Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective

Machine Learning como ferramenta gerencial para predição de indicadores e detecção de anomalias

A presente dissertação tem o objetivo de identificar as técnicas de Machine Learning utilizadas nas áreas de Engenharia e Medicina e proporcionar um conhecimento da aplicação de modelos de Machine Learning e métodos de detecção de anomalias para problemas gerenciais, pois muitas vezes veem-se estas técnicas sendo incorporadas a problemas complexos e de larga escala, sem muitos exemplos dentro dos ambientes gerenciais. O trabalho está dividido em dois artigos: o primeiro artigo é uma revisão de literatura focada em apresentar os algoritmos de Machine Learning, os tipos de problemas aos quais são aplicados e métodos de validação utilizados nas áreas de Engenharia e Medicina. O segundo artigo apresenta o processo de criação de um modelo de Machine Learning capaz de predizer um indicador gerencial bem como propor uma métrica para detecção de anomalias. Todos os artigos utilizaram ferramentas open source, como o software estatístico R. As contribuições dos artigos foram: (1) Identificação dos algoritmos mais utilizados nas áreas de Engenharias e Medicinas, os métodos de validação mais utilizados e as contribuições dos autores sobre desempenho dos algoritmos; (2) Demonstração do processo de criação de um modelo de Machine Learning (coleta e preparação dos dados, seleção das variáveis, escolha do algoritmo, seleção dos hiperparâmetros, treino do modelo e avaliação dos resultados), além do método de detecção de anomalia através da análise da distribuição da diferença entre predito e observado.The present dissertation aims to identify the Machine learning techniques used in the fields of engineering and medicine and provides knowledge of the application of models of machine learning and anomaly detection methods to management problems, since often these techniques are incorporated into complex and large-scale problems without many examples within the management environments. The work is divided into two articles: the first article is a literature review focused on presenting Machine Learning algorithms, the types of problems to which they are applied and validation methods used in the areas of Engineering and Medicine. The second article presents the process of creating a Machine Learning model capable of predicting a managerial indicator as well as proposing a metric for detecting anomalies. All articles used open-source tools, such as the statistical software R. The contributions of the articles were: (1) Identification of the most used algorithms in the areas of Engineering and Medicine, the most used validation methods and the contributions of the authors about algorithms performance; (2) Demonstration of the process of creating a Machine Learning model (collection and preparation of data, variables selection, choice of algorithm, selection of hyperparameters, training of the model and evaluation of results), in addition to the method of detection of anomaly by analyzing the distribution of the difference between predicted and observed

Essays on financial systemic risk

Tese (doutorado)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade e Gestão Pública, Programa de Pós-Graduação em Administração, 2018.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).This dissertation presented to obtain the Ph.D. degree in Business Administration is composed of two articles. The first one presents an analysis of the literature on systemic financial risk. To that end, we analyze and classify 266 articles that were published no later than September 2016 in the databases Scopus and Web of Knowledge; these articles were identified using the keywords “systemic risk”, “financial stability”,“financial”, “measure”, “indicator”, and “index”. They were evaluated based on 10 categories, namely, type of study, type of approach, object of study, method, spatial scope, temporal scope, context, focus, type of data used, and results. The analysis and classification of this literature made it possible to identify the remaining gaps in the literature on systemic risk; this contributes to a future research agenda on the topic. Moreover, the most influential articles in this field of research and the articles that compose the main stream research on systemic financial risk were identified. In the second article, we model an indicator that aims to identify systemic risk in the financial markets. Using 93 assets from different classes and from both developed and emerging countries, we apply principal components analysis (PCA) to calculate an initial indicator that is then submitted to Markov switching (MS) technique. This procedure advances the use of PCA in systemic risk modelling by preventing the need for arbitrary definitions of normal and stressed regimes. Additionally, applying MS to the indicator extracted by PCA from the correlation matrix of a relevant number of assets of various classes supports the argument that the indicator is indeed systemic. The results show that the probabilities that the indicator is under stress, according to the MS model, can be used as a signal of systemic risk. We also verified that the average risk of assets, calculated by the average value-at-risk (VaR), is affected when the series of these assets are separated in the systemic risk and normal regimes. In addition, we measure the performance of the indicator compared to other metrics built with only an asset class, especially stock indices. The results show that our model adequately depicts periods of high systemic risk, being relatively thorough

Sedimentos subaquáticos como fontes de bactérias anaeróbicas facultativas hidrocarbonoclásticas e produtoras de biossurfactantes

Doutoramento em Engenharia QuímicaActualmente são conhecidas poucas estirpes bacterianas capazes de produzir biossurfactantes (BSFs) em condições de microaerobiose ou anaerobiose. Estas bactérias têm um papel importante não só em processos naturais (ex. formação de biofilmes ou de hidratos de gás), como podem ter diversas aplicações biotecnológicas (ex. estratégias de biorremediação e aplicações industriais). As bactérias produtoras de BSFs em condições de limitação de oxigénio, com capacidade para degradar hidrocarbonetos são de particular interesse para estratégias de biorremediação de locais contaminados com hidrocarbonetos de petróleo (PHs) e na recuperação microbiana de petróleo (MEOR). Neste contexto, o objectivo deste trabalho foi o isolamento, identificação e a caracterização de bactérias anaeróbias ou anaeróbias facultativas produtoras de BSF e degradadoras de hidrocarbonetos (hidrocarbonoclásticas) na perspetiva da sua aplicação biotecnológica em condições de limitação de oxigénio. Foram escolhidos dois ambientes contaminados com PHs como potenciais fontes de bactérias hidrocarbonoclásticas produtoras de BSFs: vulcões de lama (MV) de mar profundo do Golfo de Cádis (Oceano Atlântico) e o sistema estuarino da Ria de Aveiro (Portugal). Foram preparadas culturas de enriquecimento com sedimentos subaquáticos recolhidos nestes dois habitats, como potenciais inóculos de bactérias anaeróbias facultativas. Um design experimental fatorial foi usado para testar o efeito do crude como fonte de carbono, e de nitrato e/ou sulfato, como aceitadores terminais de eletrões. De forma a melhor compreender a estrutura das comunidades bacterianas envolvidas na biodegradação de PHs nos MV do mar profundo procedeu-se à sequenciação do gene 16S rRNA das comunidades bacterianas de culturas de enriquecimento com sedimento de dois MVs, um activo e outro inactivo, e com ou sem adição de crude e/ou nitrato. Detetou-se uma diferenciação entre as comunidades dos dois MVs, independentemente dos suplementos a que as culturas foram expostas, sendo que Alphaproteobacteria e Bacilli predominaram nas culturas com sedimentos de MV activo e inactivo, respectivamente. De uma forma menos acentuada, tanto o nitrato como o crude afetaram a composição das comunidades bacterianas. Géneros de bactérias que só foram detectados nos ensaios com adição de crude (ex. Erythrobacteraceae no MV activo e Acidimicrobiale no MV inactivo) poderão ser usados como indicadores da presença de hidrocarbonetos de petróleo nestes habitats. A biodegradação de PHs nas culturas com crude foi avaliada por cromatografia gasosa acoplada a espectrometria de massa. De uma forma geral, as comunidades de culturas do MV activo foram capazes de degradar n-alcanos de tamanho inferior a C13 e compostos monoaromáticos, enquanto as comunidades do MV inactivo apresentaram a capacidade de metabolizar vários tipos de hidrocarbonetos aromáticos policíclicos. A presença de nitrato apenas afectou positivamente a biodegradação de alcanos, e não teve efeito ou foi mesmo inibitória da biodegradação de outros hidrocarbonetos. A partir de todas as culturas, com todos os tipos de sedimentos, dos MVs do Golfo de Cádis e do estuário da Ria de Aveiro, foi possível isolar-se um total de 13 isolados capazes de sobreviver exclusivamente com crude como fonte de carbono e produzir BSF em condições de aerobiose. Destas, apenas duas não foram capazes de produzir BSFs em anaerobiose. A sequenciação do gene 16S rRNA dos isolados permitiu identifica-los como pertencendo aos géneros Pseudomonas, Bacillus, Ochrobactrum, Brevundimonas, Psychrobacter, Staphylococcus, Marinobacter e Curtobacterium, a maioria dos quais não tinha ainda membros conhecidos como produtores de BSF em anaerobiose. Os resultados obtidos com este trabalho permitiram caracterizar melhor as comunidades envolvidas na degradação de PHs em MVs de mar profundo. Conseguiu-se ainda isolar e identificar estirpes, tanto de mar profundo como de ambiente estuarino, capazes de degradar PHs e produzir BSFs em condições de anaerobiose. Estas estirpes apresentam elevado potencial biotecnológico para aplicações como MEOR e biorremediação em ambientes com escassez de oxigénio.So far, only few bacterial strains are known to produce biosurfactants (BSFs) under microaerobic or anaerobic conditions. However, these bacteria are not only involved in important natural processes (e.g. biofilm and gas hydrates formations) but can also be used in several biotechnological applications (e.g. bioremediation strategies and industrial applications). Bacteria able to produce BSFs under oxygen-limiting conditions that are also able to degrade hydrocarbons, are of particular interest to bioremediation strategies of sites contaminated with petroleum hydrocarbons (PHs) and microbial enhanced oil recovery (MEOR) strategies. In this context, this work aims at isolating, identifying, and characterizing BSF-producing and hydrocarbon-degrading (hydrocarbonoclastic) bacteria grown under anaerobic conditions, which can be used in biotechnological applications under oxygen limitation. Two environments contaminated with PHs were chosen as potential sources of hydrocarbonoclastic BSF-producing bacteria: deep-sea mud volcanos from the Gulf of Cadiz (Atlantic Ocean), and the estuarine system of Ria de Aveiro (Portugal). Enrichment cultures were prepared using subaquatic sediments from both sites, as potential sources of facultative anaerobic bacteria. A factorial experimental design was used to test the effect of crude oil as carbon source, and nitrate and/or sulfate, as terminal electron acceptors. Aiming at better understanding the structure of bacterial communities involved in PHs biodegradation at deep-sea MVs, sequencing of the 16S rRNA gene was performed for bacterial communities from cultures containing sediments from two MVs, active and inactive, and with or without crude oil and/or nitrate. A distinction between the communities of MVs with different activity, independent of the supplements was observed. Alphaproteobacteria and Bacilli were the predominant classes found in enrichment cultures inoculated with active and inactive MVs sediments, respectively. In a minor scale, nitrate and crude oil additions also affected the composition of bacterial communities. Therefore, genera that only appeared in cultures with crude oil. (e.g. Erythrobacteraceae in active MV cultures and Acidimicrobiale in inactive MV cultures) can be used as biosensors of the presence of PHs in these habitats. Biodegradation of PHs in cultures containing crude oil was assessed by gas chromatography coupled with mass spectrometry. Overall, communities from active MV cultures were able to degrade n-alkanes below C13 and monoaromatic hydrocarbons, while communities from inactive MV cultures presented the ability to metabolize several types of polycyclic aromatic hydrocarbons. The presence of nitrate only had a positive effect on the biodegradation of alkanes, and had no effect or even an inhibitory effect on the biodegradation of other hydrocarbons. A total of 13 isolates able to survive on crude as carbon source and produce BSF under aerobic conditions were obtained from all cultures either from sediments of the Gulf of Cadiz MVs or the estuarine system of Ria de Aveiro. Only two isolates failed to produce BSF under anaerobiosis. Sequencing of 16S rRNA gene was used to establish the identification of isolates as Pseudomonas, Bacillus, Ochrobactrum, Brevundimonas, Psychrobacter, Staphylococcus, Marinobacter and Curtobacterium. Most of these genera had never been described as able to produce BSFs under anaerobic conditions. The results obtained in this work allowed to better characterize the deep-sea communities involved in PHs degradation, as well as, to identify strains from deep-sea and estuarine sediments able to degrade PHs and produce BSFs under anaerobic conditions. These bacteria present high biotechnological potential for applications in oxygen-limiting environments, such as, MEOR and bioremediation of environments contaminated with PHs

IFPOC Symposium:Discovering antecedents and consequences of complex change recipients' reactions to organizational change.

IFPOC symposium: Discovering antecedents and consequences of complex change recipients' reactions to organizational change Chairs: Maria Vakola (Athens University of Economics and Business) & Karen Van Dam (Open University) Discussant: Mel Fugate (American University, Washington, D.C) State of the art Organisations are required to continuously change and develop but there is a high failure rate associated with change implementation success. In the past two decades, change researchers have started to investigate change recipients' reactions to change recognizing the crucial role of these reactions for successful change. This symposium aims at identifying and discussing the complex processes that underlie the relationships among antecedents, reactions and outcomes associated with organizational change. New perspective / contributions This symposium consists of five studies that extend our knowledge in the field by (i) providing an analysis of change recipients' reactions going beyond the dichotomous approaches (acceptance or resistance) (ii) revealing understudied antecedents-reactions and reactions-consequences patterns and relationships (iii) shedding light on the role of contextual factors i.e team climate and individual factors i.e emotion regulation on the adaptation to change. This symposium is based on a combination of both quantitative (i.e diary, survey) and qualitative (i.e interviews) research methodology. Research / practical implications This symposium aims to increase our understanding of the complex processes associated with change recipients' reactions to change. Discovering how these reactions are created and what are their results may reveal important contingencies that can explain how positive organizational outcomes during times of change can be stimulated which is beneficial for both researchers and practitioners