ARTIFACT CHARACTERIZATION, DETECTION AND REMOVAL FROM NEURAL SIGNALS

Ph.DDOCTOR OF PHILOSOPH

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

Advancing Pattern Recognition Techniques for Brain-Computer Interfaces: Optimizing Discriminability, Compactness, and Robustness

In dieser Dissertation formulieren wir drei zentrale Zielkriterien zur systematischen Weiterentwicklung der Mustererkennung moderner Brain-Computer Interfaces (BCIs). Darauf aufbauend wird ein Rahmenwerk zur Mustererkennung von BCIs entwickelt, das die drei Zielkriterien durch einen neuen Optimierungsalgorithmus vereint. Darüber hinaus zeigen wir die erfolgreiche Umsetzung unseres Ansatzes für zwei innovative BCI Paradigmen, für die es bisher keine etablierte Mustererkennungsmethodik gibt

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

INVESTIGATION, DEVELOPMENT AND APPLICATION OF KNOWLEDGE BASED DIGITAL SIGNAL PROCESSING METHODS FOR ENHANCING HUMAN EEGsJ

This thesis details the development of new and reliable techniques for enhancing the human Electroencephalogram {EEGI. This development has involved the incorporation of adaptive signal processing (ASP) techniques, within an artificial intelligence (Al) paradigm, more closely matching the implicit signal analysis capabilities of the EEG expert. The need for EEG enhancement, by removal of ocular artefact (OA) , is widely recognised. However, conventional ASP techniques for OA removal fail to differentiate between OAs and some abnormal cerebral waveforms, such as frontal slow waves. OA removal often results in the corruption of these diagnostically important cerebral waveforms. However, the experienced EEG expert is often able to differentiate between OA and abnormal slow waveforms, and between different types of OA. This EEG expert knowledge is integrated with selectable adaptive filters in an intelligent OA removal system (tOARS). The EEG is enhanced by only removing OA when OA is identified, and by applying the OA removal algorithm pre-set for the specific OA type. Extensive EEG data acquisition has provided a database of abnormal EEG recordings from over 50 patients, exhibiting a variety of cerebral abnormalities. Structured knowledge elicitation has provided over 60 production rules for OA identification in the presence of abnormal frontal slow waveforms, and for distinguishing between OA types. The lOARS was implemented on personal computer (PCI based hardware in PROLOG and C software languages. 2-second, 18-channel, EEG signal segments are subjected to digital signal processing, to extract salient features from time, frequency, and contextual domains. OA is identified using a forward/backward hybrid inference engine, with uncertainty management, using the elicited expert rules and extracted signal features. Evaluation of the system has been carried out using both normal and abnormal patient EEGs, and this shows a high agreement (82.7%) in OA identification between the lOARS and an EEG expert. This novel development provides a significant improvement in OA removal, and EEG signal enhancement, and will allow more reliable automated EEG analysis. The investigation detailed in this thesis has led to 4 papers, including one in a special proceedings of the lEE, and been subject to several review articles.Department of Neurophysiology, Derriford Hospital, Plymouth, Devo

High Frequency Physiological Data Quality Modelling in the Intensive Care Unit

Intensive care medicine is a resource intense environment in which technical and clinical decision making relies on rapidly assimilating a huge amount of categorical and timeseries physiologic data. These signals are being presented at variable frequencies and of variable quality. Intensive care clinicians rely on high frequency measurements of the patient's physiologic state to assess critical illness and the response to therapies. Physiological waveforms have the potential to reveal details about the patient state in very fine resolution, and can assist, augment, or even automate decision making in intensive care. However, these high frequency time-series physiologic signals pose many challenges for modelling. These signals contain noise, artefacts, and systematic timing errors, all of which can impact the quality and accuracy of models being developed and the reproducibility of results. In this context, the central theme of this thesis is to model the process of data collection in an intensive care environment from a statistical, metrological, and biosignals engineering perspective with the aim of identifying, quantifying, and, where possible, correcting errors introduced by the data collection systems. Three different aspects of physiological measurement were explored in detail, namely measurement of blood oxygenation, measurement of blood pressure, and measurement of time. A literature review of sources of errors and uncertainty in timing systems used in intensive care units was undertaken. A signal alignment algorithm was developed and applied to approximately 34,000 patient-hours of simultaneously collected electroencephalography and physiological waveforms collected at the bedside using two different medical devices

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

Assessing Variability of EEG and ECG/HRV Time Series Signals Using a Variety of Non-Linear Methods

Time series signals, such as Electroencephalogram (EEG) and Electrocardiogram (ECG) represent the complex dynamic behaviours of biological systems. The analysis of these signals using variety of nonlinear methods is essential for understanding variability within EEG and ECG, which potentially could help unveiling hidden patterns related to underlying physiological mechanisms. EEG is a time varying signal, and electrodes for recording EEG at different positions on the scalp give different time varying signals. There might be correlation between these signals. It is important to know the correlation between EEG signals because it might tell whether or not brain activities from different areas are related. EEG and ECG might be related to each other because both of them are generated from one co-ordinately working body. Investigating this relationship is of interest because it may reveal information about the correlation between EEG and ECG signals. This thesis is about assessing variability of time series data, EEG and ECG, using variety of nonlinear measures. Although other research has looked into the correlation between EEGs using a limited number of electrodes and a limited number of combinations of electrode pairs, no research has investigated the correlation between EEG signals and distance between electrodes. Furthermore, no one has compared the correlation performance for participants with and without medical conditions. In my research, I have filled up these gaps by using a full range of electrodes and all possible combinations of electrode pairs analysed in Time Domain (TD). Cross-Correlation method is calculated on the processed EEG signals for different number unique electrode pairs from each datasets. In order to obtain the distance in centimetres (cm) between electrodes, a measuring tape was used. For most of our participants the head circumference range was 54-58cm, for which a medium-sized I have discovered that the correlation between EEG signals measured through electrodes is linearly dependent on the physical distance (straight-line) distance between them for datasets without medical condition, but not for datasets with medical conditions. Some research has investigated correlation between EEG and Heart Rate Variability (HRV) within limited brain areas and demonstrated the existence of correlation between EEG and HRV. But no research has indicated whether or not the correlation changes with brain area. Although Wavelet Transformations (WT) have been performed on time series data including EEG and HRV signals to extract certain features respectively by other research, so far correlation between WT signals of EEG and HRV has not been analysed. My research covers these gaps by conducting a thorough investigation of all electrodes on the human scalp in Frequency Domain (FD) as well as TD. For the reason of different sample rates of EEG and HRV, two different approaches (named as Method 1 and Method 2) are utilised to segment EEG signals and to calculate Pearson’s Correlation Coefficient for each of the EEG frequencies with each of the HRV frequencies in FD. I have demonstrated that EEG at the front area of the brain has a stronger correlation with HRV than that at the other area in a frequency domain. These findings are independent of both participants and brain hemispheres. Sample Entropy (SE) is used to predict complexity of time series data. Recent research has proposed new calculation methods for SE, aiming to improve the accuracy. To my knowledge, no one has attempted to reduce the computational time of SE calculation. I have developed a new calculation method for time series complexity which could improve computational time significantly in the context of calculating a correlation between EEG and HRV. The results have a parsimonious outcome of SE calculation by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain. Time series analysis method has been utilised to study complex systems that appear ubiquitous in nature, but limited to certain dynamic systems (e.g. analysing variables affecting stock values). In this thesis, I have also investigated the nature of the dynamic system of HRV. I have disclosed that Embedding Dimension could unveil two variables that determined HRV

The EEG in acute ischaemic cerebrovascular disease

The electroencephalogram (EEG) is a neurophysiological technique with high temporal resolution and sensibility in the evaluation of brain function in real time. Besides this, EEG is the gold standard for the identification of epileptogenesis and ictogenesis biomarkers. Epileptic seizures and Cerebrovascular disease are two of the most frequent neurological disorders imposing important mutual challenges. Furthermore, in recent years, stroke care has evolved remarkably and, facing a new paradigm of acute standard of care (centred on multidisciplinary Stroke Units), epileptic seizures (as stroke complications) deserve to be rethought. The EEG is an essential neurophysiological exam in the evaluation of patients with epileptic seizures, status epilepticus and/or epilepsy, both for diagnosis and classification, as well as for the establishment of a correct treatment or outcome prediction. Furthermore, EEG has been previously used in cerebrovascular disease with different purposes. However, its clinical usefulness in the differential diagnosis of transient neurological symptoms, specifically in the differentiation between a transient ischaemic attack and some epileptic seizures, and also in the diagnosis or prediction of post-stroke seizures or in post-stroke prognosis prediction, remains uncertain. In this work, we aim to use the clinical model of acute ischaemic cerebrovascular disease to study the value of EEG in the differential diagnosis of transient neurological symptoms, in the diagnosis and prediction of post-stroke seizures and epilepsy, as well as to analyse if electroencephalographic abnormalities and/or epileptic seizures are independent predictors of an anterior circulation ischaemic stroke outcome. Furthermore, since the gold standard of acute stroke care (namely intravenous alteplase treatment) is associated with a reduction of mortality and incapacity of treated patients with possible consequences in post-stroke seizure frequency, but a pro-convulsive and an epileptogenic effect of alteplase has also been described, we aim to test the hypothesis that ischaemic stroke patients treated with intravenous alteplase have a different frequency of epileptic (clinic and/or electroencephalographic) manifestations compared to non-treated patients. Different research methodologies were used in this thesis. A systematic review and meta-analysis of observational studies was performed to evaluate both the frequency of post-stroke (ictal and interictal) epileptiform activity in the EEG, and the quality of studies about this subject. Furthermore, different types of observational studies (including clinical case report, case series and cohort studies) were completed to answer relevant clinical questions. We performed a prospective longitudinal study of possible transient ischaemic attacks (TIA) patients evaluated at a tertiary centre during 36 months, with 1-3 months follow-up and also of acute anterior circulation ischaemic stroke patients, consecutively admitted to a Stroke Unit over 24 months and followed-up for one year. In both studies, patients underwent standardized clinical, diagnostic and neurophysiological assessment. A short duration (≤60 minutes) video-EEG protocol with an extended montage including 64 EEG, two electrooculogram, one electrocardiogram and at least one electromyogram channel was established. Different electroencephalographic investigation technics including visual, back-average and quantitative analysis were used in the clinical workup of patients with possible and definite, transient and established, cerebrovascular disease as tools for the differential diagnosis and for brain functional assessment, concerning not only epileptic manifestations detection and prediction but also to search for predictors of ischaemic stroke functional outcome and vital prognosis. Although epileptic seizures were the most frequent defined final diagnosis (n=13; 16.3%) in our series of 80 patients with difficult-to-diagnose transient neurological symptoms, visual inspection of EEG supported this diagnosis only in 7.5% (n=6) of patients with possible TIA. Moreover, the majority (n=6; 53.8%) of patients with the final diagnosis of epileptic seizures did not have interictal epileptiform activity in an early EEG. Furthermore, early focal slow wave activity, the most frequent EEG abnormality in this patient’s series, did not distinguished between TIA and seizure patients. Our systematic review and random-effects meta-analysis showed that the pooled frequency of post-stroke ictal and interictal epileptiform activity was 7% (95%CI: 3%-12%) and 8% (95%CI: 4%-13%) respectively. Only 2 out of 17 included studies (11.7%) attained the maximum quality score. Moreover, no study exclusively enrolled ischaemic stroke patients, highlighting the need for higher quality studies in the evaluation of epileptiform activity frequency in this type of cerebrovascular disease. Furthermore, due to detection bias, it was not possible to correlate clinical and electrographic seizures. In our prospective cohort of 151 anterior circulation acute stroke patients, we identified different post-stroke, clinical and electroencephalographic, epileptic manifestations including 22.7% (5/22) of acute symptomatic seizures that were exclusively electrographic and therefore could not otherwise be recognised. Furthermore, only EEG back-average analysis allowed the diagnosis of cortical myoclonus during intravenous alteplase perfusion in one clinical vignette included in this work and the recognition of epilepsia partialis continua as a chronic complication of this stroke type in 1.7% of patients. This original work also showed that studied clinical and EEG epileptic manifestations were not significantly different between intravenous alteplase treated and non-treated patients. This thesis work established which abnormalities of an early EEG after acute stroke (background activity asymmetry and the presence of interictal epileptiform activity) are independent predictors of epilepsy in the year after (even when adjusted for clinical and imaging stroke severity). Besides this, early (within the first 72h) post-stroke EEG features, extracted from visual (background activity diffuse slowing and asymmetry) and quantitative (such as delta-theta to alpha-beta ratio and alfa relative power) analysis were recognized as independent predictors of death or functional dependency, at hospital discharge and at 12 months after stroke. Furthermore, outcome models that incorporate delta-theta to alpha-beta ratio or alpha relative power were better than models based exclusively on clinical and imaging-related ischaemic stroke severity at hospital admission. Additionally, post-stroke acute symptomatic seizures and epilepsy were independently associated to death and to an unfavourable outcome 1 year after an acute anterior circulation ischaemic stroke, respectively. Globally, these research projects have shown the value of EEG in the current paradigm of stroke patient’s care. Furthermore, they expand the knowledge both about the EEG role as a complementary neurophysiological tool in general Neurology and about different aspects of the diagnosis and outcome of two of the most prevalent neurological disorders, Cerebrovascular Diseases and Epilepsy, in particular. Beyond the value of specific results, with this work several other research questions about EEG and seizures in ischaemic cerebrovascular disease emerge. Therefore, new possibilities of future research, ideally multicentric, clinical or translational arise