Wearable electroencephalography for long-term monitoring and diagnostic purposes

Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting, miniature and wearable devices that can be easily worn by patients will result in more EEG data being collected for extended monitoring periods. This thesis presents three new fabricated systems, in the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of epilepsy and sleep disorders by detecting specific clinically important EEG events on the sensor node, while discarding background activity. The power consumption of the WEEG monitoring device incorporating these systems can be reduced since the transmitter, which is the dominating element in terms of power consumption, will only become active based on the output of these systems. Candidate interictal activity is identified by the developed analog-based interictal spike selection system-on-chip (SoC), using an approximation of the Continuous Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW. Experimental results reveal that the SoC is able to identify 87% of interictal spikes correctly while only transmitting 45% of the data. Sections of EEG data containing likely ictal activity are detected by an analog seizure selection SoC using the low complexity line length feature. This SoC is fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental results, the fabricated SoC is able to correctly detect 83% of seizure episodes while transmitting 52% of the overall EEG data. A single-channel analog-based sleep spindle detection SoC is developed to aid the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic events of sleep. The system identifies spindle events by monitoring abrupt changes in the input EEG. An approximation of the median frequency calculation, incorporated as part of the system, allows for non-spindle activity incorrectly identified by the system as sleep spindles to be discarded. The sleep spindle detection SoC is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC achieves a sensitivity and specificity of 71.5% and 98% respectively.Open Acces

Learning more with less data using domain-guided machine learning: the case for health data analytics

The United States is facing a shortage of neurologists with severe consequences: a) average wait-times to see neurologists are increasing, b) patients with chronic neurological disorders are unable to receive diagnosis and care in a timely fashion, and c) there is an increase in neurologist burnout leading to physical and emotional exhaustion. Present-day neurological care relies heavily on time-consuming visual review of patient data (e.g., neuroimaging and electroencephalography (EEG)), by expert neurologists who are already in short supply. As such, the healthcare system needs creative solutions that can increase the availability of neurologists to patient care. To meet this need, this dissertation develops a machine-learning (ML)-based decision support framework for expert neurologists that focuses the experts’ attention to actionable information extracted from heterogeneous patient data and reduces the need for expert visual review. Specifically, this dissertation introduces a novel ML framework known as domain-guided machine learning (DGML) and demonstrates its usefulness by improving the clinical treatments of two major neurological diseases, epilepsy and Alzheimer’s disease. In this dissertation, the applications of this framework are illustrated through several studies conducted in collaboration with the Mayo Clinic, Rochester, Minnesota. Chapters 3, 4, and 5 describe the application of DGML to model the transient abnormal discharges in the brain activity of epilepsy patients. These studies utilized the intracranial EEG data collected from epilepsy patients to delineate seizure generating brain regions without observing actual seizures; whereas, Chapters 6, 7, 8, and 9 describe the application of DGML to model the subtle but permanent changes in brain function and anatomy, and thereby enable the early detection of chronic epilepsy and Alzheimer’s disease. These studies utilized the scalp EEG data of epilepsy patients and two population-level multimodal imaging datasets collected from elderly individuals

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Functional network correlates of language and semiology in epilepsy

Epilepsy surgery is appropriate for 2-3% of all epilepsy diagnoses. The goal of the presurgical workup is to delineate the seizure network and to identify the risks associated with surgery. While interpretation of functional MRI and results in EEG-fMRI studies have largely focused on anatomical parameters, the focus of this thesis was to investigate canonical intrinsic connectivity networks in language function and seizure semiology. Epilepsy surgery aims to remove brain areas that generate seizures. Language dysfunction is frequently observed after anterior temporal lobe resection (ATLR), and the presurgical workup seeks to identify the risks associated with surgical outcome. The principal aim of experimental studies was to elaborate understanding of language function as expressed in the recruitment of relevant connectivity networks and to evaluate whether it has value in the prediction of language decline after anterior temporal lobe resection. Using cognitive fMRI, we assessed brain areas defined by parameters of anatomy and canonical intrinsic connectivity networks (ICN) that are involved in language function, specifically word retrieval as expressed in naming and fluency. fMRI data was quantified by lateralisation indices and by ICN_atlas metrics in a priori defined ICN and anatomical regions of interest. Reliability of language ICN recruitment was studied in 59 patients and 30 healthy controls who were included in our language experiments. New and established language fMRI paradigms were employed on a three Tesla scanner, while intellectual ability, language performance and emotional status were established for all subjects with standard psychometric assessment. Patients who had surgery were reinvestigated at an early postoperative stage of four months after anterior temporal lobe resection. A major part of the work sought to elucidate the association between fMRI patterns and disease characteristics including features of anxiety and depression, and prediction of postoperative language outcome. We studied the efficiency of reorganisation of language function associated with disease features prior to and following surgery. A further aim of experimental work was to use EEG-fMRI data to investigate the relationship between canonical intrinsic connectivity networks and seizure semiology, potentially providing an avenue for characterising the seizure network in the presurgical workup. The association of clinical signs with the EEG-fMRI informed activation patterns were studied using the data from eighteen patients’ whose seizures and simultaneous EEG-fMRI activations were reported in a previous study. The accuracy of ICN_atlas was validated and the ICN construct upheld in the language maps of TLE patients. The ICN construct was not evident in ictal fMRI maps and simulated ICN_atlas data. Intrinsic connectivity network recruitment was stable between sessions in controls. Amodal linguistic processing and the relevance of temporal intrinsic connectivity networks for naming and that of frontal intrinsic connectivity networks for word retrieval in the context of fluency was evident in intrinsic connectivity networks regions. The relevance of intrinsic connectivity networks in the study of language was further reiterated by significant association between some disease features and language performance, and disease features and activation in intrinsic connectivity networks. However, the anterior temporal lobe (ATL) showed significantly greater activation compared to intrinsic connectivity networks – a result which indicated that ATL functional language networks are better studied in the context of the anatomically demarked ATL, rather than its functionally connected intrinsic connectivity networks. Activation in temporal lobe networks served as a predictor for naming and fluency impairment after ATLR and an increasing likelihood of significant decline with greater magnitude of left lateralisation. Impairment of awareness served as a significant classifying feature of clinical expression and was significantly associated with the inhibition of normal brain functions. Canonical intrinsic connectivity networks including the default mode network were recruited along an anterior-posterior anatomical axis and were not significantly associated with clinical signs

Advanced Invasive Neurophysiological Methods to Aid Decision Making in Paediatric Epilepsy Surgery

For patients with drug-resistant focal epilepsy, surgery is the most effective treatment to attain seizure freedom. Intracranial electroencephalogram investigations succeed in defining the seizure onset zone (SOZ) where non-invasive methods fail to identify a single seizure generator. However, resection of the SOZ does not always lead to a surgical benefit and, in addition, eloquent functions like language might be compromised. The aim of this thesis was to use advanced invasive neurophysiological methods to improve pre-surgical planning in two ways. The first aim was to improve delineation of the pathological tissue, the SOZ using novel quantitative neurophysiological biomarkers: high gamma activity (80–150Hz) phase-locked to low frequency iEEG discharges (phase-locked high gamma, PLHG) and high frequency oscillations called fast ripples (FR, 250–500Hz). Resection of contacts containing these markers were recently reported to lead to an improved seizure outcome. The current work shows the first replication of the PLHG metric in a small adult pilot study and a larger paediatric cohort. Furthermore, I tested whether surgical removal of PLHG- and/or FR-generating brain areas resulted in better outcome compared to the current clinical SOZ delineation. The second aim of this work was to aid delineation of eloquent language cortex. Invasive event-related potentials (iERP) and spectral changes in the beta and gamma frequency bands were used to determine cortical dynamics during speech perception and production across widespread brain regions. Furthermore, the relationship between these cortical dynamics and the relationship to electrical stimulation responses was explored. For delineation of pathological tissue, the combination of FRs and SOZ proved to be a promising biomarker. Localising language cortex showed the highest level of activity around the perisylvian brain regions with a significantly higher occurrence rate of iERPs compared to spectral changes. Concerning electrical stimulation mapping beta and high gamma frequency bands represented the most promising markers

Signal modelling: A versatile approach for the automatic analysis of the electroencephalogram

Despite recent advances in brain monitoring techniques, the electroencephalogram (EEG) is still widely used in the diagnosis and monitoring of epilepsy. To increase its effectiveness, long-term monitoring of patients was proposed but the large volume of recorded EEG signals produced, made their traditional interpretation by human experts difficult and automatic EEG analysis was proposed as an alternative. This Thesis is concerned, primarily, with the on-line detection of epileptic transients (spikes) in the interictal EEG signals of patients. A review of previous methods, revealed that the limited success of automatic analysis systems was linked to the vagueness of neurophysiological definitions and the subjectiveness of human interpretation, which is based on experience. To address these issues, it was realized that a common point of reference is required for the integration of medical and signal processing expertise, which could be provided by a model of the signal. Early attempts to develop such a model are described. These led to the development of spike detectors based on the derivatives of the EEG. Later, by describing medical definitions with signal processing terminology, a comprehensive model of the signal was constructed. This was based on its decomposition into background activity, spikes, transients and noise and describing each one of them in terms of simple, random signals and quasi-linear systems. This suggested a method of analysis based on inverse modelling for the decomposition of the EEG. The model for transients was estimated off-line. An on-line system, consisting of adaptive prediction error systems, constrained all-pole adaptive systems and a basic signal detection procedure was implemented. Several alternative adaptive realizations were investigated. The spike detection procedure was generalized for the detection of other transients. Finally this procedure was replaced by a Multi-Layer Perceptron neural network, whose inherent ability to learn by example is important, as it provides the means to incorporate medical experience without requiring its explicit quantification. The system is flexible and its extension to detect any number of transients is demonstrated. The method may be applied to other signals and improved by new developments in signal processing

An alternative approach for assessing drug induced seizures, using non-protected larval zebrafish

As many as 9% of epileptic seizures occur as a result of drug toxicity. Identifying compounds with seizurogenic side effects is imperative for assessing compound safety during drug development, however, multiple marketed drugs still have clinical associations with seizures. Moreover, current approaches for assessing seizurogenicity, namely rodent EEG and behavioural studies, are highly resource intensive. This being the case, alternative approaches have been postulated for assessing compound seizurogenicity, including in vitro, in vivo, and in silico methods. In this thesis, experimental work is presented supporting the use of larval zebrafish as a candidate model organism for developing new seizure liability screening approaches. Larval zebrafish are translucent, meaning they are highly amenable to imaging approaches while offering a more ethical alternative to mammalian research. Zebrafish are furthermore highly fecund facilitating capacity for both high replication and high throughput. The primary goal of this thesis was to identify biomarkers in larval zebrafish, both behavioural and physiological, of compounds that increase seizure liability. The efficacy of this model organism for seizure liability testing was assessed through exposure of larval zebrafish to a mechanistically diverse array of compounds, selected for their varying degrees of seizurogenicity. Their central nervous systems were monitored using a variety of different techniques including light sheet microscopy, local field potential recordings, and behavioural monitoring. Data acquired from these measurements were then analysed using a variety of techniques including frequency domain analysis, clustering, functional connectivity, regression, and graph theory. Much of this analysis was exploratory in nature and is reflective of the infancy of the field. Experimental findings suggest that larval zebrafish are indeed sensitive to a wide range of pharmacological mechanisms of action and that drug actions are reflected by behavioural and direct measurements of brain activity. For example, local field potential recordings revealed electrographic responses akin to pre-ictal, inter-ictal and ictal events identified in humans. Ca2+ imaging using light sheet microscopy found global increases in fluorescent intensity and functional connectivity due to seizurogenic drug administration. In addition, [2] further functional connectivity and graph analysis revealed macroscale network changes correlated with drug seizurogenicity and mechanism of action. Finally, analysis of swimming behaviour revealed a strong correlation between high speed swimming behaviours and administration of convulsant compounds. In conclusion, presented herein are data demonstrating the power of functional brain imaging, LFP recordings, and behavioral monitoring in larval zebrafish for assessing the action of neuroactive drugs in a highly relevant vertebrate model. These data help us to understand the relevance of the 4 dpf larval zebrafish for neuropharmacological studies and reveal that even at this early developmental stage, these animals are highly responsive to a wide range of neuroactive compounds across multiple primary mechanisms of action. This represents compelling evidence of the potential utility of larval zebrafish as a model organism for seizure liability testing

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition