Bioelectric signal analysis and measurement

Nonstationary time series techniques are used to analyze EEG signals for the estimation of alertness. A time varying order is extracted in sequential time series measurement of these data and strategies are devised for obtaining optimal representation of the EEG signal

Epileptogenesis in rodents leads to neural system dysfunction and loss of associative memory measured by auditory event related potentials.

Epilepsy is a common and disabling neurological condition affecting 1-2% of the world’s population. Individuals suffering from epilepsy are prone to cognitive dysfunctions with detrimental effects in neural processing and memory resulting in decreases in quality of life. An evaluation of inherent neural processes is valuable information to diagnose and clinically assess cognitive function, which could significantly improve the treatment possibilities and thereby the quality of life for epilepsy patients. An evaluation of cognitive functions during epileptogenesis was performed by experiments using auditory event related potentials (ERP) in rats before and after induction of status epilepticus (SE) using the Lithium-Pilocarpine model (LP) of epilepsy. The aim of this study was to assess changes in neural system function during epileptogenesis by evaluating inherent responses to auditory stimuli in three ERP tasks at different time periods: before SE (control state), one week-, one month- and two months- after SE (epileptic state). 1. Habituation- (a.) evaluate the ability to habituate to repeated auditory stimuli using the N70 peak response, (b.) examine the time-frequency response through inter-trial coherence (ITC) and event-related spectral perturbation (ERSP); 2. Chirp- evaluate the auditory steady state responses through ITC; and, 3. Mismatch-Negativity (MMN)- evaluate associative memory through ERP responses to regular or odd tones. Habituation tasks showed increased N70 peak magnitude during epileptogenesis from 1-week, 1-month, and 2-months after SE using repeated measures analysis of variance (rANOVA) with significant differences before and after SE (p\u3c0.05, 1-week, 2-months). ITC showed significant differences between groups during habituation from 0.5-20 Hz and ERSP from 60-100 Hz and 0.5-15 Hz, with baseline corrected ERSP revealing differences from 1-30 Hz. The habituation results indicate a diminished ability to properly habituate to repeated stimuli with abnormal neuronal firing in the epileptic state compared to the non-epileptic control state linking a possible mechanism with imbalances in neuronal inhibition and excitation during epileptogenesis. Chirp response ITC showed increased synchronous activity in high gamma band (\u3e40 Hz) during epileptogenesis indicating the neuronal response in epileptic groups are phase locked to the chirp stimuli at a higher incidence than controls. Epileptic MMN ERP responses for odd and regular tones exhibited a decrease in the response curves from 250-350ms post-stimulus indicating a loss of ability to distinguish tones and difficulties with their associative memory during epileptogenesis.Our results indicate that a proper EEG-based analysis of auditory ERPs are useful in evaluating neural systems during epileptogenesis showing clear imbalances in excitatory: inhibitory function, as well as an indication that associative memory is detrimentally affected. The ERP methods employed may help in the diagnosis of the epileptic patients with cognitive disabilities as their epilepsy progresses, as it is simple, non-invasive and cost effective

Machine Learning for Understanding Focal Epilepsy

The study of neural dysfunctions requires strong prior knowledge on brain physiology combined with expertise on data analysis, signal processing, and machine learning. One of the unsolved issues regarding epilepsy consists in the localization of pathological brain areas causing seizures. Nowadays the analysis of neural activity conducted with this goal still relies on visual inspection by clinicians and is therefore subjected to human error, possibly leading to negative surgical outcome. In absence of any evidence from standard clinical tests, medical experts resort to invasive electrophysiological recordings, such as stereoelectroencephalography to assess the pathological areas. This data is high dimensional, it could suffer from spatial and temporal correlation, as well as be affected by high variability across the population. These aspects make the automatization attempt extremely challenging. In this context, this thesis tackles the problem of characterizing drug resistant focal epilepsy. This work proposes methods to analyze the intracranial electrophysiological recordings during the interictal state, leveraging on the presurgical assessment of the pathological areas. The first contribution of the thesis consists in the design of a support tool for the identification of epileptic zones. This method relies on the multi-decomposition of the signal and similarity metrics. We built personalized models which share common usage of features across patients. The second main contribution aims at understanding if there are particular frequency bands related to the epileptic areas and if it is worthy to focus on shorter periods of time. Here we leverage on the post-surgical outcome deriving from the Engel classification. The last contribution focuses on the characterization of short patterns of activity at specific frequencies. We argue that this effort could be helpful in the clinical routine and at the same time provides useful insight for the understanding of focal epilepsy

Caracterización de señales electroencefalográficas utilizando la transformada wavelet discreta como herramienta para apoyar el diagnóstico del trastorno por déficit de atención e hiperactividad TDAH

Despite arising in childhood, attention deficit hyperactivity disorder (ADHD) can persist into adulthood, compromising the individual’s social skills. ADHD diagnosis is a real chal- lenge due to its dependence on the clinical observation of the patient, the information provided by parents and teachers, and the clinicians’ expertise..