7 research outputs found

    Informativeness of sleep cycle features in Bayesian assessment of newborn electroencephalographic maturation

    Get PDF
    Clinical experts assess the newborn brain development by analyzing and interpreting maturity-related features in sleep EEGs. Typically, these features widely vary during the sleep hours, and their informativeness can be different in different sleep stages. Normally, the level of muscle and electrode artifacts during the active sleep stage is higher than that during the quiet sleep that could reduce the informative-ness of features extracted from the active stage. In this paper, we use the methodology of Bayesian averaging over Decision Trees (DTs) to assess the newborn brain maturity and explore the informativeness of EEG features extracted from different sleep stages. This methodology has been shown providing the most accurate inference and estimates of uncertainty, while the use of DT models enables to find the EEG features most important for the brain maturity assessment

    Classification of newborn EEG maturity with Bayesian averaging over decision trees

    Get PDF
    EEG experts can assess a newborn’s brain maturity by visual analysis of age-related patterns in sleep EEG. It is highly desirable to make the results of assessment most accurate and reliable. However, the expert analysis is limited in capability to provide the estimate of uncertainty in assessments. Bayesian inference has been shown providing the most accurate estimates of uncertainty by using Markov Chain Monte Carlo (MCMC) integration over the posterior distribution. The use of MCMC enables to approximate the desired distribution by sampling the areas of interests in which the density of distribution is high. In practice, the posterior distribution can be multimodal, and so that the existing MCMC techniques cannot provide the proportional sampling from the areas of interest. The lack of prior information makes MCMC integration more difficult when a model parameter space is large and cannot be explored in detail within a reasonable time. In particular, the lack of information about EEG feature importance can affect the results of Bayesian assessment of EEG maturity. In this paper we explore how the posterior information about EEG feature importance can be used to reduce a negative influence of disproportional sampling on the results of Bayesian assessment. We found that the MCMC integration tends to oversample the areas in which a model parameter space includes one or more features, the importance of which counted in terms of their posterior use is low. Using this finding, we proposed to cure the results of MCMC integration and then described the results of testing the proposed method on a set of sleep EEG recordings

    Spontaanien aktiviteettipurskeiden automaattinen tunnistus keskosten aivosähkökäyrästä

    Get PDF
    Very preterm infants may require neonatal intensive care for several months, and the developmental outcome of the care depends on how well brain function can be managed. Direct monitoring of brain function with electroencephalography (EEG) is currently not a part of routine care, since it is perceived challenging due to difficulties in its interpretation. Therefore, automated methods for EEG interpretation are needed in order to make brain monitoring part of the routine in neonatal intensive care. This thesis investigates the detection of spontaneous activity transients (SATs), which form the majority of brain activity in preterm infants. Using manual markings by three doctors in 18 short recordings of preterm EEG, I show that SATs can be recognized by doctors in a consistent manner. A commercially available algorithm is then tested for its ability to detect SATs automatically. The performance of the algorithm is clearly insufficient and therefore it is developed further. The parameters of the new, streamlined algorithm are optimized using unanimous markings by the three doctors as a gold standard. Estimates for the performance of the algorithm on unseen data are obtained by running the optimization 18 times, each time leaving out one of the recordings. The algorithm is then run on the EEG left out from the optimization using the optimized parameters. The estimated performance of the algorithm is found to be excellent, with sensitivity of 96.6 +- 2.8 % and specificity of 95.1 +- 5.6 %. Segmentation of the EEG into SATs and periods between SATs is a starting point for further analysis. One promising direction for future studies is to use SAT%, the proportion of time covered by SATs, to detect cycles of different vigilance stages in preterm infants. Such cyclicity could become a marker of the brain's wellbeing. The algorithm presented in this thesis may contribute to better care of preterm infants.Erittäin ennenaikaisesti syntyneet keskoset saattavat tarvita teho-osastohoitoa jopa kuukausien ajan. Hoidon vaikutus lapsen kehitykseen riippuu paljon siitä, kuinka hyvin aivojen hoito onnistuu. Aivojen toiminnan jatkuva valvonta elektroenkefalografian (EEG) avulla ei vielä kuulu tavanomaiseen hoitokäytäntöön, koska EEG:n tulkintaa pidetään vaikeana. EEG:n tulkintaan tarvitaankin automaattisia menetelmiä, jotta aivojen tarkkailusta tulisi osa vastasyntyneiden tehohoidon rutiinia. Tässä työssä tutkitaan spontaanien aktiviteettipurskeiden tunnistamista (engl. spontaneous activity transient, SAT). Keskosten aivotoiminta muodostuu suurelta osin aktiviteettipurskeista. Käyttämällä kolmen lääkärin käsin tehtyjä merkintöjä aktiviteettipurskeista 18 lyhyessä keskosilta mitatussa EEG:ssä todistan, että lääkärit tunnistavat aktiviteettipurskeet johdonmukaisesti. Tämän jälkeen testaan, sopiiko eräs myynnissä oleva algoritmi aktiviteettipurskeiden automaattiseen tunnistukseen. Algoritmin suorituskyky ei ole riittävä, joten kehitän siitä paremman version. Uuden, parannellun algoritmin parametrit optimoidaan käyttämällä opetusaineistona niitä EEGjaksoja, joiden luokittelusta kaikki kolme lääkäriä olivat yhtä mieltä. Algoritmin suorituskykyä arvioidaan suorittamalla optimointi 18 kertaa siten, että kullakin kerralla yksi mittauksista jätetään pois opetusaineistosta. Optimoitua menetelmää käytetään sitten aktiviteettipurskeiden tunnistamiseen poisjätetyssä mittauksessa. Algoritmin arvioitu suorituskyky on erinomainen; sen sensitiivisyys on 96,6 +- 2,8 % ja spesifisyys 95,1 +- 5,6 %. EEG:n segmentointi aktiviteettipurskeisiin ja niiden välisiin jaksoihin tarjoaa pohjan jatkoanalyysille. Aktiviteettipurskeiden osuutta EEG:stä (SAT%) voidaan mahdollisesti käyttää keskosen vireystilan vaihtelujen seuraamiseen. Vireystilojen säännöllinen vaihtelu saattaa olla merkki aivojen hyvinvoinnista. Tässä työssä esitelty algoritmi voi osaltaan edesauttaa keskosten hoidon kehittymistä entistä paremmaksi

    Cross-cultural evidence for the influence of positive self-evaluation on cross-cultural differences in well-being

    Get PDF
    Poster Session F - Well-Being: abstract F197We propose that cultural norms about realism and hedonism contribute to the cross-cultural differences in well-being over and above differences in objective living conditions. To test this hypothesis, we used samples from China and the United States. Results supported the mediating role of positive evaluative bias in cross-cultural differences in well-being.postprin

    Values and need satisfaction across 20 world regions

    Get PDF
    Poster Session F - Motivation/Goals: abstract F78Intrinsic valuing predicts the satisfaction of psychological needs (Niemiec, Ryan, & Deci, 2009). We conceptually replicate and extend this finding across 20 world regions. In multi-level models, Schwartz’s (1992) self-transcendence value was positively related to autonomy, competence, and relatedness satisfaction, even when controlling for the Big Five.postprin

    Active learning in infancy and adulthood: individual strategies for information sampling

    Get PDF
    Humans are astounding learners. They don’t passively absorb information but actively engage in the process: they select information according to their own characteristics e.g., their state of knowledge, abilities, needs or goals, which has the potential to profoundly impact on how individuals experience the world. This PhD aimed to characterise different ways in which agents tailor their sampling of information to fit their priors’ strength, attentional skills, learning progress or executive functioning. In a first study (Chap. 2) conducted with adults, we looked at the influence of priors (prior access to informative stimuli) on visual objects recognition and exploration. Priors enabled participants to guide their fixations to quantitatively more informative locations when exploring ambiguous stimuli. However, presenting stimuli of varying ambiguity levels in a random fashion destroyed this ability to guide exploration with specific priors. In a second study (Chap. 3 and 4) using electroencephalography (EEG), we showed that 10-month-old infants’ parent-reported trait attention was linked to their processing of an information stream in which visual distractors interrupted an ongoing movie. Importantly, we found that infants’ trait sensory processing as reported by parents was not only linked to their engagement with the task, but also to their brain response to distractors, linking together several levels of individual differences in information processing. At the brain level, we found a crucial role of occipital high-frequency gamma-range EEG activity and, for the first time in infants, of its alignment with lower-frequency activity for blocking the processing of distractors vs. the ongoing video. These results bring in new and valuable information for theories of how the brain processes information and implements attentional mechanisms early on during life. Finally, in a last study, we looked at the influence of learning progress (Chap. 5) and executive functions (Chap. 6) on how 15-month-old infants learn and explore. We showed that learning progress at the start of a matching-rule learning task but not the achieved score per se, predicted how long infants would stay on the task. We also investigated the existence of overarching exploration strategies in infants by looking at exploratory depth’s and breadth’s stability within the visual modality and between the visual and manual modalities, as well as their link with individual differences in executive function. We only found evidence for stability in exploratory breadth between two sets of visual trials. This visual exploration breadth was positively correlated with participants’ age and visual working memory. Overall, this thesis brings together studies with different age groups and techniques which all point to the fact that individuals actively shape their own sampling of information in a deterministic fashion that suits their personal state and abilities

    Pilot study for subgroup classification for autism spectrum disorder based on dysmorphology and physical measurements in Chinese children

    Get PDF
    Poster Sessions: 157 - Comorbid Medical Conditions: abstract 157.058 58BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder affecting individuals along a continuum of severity in communication, social interaction and behaviour. The impact of ASD significantly varies amongst individuals, and the cause of ASD can originate broadly between genetic and environmental factors. Objectives: Previous ASD researches indicate that early identification combined with a targeted treatment plan involving behavioural interventions and multidisciplinary therapies can provide substantial improvement for ASD patients. Currently there is no cure for ASD, and the clinical variability and uncertainty of the disorder still remains. Hence, the search to unravel heterogeneity within ASD by subgroup classification may provide clinicians with a better understanding of ASD and to work towards a more definitive course of action. METHODS: In this study, a norm of physical measurements including height, weight, head circumference, ear length, outer and inner canthi, interpupillary distance, philtrum, hand and foot length was collected from 658 Typical Developing (TD) Chinese children aged 1 to 7 years (mean age of 4.19 years). The norm collected was compared against 80 ASD Chinese children aged 1 to 12 years (mean age of 4.36 years). We then further attempted to find subgroups within ASD based on identifying physical abnormalities; individuals were classified as (non) dysmorphic with the Autism Dysmorphology Measure (ADM) from physical examinations of 12 body regions. RESULTS: Our results show that there were significant differences between ASD and TD children for measurements in: head circumference (p=0.009), outer (p=0.021) and inner (p=0.021) canthus, philtrum length (p=0.003), right (p=0.023) and left (p=0.20) foot length. Within the 80 ASD patients, 37(46%) were classified as dysmorphic (p=0.00). CONCLUSIONS: This study attempts to identify subgroups within ASD based on physical measurements and dysmorphology examinations. The information from this study seeks to benefit ASD community by identifying possible subtypes of ASD in Chinese population; in seek for a more definitive diagnosis, referral and treatment plan.published_or_final_versio
    corecore