Patient-specific seizure onset detection

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 121-124).Approximately one percent of the world's population exhibits symptoms of epilepsy, a serious disorder of the central nervous system that predisposes those affected to experiencing recurrent seizures. The risk of injury associated with epileptic seizures might be mitigated by the use of a device that can reliably detect or predict the onset of seizure episodes and then warn caregivers of the event. In a hospital this device could also be used to initiate time-sensitive clinical procedures necessary for characterizing epileptic syndromes. This thesis discusses the design of a real-time, patient-specific method that can be used to detect the onset of epileptic seizures in non-invasive EEG, and then initiate time-sensitive clinical procedures like ictal SPECT. We adopt a patient-specific approach because of the clinically observed consistency of seizure and non-seizure EEG characteristics within patients, and their great heterogeneity across patients. We also treat patient-specific seizure onset detection as a binary classification problem. Our observation is a multi-channel EEG signal; its features include amplitude, fundamental frequency, morphology, and spatial localization on the scalp; and it is classified as an instance of non-seizure or seizure EEG based on the learned features of training examples from a single patient. We use a multi-level wavelet decomposition to extract features that capture the amplitude, fundamental frequency, and morphology of EEG waveforms. These features are then classified using a support vector machine or maximum-likelihood classifier trained on a patient's seizure and non-seizure EEG; non-seizure EEG includes normal and artifact contaminated EEG from various states of consciousness.(cont.) The outcome of the classification is examined in the context of automatically extracted spatial and temporal constraints before the onset of seizure activity is declared. During validation tests our method exhibited an average latency of 8.0[plus-minus]3.2 seconds while correctly identifying 131 of 139 seizure events from thirty-six, de-identified test subjects; and only 11 false-detections over 49 hours of randomly selected non-seizure EEG from these subjects. The validation tests also highlight the high learning rate of the detector; a property that allows it to exhibit excellent performance even when trained on as few as two seizure events from the test subject. We also demonstrate through a comparative study that our patient-specific detector outperforms a nonpatient-specific, or generic detector in terms of a lower average detection latency; a lower total number of false-detections; and a higher total number of true-detections. Our study also underscores the likely event of a generic detector performing very poorly when the seizure EEG of a subject in its training set matches the non-seizure EEG of the test subject.by Ali Hossam Shoeb.M.Eng

Backtranslation of EEG biomarkers of Alzheimer's disease from patients to mouse model

The present Ph.D. thesis has been mainly developed on the data of the project with the short name PharmaCog (2010-2015), granted by the European Framework Programme 7 with about 28 millions of Euro (i.e. Innovative Medicine Initiative, IMI, grant agreement n°115009; www.pharmacog.org). This project involved 15 academic institutions, 12 global pharmaceutical companies, and 5 small and medium sized enterprises (SMEs). The PharmaCog project aimed at improving the pathway of drug discovery in Alzheimer’s disease (AD), based on a major interest of pharma companies, namely the validation of electrophysiological, neuroimaging, and blood biomarkers possibly sensitive to the effect of disease-modifying drugs reducing Ab42 in the brain in AD patients at the prodromal stage of amnesic mild cognitive impairment (aMCI). The core concept of the PharmaCog project was that the pathway of drug discovery in AD may be enhanced by (1) the validation of biomarkers derived from blood, EEG, magnetic resonance imaging (MRI), and positron emission tomography (PET) in patients with aMCI due to AD diagnosed by in-vivo measurement of Ab42 and phospho-tau in the brain and (2) the evaluation of the translational value of those human biomarkers in wild type (WT) mice and animal models of AD including transgenic mice with the mutation of PS1 and/or APP (i.e. PDAPP and TASTPM strains). Those genetic factors induce an abnormal accumulation of Ab42 in the brain and related cognitive deficits. The expected results may be (1) the identification of a matrix of biomarkers sensitive to the prodromal AD (aMCI cognitive status) and its progression in patients and (2) the selection of similar biomarkers related to AD neuropathology and cognitive deficits in PDAPP and TASTPM strains. These biomarkers were expected to be very useful in clinical trials testing the efficacy and neurobiological impact of new disease-modifying drugs against prodromal AD. For the development of this Ph.D. thesis, the access to the experiments and the data of the PharmaCog project was allowed by Prof. Claudio Babiloni, leader of an Italian Unit (University of Foggia in 2010-2012 and Sapienza University of Rome in 2013-2015) of the PharmaCog Consortium and coordinator of study activities relative to biomarkers derived from electroencephalographic (EEG) signals recorded from human subjects and animals in that project. Specifically, Prof. Claudio Babiloni was in charge for the centralized qualification and analysis of EEG data recorded from aMCI patients (Work Package 5, WP5) and transgenic mouse models of AD such as PDAPP and TASTPM strains (WP6). The data of the present Ph.D. thesis mostly derived from the WP5 and WP6. This document illustrating the Ph.D. thesis is structured in three main Sections: ▪ An Introductive part illustrating concisely the AD neuropathology, the mouse models of AD used in this thesis, and basic concepts of EEG techniques useful to understand the present study results; ▪ An Experimental part describing the result of the four research studies led in the framework of this Ph.D. project. Two of these studies were published in international journals registered in ISI/PubMed with impact factor, while the other two are being currently under minor revisions in those journals; ▪ A Conclusion section

Dynamics of large-scale brain activity in health and disease

Tese de doutoramento em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa através da Faculdade de Ciências, 2008Cognition relies on the integration of information processed in widely distributed brain regions. Neuronal oscillations are thought to play an important role in the supporting local and global coordination of neuronal activity. This study aimed at investigating the dynamics of the ongoing healthy brain activity and early changes observed in patients with Alzheimer's disease (AD). Electro- and magnetoencephalography (EEG/MEG) were used due to high temporal resolution of these techniques. In order to evaluate the functional connectivity in AD, a novel algorithm based on the concept of generalized synchronization was improved by defining the embedding parameters as a function of the frequency content of interest. The time-frequency synchronization likelihood (TF SL) revealed a loss of fronto-temporal/parietal interactions in the lower alpha (8 10 Hz) oscillations measured by MEG that was not found with classical coherence. Further, long-range temporal (auto-) correlations (LRTC) in ongoing oscillations were assessed with detrended fluctuation analysis (DFA) on times scales from 1 25 seconds. Significant auto-correlations indicate a dependence of the underlying dynamical processes at certain time scales of separation, which may be viewed as a form of "physiological memory". We tested whether the DFA index could be related to the decline in cognitive memory in AD. Indeed, a significant decrease in the DFA exponents was observed in the alpha band (6 13 Hz) over temporo-parietal regions in the patients compared with the age-matched healthy control subjects. Finally, the mean level of SL of EEG signals was found to be significantly decreased in the AD patients in the beta (13 30 Hz) and in the upper alpha (10 13 Hz) and the DFA exponents computed as a measure of the temporal structure of SL time series were larger for the patients than for subjects with subjective memory complaint. The results obtained indicate that the study of spatio-temporal dynamics of resting-state EEG/MEG brain activity provides valuable information about the AD pathophysiology, which potentially could be developed into clinically useful indices for assessing progression of AD or response to medication

Wearable Sensors to Evaluate Autonomic Response to Olfactory Stimulation: The Influence of Short, Intensive Sensory Training

In the last few decades, while the sensory evaluation of edible products has been leveraged to make strategic decisions about many domains, the traditional descriptive analysis performed by a skilled sensory panel has been seen to be too complex and time-consuming for the industry needs, making it largely unsustainable in most cases. In this context, the study of the effectiveness of different methods for sensory training on panel performances represents a new trend in research activity. With this purpose, wearable sensors are applied to study physiological signals (ECG and skin conductance) concerned with the emotions in a cohort of volunteers undergoing a short, two-day (16 h) sensory training period related to wine tasting. The results were compared with a previous study based on a conventional three-month (65 h) period of sensory training. According to what was previously reported for long panel training, it was seen that even short, intensive sensory training modulated the ANS activity toward a less sympathetically mediated response as soon as odorous compounds become familiar. A large-scale application of shorter formative courses in this domain appears possible without reducing the effectiveness of the training, thus leading to money saving for academia and scientific societies, and challenging dropout rates that might affect longer courses

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