengineering 201

Automated classification of human epileptic spikes for the purpose of modelling bold changes using simultaneous intracranial EEG-fMRI

Mapping the BOLD correlates of interictal epileptiform discharges (IEDs) using EEG-fMRI can provide a unique insight into the region(s) responsible for their generation. Scalp EEG-fMRI studies have shown to provide added clinical value in the localisation of the epileptogenic zone in patients with pharmacoresistant epilepsy undergoing presurgical evaluation. However, scalp EEG has limited sensitivity in detecting IEDs as only a small percentage of the underlying electrical activity is recorded. Intracranial EEG (icEEG) provides a higher sensitivity of detecting underlying IEDs compared to scalp EEG due to the electrodes being closer to their generators. Recent safety and feasibility studies have allowed the acquisition of simultaneous icEEG-fMRI circumventing the lack of whole brain coverage of icEEG. Therefore, icEEG-fMRI has the potential to provide unprecedented insight in the relationship between the region(s) generating IEDs and the epileptogenic zone. However, one of the main challenges associated with icEEG-fMRI data is the difficulty of forming a parsimonious model of potential BOLD changes from the complex spatio-temporal dynamics of icEEG IEDs. The aim of this thesis is to provide a solution for a more consistent and less biased marking of icEEG IEDs using an automated neuronal spike classification algorithm, Wave_clus (WC), for the purpose of producing more biological meaningful IED-related BOLD maps. Adapting the icEEG IED dataset to Wave_clus was the first problem tackled which involved developing a new algorithm that identified the peak of the spiky component of an IED and defining an optimal IED classification epoch time-window. The two chapters that followed involved assessing the performance of WC as an icEEG IED classifier. First, I assessed the performance by comparing WC IED classification to the classification of multiple EEG reviewers using a novel validation scheme. This was determined by analysing whether WC-human agreement variability falls within inter-reviewer agreement variability and comparing the individual IED class labels visually and quantitatively. In this regard WC performance was found to be indistinguishable to that of EEG reviewers. Second I assessed the performance of WC by comparing the IED-related BOLD maps obtained using WC to those obtained using the visual/conventional approach. I found that WC was able to produce more biologically meaningful IED-related BOLD maps indicating that this approach can be used to further explore the region(s) responsible for generating IEDs in patients that have undergone icEEG-fMRI

Resource efficient on-node spike sorting

Current implantable brain-machine interfaces are recording multi-neuron activity by utilising multi-channel, multi-electrode micro-electrodes. With the rapid increase in recording capability has come more stringent constraints on implantable system power consumption and size. This is even more so with the increasing demand for wireless systems to increase the number of channels being monitored whilst overcoming the communication bottleneck (in transmitting raw data) via transcutaneous bio-telemetries. For systems observing unit activity, real-time spike sorting within an implantable device offers a unique solution to this problem. However, achieving such data compression prior to transmission via an on-node spike sorting system has several challenges. The inherent complexity of the spike sorting problem arising from various factors (such as signal variability, local field potentials, background and multi-unit activity) have required computationally intensive algorithms (e.g. PCA, wavelet transform, superparamagnetic clustering). Hence spike sorting systems have traditionally been implemented off-line, usually run on work-stations. Owing to their complexity and not-so-well scalability, these algorithms cannot be simply transformed into a resource efficient hardware. On the contrary, although there have been several attempts in implantable hardware, an implementation to match comparable accuracy to off-line within the required power and area requirements for future BMIs have yet to be proposed. Within this context, this research aims to fill in the gaps in the design towards a resource efficient implantable real-time spike sorter which achieves performance comparable to off-line methods. The research covered in this thesis target: 1) Identifying and quantifying the trade-offs on subsequent signal processing performance and hardware resource utilisation of the parameters associated with analogue-front-end. Following the development of a behavioural model of the analogue-front-end and an optimisation tool, the sensitivity of the spike sorting accuracy to different front-end parameters are quantified. 2) Identifying and quantifying the trade-offs associated with a two-stage hybrid solution to realising real-time on-node spike sorting. Initial part of the work focuses from the perspective of template matching only, while the second part of the work considers these parameters from the point of whole system including detection, sorting, and off-line training (template building). A set of minimum requirements are established which ensure robust, accurate and resource efficient operation. 3) Developing new feature extraction and spike sorting algorithms towards highly scalable systems. Based on waveform dynamics of the observed action potentials, a derivative based feature extraction and a spike sorting algorithm are proposed. These are compared with most commonly used methods of spike sorting under varying noise levels using realistic datasets to confirm their merits. The latter is implemented and demonstrated in real-time through an MCU based platform.Open Acces