Mining Biomarkers Of Epilepsy From Large-Scale Intracranial Electroencephalography

Epilepsy is a chronic neurological disorder characterized by seizures. Affecting over 50 million people worldwide, the quality of life of a patient with uncontrolled epilepsy is degraded by medical, social, cognitive, and psychological dysfunction. Fortunately, two-thirds of these patients can achieve adequate seizure control through medications. Unfortunately, one-third cannot. Improving treatment for this patient population depends upon improving our understanding of the underlying epileptic network. Clinical therapies modulate this network to some degree of success, including surgery to remove the seizure onset zone or neuromodulation to alter the brain\u27s dynamics. High resolution intracranial EEG (iEEG) is often employed to study the dynamics of cortical networks, from interictal patterns to more complex quantitative features. These interictal patterns include epileptiform biomarkers whose detection and mapping, along with seizures and neuroimaging, form the mainstay of data for clinical decision making around drug therapy, surgery, and devices. They are also increasingly important to assess the effects of epileptic physiology on brain functions like behavior and cognition, which are not well characterized. In this work, we investigate the significance and trends of epileptiform biomarkers in animal and human models of epilepsy. We develop reliable methods to quantify interictal patterns, applying state of the art techniques from machine learning, signal processing, and EEG analysis. We then validate these tools in three major applications: 1. We study the effect of interictal spikes on human cognition, 2. We assess trends of interictal epileptiform bursts and their relationship to seizures in prolonged recordings from canines and rats, and 3. We assess the stability of long-term iEEG spanning several years. These findings have two main impacts: (1) they inform the interpretation of interictal iEEG patterns and elucidate the timescale of post-implantation changes. These findings have important implications for research and clinical care, particularly implantable devices and evaluating patients for epilepsy surgery. (2) They provide an analytical framework to enable others to mine large-scale iEEG datasets. In this way we hope to make a lasting contribution to accelerate collaborative research not only in epilepsy, but also in the study of animal and human electrophysiology in acute and chronic conditions

UNSUPERVISED CLASSIFICATION OF HIGH-FREQUENCY OSCILLATIONS IN NEOCORTICAL EPILEPSY AND CONTROL PATIENTS

Quality of life for the more than 15 million people with drug-resistant epilepsy is tied to how precisely the brain areas responsible for generating their seizures can be localized. High-frequency (100-500 Hz) field-potential oscillations (HFOs) are emerging as a candidate biomarker for epileptogenic networks, but quantitative HFO studies are hampered by selection bias arising out of the need to reduce large volumes of data in the absence of capable automated processing methods. In this thesis, I introduce and evaluate an algorithm for the automatic detection and classification of HFOs that can be deployed without human intervention across long, continuous data records from large numbers of patients. I then use the algorithm in analyzing unique macro- and microelectrode intracranial electroencephalographic recordings from human neocortical epilepsy patients and controls. A central finding is that one class of HFOs discovered by the algorithm (median bandpassed spectral centroid ~140 Hz) is more prevalent in the seizure onset zone than outside. The outcomes of this work add to our understanding of epileptogenic networks and are suitable for near-term translation into improved surgical and device-based treatments

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

脳波信号解析に注目したノイズ除去、特徴抽出、実験観測応用を最適化する数理基盤に関する研究

Electroencephalography (EEG) data inevitably contains a large amount of noise particularly from ocular potentials in tasks with eye-movements and eye-blink, known as electrooculography (EOG) artifact, which has been a crucial issue in the braincomputer- interface (BCI) study. The eye-movements and eye-blinks have different time-frequency properties mixing together in EEGs of interest. This time-frequency characteristic has been substantially dealt with past proposed denoising algorithms relying on the consistent assumption based on the single noise component model. However, the traditional model is not simply applicable for biomedical signals consist of multiple signal components, such as weak EEG signals easily recognized as a noise because of the signal amplitude with respect to the EOG signal. In consideration of the realistic signal contamination, we newly designed the EEG-EOG signal contamination model for quantitative validations of the artifact removal from EEGs, and then proposed the two-stage wavelet shrinkage method with the undecimated wavelet decomposition (UDWT), which is suitable for the signal structure. The features of EEG-EOG signal has been extracted with existing decomposition methods known as Principal Component Analysis (PCA), Independent Component Analysis (ICA) based on a consistent assumption of the orthogonality of signal vectors or statistical independence of signal components. In the viewpoint of the signal morphology such as spiking, waves and signal pattern transitions, A systematic decomposition method is proposed to identify the type of signal components or morphology on the basis of sparsity in time-frequency domain. Morphological Component Analysis (MCA) is extended the traditional concept of signal decomposition including Fourier and wavelet transforms and provided a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases being independent of each other and uniqueness representation, called the concept of “dictionary”. MCA is applied to decompose the real EEG signal and clarified the best combination of dictionaries for the purpose. In this proposed semi-realistic biological signal analysis, target EEG data was prepared as mixture signals of artificial eye movements and blinks and iEEG recorded from electrodes embedded into the brain intracranially and then those signals were successfully decomposed into original types by a linear expansion of waveforms such as redundant transforms: UDWT, DCT,LDCT, DST and DIRAC. The result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST and DIRAC to represent the baseline envelop, multi frequency wave forms and spiking activities individually as representative types of EEG morphologies. MCA proposed method is used in negative-going Bereitschaftspotential (BP). It is associated with the preparation and execution of voluntary movement. Thus far, the BP for simple movements involving either the upper or lower body segment has been studied. However, the BP has not yet been recorded during sit-to-stand movements, which use the upper and lower body segments. Electroencephalograms were recorded during movement. To detect the movement of the upper body segment, a gyro sensor was placed on the back, and to detect the movement of the lower body segment, an electromyogram (EMG) electrode was placed on the surface of the hamstrings and quadriceps. Our study revealed that a negative-going BP was evoked around -3 to -2 seconds before the onset of the upper body movement in the sit-to-stand movement in response to the start cue. The BP had a negative peak before the onset of the movement. The potential was followed by premotor positivity, a motor-related potential, and a reafferent potential. The BP for the sit-to-stand movement had a steeper negative slope (-0.8 to -0.001 seconds) just before the onset of the upper body movement. The slope correlated with the gyro peak and the max amplitude of hamstrings EMG. A BP negative peak value was correlated with the max amplitude of the hamstring EMG. These results suggested that the observed BP is involved in the preparation/execution for a sit-to-stand movement using the upper and lower body. In summary, this thesis is help to pave the practical approach of real time analysis of desired EEG signal of interest toward the implementation of rehabilitation device which may be used for motor disabled people. We also pointed out the EEG-EOG contamination model that helps in removal of the artifacts and explicit dictionaries are representing the EEG morphologies.九州工業大学博士学位論文 学位記番号:生工博甲第290号 学位授与年月日:平成29年3月24日1 Introduction|2 Research Background and Preliminaries|3 Introduction of Morphological Component Analysis|4 Two-Stage Undecimated Wavelet Shrinkage Method|5 Morphologically Decomposition of EEG Signals|6 Bereitschaftspotential for Rise to Stand-Up Behavior九州工業大学平成28年

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

Improvements in Neonatal Brain Monitoring after Perinatal Asphyxia

Perinatal hypoxic ischemic encephalopathy (HIE) is a major cause of morbidity and mortality world-wide. Common sequelae in survivors include cerebral palsy (CP), epilepsy and sensory as well as cognitive problems. The consequences of HIE impose significant long-term personal and financial burden on the affected families and the society. The most cost-effective approach to reducing neonatal mortality world-wide would be to improve access to antenatal care4. However, even in developed countries, the exact factors triggering perinatal asphyxia as well as the time of onset of brain injury are often difficult to determine, and it remains a major clinical problem. Seizures commonly occur in the neonate with HIE and are often the only sign of serious underlying brain dysfunction6. Animal studies have shown that neonatal seizures in the context of HIE may cause additional brain injury and that their pharmacological suppression may improve outcome9. Monitoring of brain function using the electroencephalogram (EEG), continuously or by serial EEGs is well-suited to give insight into brain function and its dynamic changes in neonatal HIE and helps to guide treatment as well as prognostication. A good understanding of the pathophysiology of HIE is needed not only in the selection of suitable diagnostic tests and treatment methods, but also to develop new therapeutic strategies

Temporal Characteristics of High-Frequency Oscillations as a Biomarker of Human Epilepsy

Epilepsy is a debilitating neurological disorder characterized by recurrent spontaneous seizures. While seizures themselves adversely affect physiological function for short time periods relative to normal brain states, their cumulative impact can significantly decrease patient quality of life in myriad ways. For many, anti-epileptic drugs are effective first-line therapies. One third of all patients do not respond to chemical intervention, however, and require invasive resective surgery to remove epileptic tissue. While this is still the most effective last-line treatment, many patients with ‘refractory’ epilepsy still experience seizures afterward, while some are not even surgical candidates. Thus, a significant portion of patients lack further recourse to manage their seizures – which additionally impacts their quality of life. High-frequency oscillations (HFOs) are a recently discovered electrical biomarker with significant clinical potential in refractory human epilepsy. As a spatial biomarker, HFOs occur more frequently in epileptic tissue, and surgical removal of areas with high HFO rates can result in improved outcomes. There is also limited preliminary evidence that HFOs change prior to seizures, though it is currently unknown if HFOs function as temporal biomarkers of epilepsy and imminent seizure onset. No such temporal biomarker has ever been identified, though if it were to exist, it could be exploited in online seizure prediction algorithms. If these algorithms were clinically implemented in implantable neuromodulatory devices, improvements to quality of life for refractory epilepsy patients might be possible. Thus, the overall aim of this work is to investigate HFOs as potential temporal biomarkers of seizures and epilepsy, and further to determine whether their time-varying properties can be exploited in seizure prediction. In the first study we explore population-level evidence for the existence of this temporal effect in a large clinical cohort with refractory epilepsy. Using sophisticated automated HFO detection and big-data processing techniques, a continuous measure of HFO rates was developed to explore gradual changes in HFO rates prior to seizures, which were analyzed in aggregate to assess their stereotypical response. These methods resulted in the identification of a subset of patients in whom HFOs from epileptic tissue gradually increased before seizures. In the second study, we use machine learning techniques to investigate temporal changes in HFO rates within individuals, and to assess their potential usefulness in patient-specific seizure prediction. Here, we identified a subset of patients whose predictive models sufficiently differentiated the preictal (before seizure) state better than random chance. In the third study, we extend our prediction framework to include the signal properties of HFOs. We explore their ability to improve the identification of preictal periods, and additionally translate their predictive models into a proof-of-concept seizure warning system. For some patients, positive results from this demonstration show that seizure prediction using HFOs could be possible. These studies overall provide convincing evidence that HFOs can change in measurable ways prior to seizure start. While this effect was not significant in some individuals, for many it enabled seizures to be predicted above random chance. Due to data limitations in overall recording duration and number of seizures captured, these findings require further validation with much larger high-density intracranial EEG datasets. Still, they provide a preliminary framework for the eventual use of HFOs in patient-specific seizure prediction with the potential to improve the lives of those with refractory epilepsy.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168079/1/jaredmsc_1.pd

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Development of tools and paradigms to assess brain cortical activity during cognitive tasks

Monitoring brain cortical activity is essential to decipher and understand neurophysiological behaviour. A wide amount of tools and experimental setups has been developed to stimulate, record and analyze brain activity. The identification of quantitative metrics to assess this activity during specific tasks remains an essential requirement, as it could lead to improve diagnostics, describe objectively self-assessed condition, or track variation during long-term studies. This thesis introduces the development of tools and paradigms to assess brain cortical activity during cognitive tasks. It introduces a complete set of analyses based on EEG signals, under two main scopes, schizophrenia and postural control. The first part of the work evaluates the impact of a potential therapeutic solution for patients with schizophrenia. A longitudinal study case is introduced, where psychometrics data are compared with three types of analysis from EEG data: temporal, spectral and connectivity. The small sample size prevents us to draw definitive conclusion, however, this work reveals the interest to use EEG-based metrics to complete the standard psychometrics assessment. The second part of the work focuses on postural control, using a novel measurement setup, called BioVRSea, combining virtual reality and a moving platform. The brain cortical activity of more than 150 healthy individuals have been investigated during this experiment. A robust neurophysiological reference has been identified using power spectral density. Moreover, combining brain connectivity and microstate segmentation, network dynamics reveal a coherent brain remodeling throughout the acquisition, strengthening our current knowledge regarding complex postural control. The current work highlights the concrete benefit of using EEG signal to decipher brain cortical activity. The tools developed in this thesis are of interest to build a neurophysiological signature of specific cognitive tasks, that will be crucial for a further understanding of neurodegenerative disease.Að fylgjast með starfsemi heilaberki er nauðsynlegt til að útskýra og skilja tauga-lífeðlisfræðilega hegðun. Fjölbreytt útval af tólum og tilraunauppsetningum hefur verið þróað til að örva, vista og greina heilastarfsemi. Það er nauðsynleg krafa að finna magnmælingu til að greina þessa starfsemi í ákveðnu verkefni, því það gæti leitt að beturumbættu greiningarferli, útskýrt hlutlægu sjálfsmats ástandi, eða fylgst með breytingum í lang-tíma rannsóknum. Þessi ritgerð kynnir þróunn tóla og hugmyndafræði til að meta heilaberka starfsemi við vitræn verkefni. Ritgerðinn kynnir heilt safn af greiningum byggt á EEG merkj- um, í tvem megin sviðum, geðklofa og líkamsstöðustjórnun. Fyrsti hluti verkefnisins metur áhrifin af mögulegum meðferðarlegum lausnum fyrir sjúklinga með geðklofa. Kynnt er langtímarannsóknartilvik, þar sem þar sem sálfræðigögn eru borin saman við þrenns konar greiningar úr heilarita gögnum: tímabundnum, litrófs- og tengingum. Lítil úrtaksstærð kemur í veg fyrir að við getum dregið endanlegar ályktanir, en þessi vinna sýnir áhugann á því að nota heilalínuritaða mælikvarða til að ljúka stöðluðu sálfræðimati. Annar hluti verksins fjallar um líkamsstöðustýringu, með því að nota nýja mæling- aruppsetningu, sem kallast BioVRSea, sem sameinar sýndarveruleika og hreyfanlegan vettvang. Heilabarkarvirkni af meira en 150 heilbrigðra einstaklinga hefur verið rann- sökuð í þessari tilraun. Öflug taugalífeðlisfræðileg tilvísun hefur fundist með því að nota kraftrófsþéttleika. Þar að auki, með því að sameina heilatengingu og örstöðu- skiptingu, sýnir netverkun samfellda endurgerð heilans í gegnum tökuna, sem styrkir núverandi þekkingu okkar varðandi flókna líkamsstöðustjórnun. Þessi rannsókn undirstrikar raunverulegan ávinning af því að nota EEG merki til að ráða virkni heilabarka. Tólin sem þróuð eru í þessari ritgerð eru mikilvæg til að byggja upp taugalífeðlisfræðilega undirskrift ákveðinna vitræna verkefna, sem mikilvæg eru fyrir frekari skilning á taugahrörnunarsjúkdómum

Libro de actas. XXXV Congreso Anual de la Sociedad Española de Ingeniería Biomédica

596 p.CASEIB2017 vuelve a ser el foro de referencia a nivel nacional para el intercambio científico de conocimiento, experiencias y promoción de la I D i en Ingeniería Biomédica. Un punto de encuentro de científicos, profesionales de la industria, ingenieros biomédicos y profesionales clínicos interesados en las últimas novedades en investigación, educación y aplicación industrial y clínica de la ingeniería biomédica. En la presente edición, más de 160 trabajos de alto nivel científico serán presentados en áreas relevantes de la ingeniería biomédica, tales como: procesado de señal e imagen, instrumentación biomédica, telemedicina, modelado de sistemas biomédicos, sistemas inteligentes y sensores, robótica, planificación y simulación quirúrgica, biofotónica y biomateriales. Cabe destacar las sesiones dedicadas a la competición por el Premio José María Ferrero Corral, y la sesión de competición de alumnos de Grado en Ingeniería biomédica, que persiguen fomentar la participación de jóvenes estudiantes e investigadores