A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification

Electrocardiogram (ECG) signals, which capture the heart's electrical activity, are used to diagnose and monitor cardiac problems. The accurate classification of ECG signals, particularly for distinguishing among various types of arrhythmias and myocardial infarctions, is crucial for the early detection and treatment of heart-related diseases. This paper proposes a novel approach based on an improved differential evolution (DE) algorithm for ECG signal classification for enhancing the performance. In the initial stages of our approach, the preprocessing step is followed by the extraction of several significant features from the ECG signals. These extracted features are then provided as inputs to an enhanced multi-layer perceptron (MLP). While MLPs are still widely used for ECG signal classification, using gradient-based training methods, the most widely used algorithm for the training process, has significant disadvantages, such as the possibility of being stuck in local optimums. This paper employs an enhanced differential evolution (DE) algorithm for the training process as one of the most effective population-based algorithms. To this end, we improved DE based on a clustering-based strategy, opposition-based learning, and a local search. Clustering-based strategies can act as crossover operators, while the goal of the opposition operator is to improve the exploration of the DE algorithm. The weights and biases found by the improved DE algorithm are then fed into six gradient-based local search algorithms. In other words, the weights found by the DE are employed as an initialization point. Therefore, we introduced six different algorithms for the training process (in terms of different local search algorithms). In an extensive set of experiments, we showed that our proposed training algorithm could provide better results than the conventional training algorithms.Comment: 44 pages, 9 figure

Validation of a method for the estimation of energy expenditure during physical activity using a mobile device accelerometer

The main goal of this paper consists on the adaption and validation of a method for the measurement of the energy expenditure during physical activities. Sensors available in a mobile device, e.g., a smartphone, a smartwatch, or others, allow the capture of several signals, which may be used to the estimation of the energy expenditure. The adaption consists in the comparison between the units of the data acquired by a tri-axial accelerometer and a mobile device accelerometer. The tests were performed by healthy people with ages between 12 and 50 years old that performed several activities, such as standing, gym (walking), climbing stairs, walking, jumping, running, playing tennis, and squatting, with a mobile device on the waist. The validation of the method showed that the energy expenditure is underestimated and super estimated in some cases, but with reliable results. The creation of a validated method for the measurement of energy expenditure during physical activities capable for the implementation in a mobile application is an important issue for increase the acceptance of the mobile applications in the market. As verified the results obtained are around 124.6 kcal/h, for walking activity, and 149.7 kcal/h, for running activity.This work was supported by FCT project PEst-OE/EEI/L A0008/2013 (Este trabalho foi suportado pelo projecto FCT PEst-OE/EEI/LA0008/2013). The authors would also like to acknowledge the contribution of the COST Action IC1303 – AAPELE – Architectures, Algorithms and Protocols for Enhanced Living Environments

Formação aplicada ao sector agroindustrial

Hoje em dia, devido à crise nacional e internacional, existe a consciência que a eficácia e a eficiência das empresas do setor agroindustrial estão parcialmente relacionadas com as competências técnicas dos produtores e dos seus colaboradores. Assim tornar-se premente a melhoria das suas competências técnicas, a fim de promover o aumento da produtividade através do desenvolvimento e modernização de técnicas e sistemas tradicionais neste sector. Este artigo apresenta os principais resultados do projeto, (1) análise das necessidades formativas no sector agroindustrial em Portugal, (2) análise da oferta formativa e entidades de formação, (3) análise das tendências de evolução do mercado, (4) definição de uma estratégia formativa, e (5) ajustamento e desenvolvimento de planos formativos dirigidos ao sector agroindustrial. Este último resultado destina-se à criação de propostas de currículos de cursos adequados ao sector, que possam promover o desenvolvimento do sector agroindustrial em Portugal e da sua competitividade, pela adesão a inovações de cariz tecnológico, metodológico e de práticas, pela capacidade de investir e de risco e pela adopção de normas da Comunidade Europeia de produção e comercialização.info:eu-repo/semantics/publishedVersio

Context-aware algorithms for Diabetes or Prediabetes prediction and diagnosis support in Ambient Assisted Living

The need to control diabetes provides an opportunity to develop new technological solutions for self-management and real predictions. These predictions can be useful in preventing unwanted events, such as hypoglycaemia. The patients with diabetes type 1 and some patients with diabetes type 2 commonly fear hypoglycaemia. The aim of this thesis is to develop a context-aware framework for hypoglycaemia prediction using sparse data, information fusion and classifiers’ consensus decision in a 24h hour time frame. With this approach, we contribute by proposing a hypoglycaemia prediction algorithm, in a self-management scenario, allowing it to be used by patients who perform their monitorization using a glucometer. The literature proposes glycaemia prediction algorithms using data from Continuous Glucose Monitoring (CGM) systems, but these approaches are not extensible to patients without these systems. Prediction algorithms based on discrete information are a challenge, so we proposed a novel context-aware framework for hypoglycaemia prediction based on data fusion and classifiers’ consensus decision. The fusion of additional context information with the conventional features can contribute to decrease the effect of inter- and intra-subject variability on prediction patterns. Also, the prediction decision based on classifiers’ consensus can contribute to the creation of suitable and generalised predictive algorithms. Integrating contextual and time-based features improves the accuracy on predicted hypoglycaemia. Using the classifiers’ consensus decision, 66% of the researched patients have over 90% of hypoglycaemia predicted (with 37.7% with 100% of hypoglycaemia predicted), without the increase of false positives (false alarms). This work shows the importance of data fusion and consensus decision to handle the patterns associated with hypoglycaemia risk and its prediction, however, further research is necessary to provide the necessary interpretability to the predictive models.A necessidade de controlar a diabetes oferece a oportunidade para o desenvolvimento de novas soluções tecnológicas para autogestão e previsões reais. Essas previsões podem ser úteis na prevenção de eventos indesejados, como a hipoglicemia. Os pacientes com diabetes tipo 1 e alguns pacientes com diabetes tipo 2 geralmente temem a hipoglicemia. O objetivo desta tese é desenvolver um algoritmo sensível ao contexto para a previsão de hipoglicemia recorrendo a dados discretos, fusão de informação e decisão consensual de classificadores em um período de 24 horas. Com esta abordagem, contribuímos propondo um algoritmo de previsão de hipoglicemia, num cenário de autogestão, permitindo que seja utilizado por pacientes que realizam a sua monitorização com um glicosímetro. A literatura propõe algoritmos de previsão de glicemia usando dados de sistemas contínuos de monitorização, mas essas abordagens não são extensíveis a pacientes sem esses sistemas. Algoritmos de previsão baseados em informações discretas são um desafio, por isso propusemos uma novo algoritmo sensível ao contexto para previsão de hipoglicemia com base na fusão de dados e decisão de consenso de classificadores. A fusão de informações de contexto com as variáveis convencionais pode contribuir para diminuir o efeito da inter- e intra-variabilidade nos padrões de previsão. Além disso, a decisão de previsão baseada no consenso dos classificadores pode contribuir para a criação de algoritmos preditivos adequados e generalizados. A integração de variáveis contextuais e baseadas no tempo melhora a precisão e a previsão de hipoglicemia. Usando a decisão de consenso dos classificadores, 66% dos pacientes têm mais de 90% de hipoglicemias previstas (37,7% dos pacientes com 100% de hipoglicemias previstas) sem o aumento de falsos positivos (alarmes falsos). Este trabalho mostra a importância da fusão de dados e decisão consensual para capturar os padrões associados ao risco de hipoglicemia e sua previsão, no entanto, é necessário aprofundar mais a questão da fusão de dados e modelos e explorar a interpretabilidade dos modelos preditivos.The research has been partially funded by the FCT/MCTES through national funds, and when applicable, co-funded EU funds under the project UIDB/50008/2020 and Operação Centro 01-0145-FEDER-000019 – C4 – Centro de Competências em Cloud Computing, cofinanced by the Programa Operacional Regional do Centro (CENTRO 2020), through the Sistema de Apoio à Investigação Científica e Tecnológica – Programas Integrados de IC&DT. I would also like to acknowledge the contribution of the COST Action IC1303: AAPELE— Archi- tectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226; SHELD-ON—Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology)

Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction – A systematic literature review

International audienceBackground and aim - Hypoglycaemia prediction play an important role in diabetes management being able to reduce the number of dangerous situations. Thus, it is relevant to present a systematic review on the currently available prediction algorithms and models for hypoglycaemia (or hypoglycemia in US English) prediction.Methods - This study aims to systematically review the literature on data-based algorithms and models using diabetics real data for hypoglycaemia prediction. Five electronic databases were screened for studies published from January 2014 to June 2020: ScienceDirect, IEEE Xplore, ACM Digital Library, SCOPUS, and PubMed. Results - Sixty-three eligible studies were retrieved that met the inclusion criteria. The review identifies the current trend in this topic: most of the studies perform short-term predictions (82.5%). Also, the review pinpoints the inputs and shows that information fusion is relevant for hypoglycaemia prediction. Regarding data-based models (80.9%) and hybrid models (19.1%) different predictive techniques are used: Artificial neural network (22.2%), ensemble learning (27.0%), supervised learning (20.6%), statistic/probabilistic (7.9%), autoregressive (7.9%), evolutionary (6.4%), deep learning (4.8%) and adaptative filter (3.2%). Artificial Neural networks and hybrid models show better results. Conclusions - The data-based models for blood glucose and hypoglycaemia prediction should be able to provide a good balance between the applicability and performance, integrating complementary data from different sources or from different models. This review identifies trends and possible opportunities for research in this topic

Hypoglycaemia prediction using information fusion and classifiers consensus

International audienceThe recommendation that there must be a balance between insulin, food, and exercise to keep diabetes under control provides an opportunity for developing mobile applications for self-management of the disease. Real predictions can improve the quality of patients’ lives by avoiding unwanted events, namely, hypoglycaemia. We proposed a hypoglycaemia prediction approach combining information fusion and classifiers consensus to predict the risk of hypoglycaemia in a 24-h window. First, we train a multi-classifiers system from different sources of different patients. After using data from a unique patient, we performed the prediction of the risk of hypoglycaemia and evaluate the consensus decision of the single models resulting from the learning process. The predictions were performed for 54 patients from the University of California Irvine diabetes dataset. The results from classifiers consensus decision provide very promising results, which are acceptable considering that we used sparse data and data from self-monitoring blood glucose. Our approach shows that with a 24-h window is possible to catch appropriate patterns associated with the risk of hypoglycaemia and proposed a solution that can improve the hypoglycaemia prediction with a higher specificity, i.e. less false alarms, when compared with similar literature

TICE.Healthy: Integração de soluções TIC para a "Saúde e Qualidade de Vida"

Este artigo descreve o projeto português TICE.Healthy, que possui como principal meta a integração de soluções de Tecnologias de Informação e Comunicação (TIC) para a “Saúde e Qualidade de Vida”. Este projeto integra oito sub-projetos que visam a disponibilização de aplicações numa plataforma web, denominada eVida, permitindo a partilha e troca de informação e dados médicos, utilizando formatos padrão. Cada um dos sub-projetos possui dados de natureza diferente e, por este motivo, para que a sua integração seja eficiente, foram desenvolvidas diferentes estratégias de definição, uso e integração de informação. Este artigo aborda com mais detalhe um destes sub-projetos, o Metabolic.Care, sendo apresentadas as estratégias adotadas, as melhores práticas encontradas para resolver os problemas, as oportunidades e os riscos, e as dificuldades encontradas

Prediction of Atrial Fibrillation using artificial intelligence on Electrocardiograms: A systematic review

Atrial Fibrillation (AF) is a type of arrhythmia characterized by irregular heartbeats, with four types, two of which are complicated to diagnose using standard techniques such as Electrocardiogram (ECG). However, and because smart wearables are increasingly a piece of commodity equipment, there are several ways of detecting and predicting AF episodes using only an ECG exam, allowing physicians easier diagnosis. By searching several databases, this study presents a review of the articles published in the last ten years, focusing on those who reported studies using Artificial Intelligence (AI) for prediction of AF. The results show that only twelve studies were selected for this systematic review, where three of them applied deep learning techniques (25%), six of them used machine learning methods (50%) and three others focused on applying general artificial intelligence models (25%). To conclude, this study revealed that the prediction of AF is yet an under-developed field in the context of AI, and deep learning techniques are increasing the accuracy, but these are not as frequently applied as it would be expected. Also, more than half of the selected studies were published since 2016, corroborating that this topic is very recent and has a high potential for additional research

Implementing Mobile Games into Care Services—Service Models for Finnish and Chinese Elderly Care

The purpose of this paper was to create service models for cognitively stimulating mobile games and incorporate them into Finnish and Chinese elderly care. The implementation involved the use of two different mobile games as part of the everyday lives of older adults in care homes in Finland (3 months) and China (6 months). Although a large number of publications examine serious games in elderly care, there are rather few publications related to the practical implementation within the elderly care processes. In general, rehabilitation orientated games should incorporate entertainment (motivation) and relevant therapeutic content (rehabilitation) in order to be effective. Regardless of the game design, successful implementation of the games in elderly care is paramount to benefit the end user. In this paper, two mobile games were investigated as a case study. To investigate the therapeutic content of the games, the game outcomes (game scores and time stamps) were automatically recorded to facilitate analysis of the participant’s progress during the trial. To investigate motivation, user feedback was collected through observation of the game trials and by interviewing the nursing staff and the participants (test group). The gaming service implementation was designed in collaboration with the nursing staff and researchers, according to an experimentation-driven approach, in which the service model ideas were tested by the professionals before piloting. In both countries, the players and the nursing staff found the games showed potential as self-managed rehabilitation tools. Other significant effects of gameplay were enhanced recreation and self-managed activity level. Despite cultural differences, the gaming experience was amazingly similar and improvements in game scores were also observed during the trial in both countries. The biggest difference between the pilots was the implementation process, which led to the development of two different service models that are reported in this paper. In Finland, the games were embedded into the care practices and the nursing staff were responsible for the piloting. In China, the games were independent of the care process and an external service provider (the researcher) managed the piloting. The findings imply that service design in different cultures should be carefully considered when implementing new digital services