Artifact Rejection Methodology Enables Continuous, Noninvasive Measurement of Gastric Myoelectric Activity in Ambulatory Subjects.

The increasing prevalence of functional and motility gastrointestinal (GI) disorders is at odds with bottlenecks in their diagnosis, treatment, and follow-up. Lack of noninvasive approaches means that only specialized centers can perform objective assessment procedures. Abnormal GI muscular activity, which is coordinated by electrical slow-waves, may play a key role in symptoms. As such, the electrogastrogram (EGG), a noninvasive means to continuously monitor gastric electrical activity, can be used to inform diagnoses over broader populations. However, it is seldom used due to technical issues: inconsistent results from single-channel measurements and signal artifacts that make interpretation difficult and limit prolonged monitoring. Here, we overcome these limitations with a wearable multi-channel system and artifact removal signal processing methods. Our approach yields an increase of 0.56 in the mean correlation coefficient between EGG and the clinical "gold standard", gastric manometry, across 11 subjects (p < 0.001). We also demonstrate this system's usage for ambulatory monitoring, which reveals myoelectric dynamics in response to meals akin to gastric emptying patterns and circadian-related oscillations. Our approach is noninvasive, easy to administer, and has promise to widen the scope of populations with GI disorders for which clinicians can screen patients, diagnose disorders, and refine treatments objectively

A hybrid unsupervised approach toward EEG epileptic spikes detection

Epileptic spikes are complementary sources of information in EEG to diagnose and localize the origin of epilepsy. However, not only is visual inspection of EEG labor intensive, time consuming, and prone to human error, but it also needs long-term training to acquire the level of skill required for identifying epileptic discharges. Therefore, computer-aided approaches were employed for the purpose of saving time and increasing the detection and source localization accuracy. One of the most important artifacts that may be confused as an epileptic spike, due to morphological resemblance, is eye blink. Only a few studies consider removal of this artifact prior to detection, and most of them used either visual inspection or computer-aided approaches, which need expert supervision. Consequently, in this paper, an unsupervised and EEG-based system with embedded eye blink artifact remover is developed to detect epileptic spikes. The proposed system includes three stages: eye blink artifact removal, feature extraction, and classification. Wavelet transform was employed for both artifact removal and feature extraction steps, and adaptive neuro-fuzzy inference system for classification purpose. The proposed method is verified using a publicly available EEG dataset. The results show the efficiency of this algorithm in detecting epileptic spikes using low-resolution EEG with least computational complexity, highest sensitivity, and lesser human interaction compared to similar studies. Moreover, since epileptic spike detection is a vital component of epilepsy source localization, therefore this algorithm can be utilized for EEG-based pre-surgical evaluation of epilepsy

Alterations in electrodermal activity and cardiac parasympathetic tone during hypnosis

Exploring autonomic nervous system (ANS) changes during hypnosis is critical for understanding the nature and extent of the hypnotic phenomenon and for identifying the mechanisms underlying the effects of hypnosis in different medical conditions. To assess ANS changes during hypnosis, electrodermal activity and pulse rate variability (PRV) were measured in 121 young adults. Participants either received hypnotic induction (hypnosis condition) or listened to music (control condition), and both groups were exposed to test suggestions. Blocks of silence and experimental sound stimuli were presented at baseline, after induction, and after de-induction. Skin conductance level (SCL) and high frequency (HF) power of PRV measured at each phase were compared between groups. Hypnosis decreased SCL compared to the control condition; however, there were no group differences in HF power. Furthermore, hypnotic suggestibility did not moderate ANS changes in the hypnosis group. These findings indicate that hypnosis reduces tonic sympathetic nervous system activity, which might explain why hypnosis is effective in the treatment of disorders with strong sympathetic nervous system involvement, such as rheumatoid arthritis, hot flashes, hypertension, and chronic pain. Further studies with different control conditions are required to examine the specificity of the sympathetic effects of hypnosis

Development and Characterization of Ear-EEG for Real-Life Brain-Monitoring

Functional brain monitoring methods for neuroscience and medical diagnostics have until recently been limited to laboratory settings. However, there is a great potential for studying the human brain in the everyday life, with measurements performed in more realistic real-life settings. Electroencephalography (EEG) can be measured in real-life using wearable EEG equipment. Current wearable EEG devices are typically based on scalp electrodes, causing the devices to be visible and often uncomfortable to wear for long-term recordings. Ear-EEG is a method where EEG is recorded from electrodes placed in the ear. The Ear-EEG supports non-invasive long-term recordings of EEG in real-life in a discreet way. This Ph.D. project concerns the characterization and development of ear-EEG for real-life brain-monitoring. This was addressed through characterization of physiological artifacts in real-life settings, development and characterization of dry-contact electrodes for real-life ear-EEG acquisition, measurements of ear-EEG in real-life, and development of a method for mapping cortical sources to the ear. Characterization of physiological artifacts showed a similar artifact level for recordings from ear electrodes and temporal lobe scalp electrodes. Dry-contact electrodes and flexible earpieces were developed to increase the comfort and user-friendliness of the ear-EEG. In addition, electronic instrumentation was developed to allow implementation in a hearing-aid-sized ear-EEG device. Ear-EEG measurements performed in real-life settings with the dry-contact electrodes, were comparable to temporal lobe scalp EEG, when referenced to a Cz scalp electrode. However, the recordings showed that further development of the earpieces and electrodes are needed to obtain a satisfying recording quality, when the reference is located close to or in the ear. Mapping of the electric fields from well-defined cortical sources to the ear, showed good agreement with previous ear-EEG studies and has the potential to provide valuable information for future development of the ear-EEG method. The Ph.D. project showed that ear-EEG measurements can be performed in real-life, with dry-contact electrodes. The brain processes studied, were established with comparable clarity on recordings from temporal lobe scalp and ear electrodes. With further development of the earpieces, electrodes, and electronic instrumentation, it appears to be realistic to implement ear-EEG into unobtrusive and user-friendly devices for monitoring of human brain processes in real-life

Correction of blink artifacts using independent component analysis and empirical mode decomposition.

Blink-related ocular activity is a major source of artifacts in electroencephalogram (EEG) data. Independent component analysis (ICA) is a well-known technique for the correction of such ocular artifacts, but one of the limitations of ICA is that the ICs selected for removal contain not only ocular activity but also some EEG activity. Straightforward removal of these ICs might, therefore, lead to a loss of EEG data. In this article a method is proposed to separate blink-related ocular activity from actual EEG by combining ICA with a novel technique, empirical mode decomposition. This combination of two techniques allows for maximizing the retention of EEG data and the selective removal of the eyeblink artifact. The performance of the proposed method is demonstrated with simulated and real data

Detection of Human Vigilance State During Locomotion Using Wearable FNIRS

Human vigilance is a cognitive function that requires sustained attention toward change in the environment. Human vigilance detection is a widely investigated topic which can be accomplished by various approaches. Most studies have focused on stationary vigilance detection due to the high effect of interference such as motion artifacts which are prominent in common movements such as walking. Functional Near-Infrared Spectroscopy is a preferred modality in vigilance detection due to the safe nature, the low cost and ease of implementation. fNIRS is not immune to motion artifact interference, and therefore human vigilance detection performance would be severely degraded when studied during locomotion. Properly treating and removing walking-induced motion artifacts from the contaminated signals is crucial to ensure accurate vigilance detection. This study compared the vigilance level detection during both stationary and walking states and confirmed that the performance of vigilance level detection during walking is significantly deteriorated (with a

Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis

The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. This paper proposes two robust methods: i) Wavelet packet decomposition (WPD), and ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: i) Difference in the signal to noise ratio ({\Delta}SNR) and ii) Percentage reduction in motion artifacts ({\eta}). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average {\Delta}SNR (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average {\eta} (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique i.e. the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average {\Delta}SNR and {\eta} values of 30.76 dB and 59.51%, respectively for all the EEG recordings. On the other hand, the two-stage motion artifacts removal technique i.e. WPD-CCA has produced the best average {\Delta}SNR (16.55 dB, utilizing db1 wavelet packet) and largest average {\eta} (41.40%, using fk8 wavelet packet). The highest average {\Delta}SNR and {\eta} using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed.Comment: 25 pages, 10 figures and 2 table

Cerebral language networks and neuropsychological profile in children with frontotemporal lobe epilepsy : a multimodal neuroimaging and neuropsychological approach

Thèse de doctorat présentée en vue de l'obtention du doctorat en psychologie (Ph.D).L'enfance et l'adolescence sont des périodes uniques de la vie où les changements neuronaux favorisent l'établissement de réseaux cérébraux matures et le développement des capacités intellectuelles. Le langage est un domaine cognitif qui est, non seulement essentiel pour la communication interhumaine, mais qui contribue également au développement de nombreuse capacités et prédit de manière significative la réussite académique. Les régions cérébrales frontotemporales sont des régions clés du réseau langagier du cerveau. Il a été démontré que les neuropathologies telles que l'épilepsie des lobes frontal et temporal (ELF et ELT) interfèrent avec le développement des réseaux cérébraux du langage et provoquent des circuits cérébraux aberrants. Les patrons exacts de réorganisation des réseaux cérébraux fonctionnels ne sont toutefois, pas entièrement compris et l'association avec le profil neuropsychologique reste spéculative. Par conséquent, l'objectif principal de cette thèse est d'accroître la compréhension des altérations du réseau langagier et d'améliorer les connaissances de l'association de l'architecture du réseau et des capacités cognitives chez les enfants et les adolescents avec ELF ou ELT. La présente thèse est composée de trois articles scientifiques, les deux premiers présentant des travaux méthodologiques qui ont permis d'optimiser les méthodes appliquées dans le troisième article, l'étude empirique principale menée auprès d'enfants avec ELF et ELT. Le premier article présente le bilan neuropsychologique pédiatrique comme un outil important pour estimer les capacités cognitives et dresser un profil cognitif avec ses forces et ses faiblesses. Dans le deuxième article, l'analyse factorielle parallèle (PARAFAC) est présentée et validée comme une nouvelle technique employée pour corriger les artefacts de mouvement qui contaminent le signal hémodynamique évalué par la spectroscopie fonctionnelle proche infrarouge (fNIRS). Une meilleure qualité du signal permet une interprétation fiable de la réponse cérébrale en plis de déduire des métriques d'organisation du réseau cérébral. Le troisième article consiste en une étude empirique, où le traitement cérébral du langage, est comparé entre des enfants avec ELF et ELT, et des pairs neuroptypiques. Les schémas de connectivité fonctionnelle indiquent que le groupe de patients présente moins de connexions intra-hémisphériques dans l'hémisphère gauche et entre les hémisphères, et des connexions accrues dans l'hémisphère droit par rapport au groupe témoin. Les mesures de l'architecture du réseau révèlent en outre une efficacité de traitement local plus élevée dans l'hémisphère droit chez les enfants atteints de ELF et ELT par rapport aux enfants en bonne santé. L'architecture du réseau local de l'hémisphère gauche et la capacité intellectuelle globale dans le groupe de patients sont négativement liées, tandis que dans le groupe contrôle, aucune association de ce type n'est identifiable. Ces résultats suggèrent que la réorganisation du réseau de langage chez les enfants avec ELF ou ELT semble dans certains cas soutenir un meilleur résultat cognitif, soit lorsque l'efficacité du traitement local dans l'hémisphère gauche est diminuée. Au contraire, une plus grande efficacité de traitement local semble être une caractéristique d'un réseau de langage cérébral associé à de moins bonnes capacités cognitives. Les travaux de recherche de cette thèse de doctorat fournissent des lignes directrices pour l'utilisation de l'évaluation neuropsychologique pédiatrique, à la fois dans un contexte clinique et scientifique. L'introduction de PARAFAC pour corriger les artefacts de mouvement dans le signal fNIRS est un ajout important au pipeline de prétraitement qui permet d'augmenter la qualité du signal pour une analyse ultérieure. De futurs projets pourront s'appuyer sur cette validation initiale et étendre l'utilisation de PARAFAC pour les analyses du signal fNIRS. Sur cette base méthodologique solide, le travail empirique confirme l'incidence accrue de circuits cérébraux aberrants liés au traitement du langage chez les enfants atteints de ELF et de ELT, et soutient en outre l'efficacité du réseau local en tant que déterminant clé de l'impact de la plasticité cérébrale précoce sur les capacités cognitives. Afin de mieux comprendre les altérations du réseau en réponse aux neuropathologies et leur impact, des études avec des échantillons plus grands et de différents groupes d'âge, devraient étudier plus spécifiquement le rôle des facteurs cliniques (e.g., le type d'épilepsie, la latéralisation de l'épilepsie, le contrôle des crises, etc.) et aborder leurs influences sur le développement. À long terme, cela augmentera le pronostic des phénotypes cliniques chez les patients pédiatriques atteints de ELF et de ELT, et offrira des opportunités d'interventions précoces pour soutenir un développement typique.Childhood and adolescence are unique periods in life where neuronal changes support the establishment of mature brain networks and the development of intellectual capacities. Language is one cognitive domain that is not only an essential part of inter-human communication but also contributes to the development of other capacities and significantly influences academic achievement. Frontotemporal brain areas are key regions of the brain's language network. Neuropathologies such as frontal and temporal lobe epilepsies (FLE and TLE) have been shown to interfere with developing brain language networks and cause aberrant cerebral circuits. The exact patterns of functional brain network reorganization are not fully understood and the association with the neuropsychological profile remains speculative. Therefore, the main objective of this thesis was to increase comprehension of language network alterations and enhance the knowledge on the association of network topology and cognitive capacities in children and adolescents with FLE or TLE. This thesis consists of three scientific articles, with the first two presenting methodological work that allowed for the optimization of the methods applied in the third article, which is the main empirical study conducted on children with FLE and TLE. The first article presents the pediatric neuropsychological assessment as a valuable tool to estimate cognitive capacities and draw a cognitive profile with strengths and weaknesses. In the second article, parallel factor analysis (PARAFAC) is presented and validated as a novel technique to correct motion artifacts that contaminate the hemodynamic signal assessed with functional near-infrared spectroscopy (fNIRS). A better signal quality is the basis for a reliable interpretation of the cerebral response and derive metrics of brain network organization. The third article consists of an empirical study where cerebral language processing is compared between children with FLE and TLE, and neuroptypical peers. Patterns of functional connectivity indicate that the patient group demonstrates fewer intra-hemispheric connections in the left hemisphere and between hemispheres, and increased connections within the right hemisphere as compared to the control group. Metrics of network architecture further reveal a higher local processing efficiency within the right hemisphere in children with FLE and TLE compared to healthy peers. Local network architecture of the left hemisphere and the overall intellectual capacity in the patient group is negatively related, while in the control group no such association is identifiable. These findings suggest that language network reorganization in children with FLE or TLE in some cases seems to support a better cognitive outcome, namely when local processing efficiency in the left hemisphere is decreased. On the contrary, a higher local processing efficiency seems to be a characteristic of a brain language network that goes along with worse cognitive capacities. The research work of this doctoral thesis provides guidelines for the use of pediatric neuropsychological assessment both in a clinical and scientific context. The introduction of PARAFAC to correct motion artifact in the fNIRS signal is an important add-on to the preprocessing pipeline that allows to increase signal quality for subsequent analysis. Future projects will be able to build on this initial validation and extend PARAFAC's use for fNIRS analysis. On this solid methodological foundation, the empirical work confirms the increased incidence of aberrant brain circuits related to language processing in children with FLE and TLE, and further supports local network efficiency as a key determinant of the impact of early brain plasticity on cognitive capacities. In order to further understand network alterations in response to neuropathologies and their impact, studies with larger samples sizes and different age groups should further investigate the specific role of clinical factors (e.g., epilepsy type, epilepsy lateralization, seizure control, etc.) and address developmental influences. Ultimately, this will increase prognosis of clinical phenotypes in pediatric patients with FLE and TLE, and offer opportunities for early interventions to support a healthy development