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Monitoring signals such as fetal heart rate (FHR) are important indicators of fetal well-being. Computer-assisted analysis of FHR patterns has been successfully used as a decision support tool. However, the absence of a gold standard for the building blocks decision-making in the systems design process impairs the development of new solutions. Here we propose a prognostic model based on advanced signal processing techniques and machine learning algorithms for the fetal state assessment within a comprehensive evaluation process. Feature-engineering-based and time-series-based machine learning classifiers were modeled into three data segmentation schemas for CTU-UHB, HUFA, and DB-TRIUM datasets and the generalization performance was assessed by a two-way cross-dataset evaluation. It has been shown that the feature-based algorithms outperformed the time-series ones on data-limited scenarios. The Support Vector Machines (SVM) obtained the best results on the datasets individually: specificity (85.6% ) and sensitivity (67.5%). On the other hand, the most effective generalization results were achieved by the Multi-layer perceptron (MLP) with a specificity of 71.6% and sensitivity of 61.7%. The overall process provided a combination of techniques and methods that increased the final prognostic model performance, achieving relevant results and requiring a smaller amount of data when compared to the state-of-the-art fetal status assessment solutions.
Manuel Gonçalves Da Silva Neto; João Paulo Do Vale Madeiro; João Alexandre Lobo Marques; Danielo G. Gomes. Towards an efficient prognostic model for fetal state assessment. Measurement 2021, 185, 110034 .
AMA StyleManuel Gonçalves Da Silva Neto, João Paulo Do Vale Madeiro, João Alexandre Lobo Marques, Danielo G. Gomes. Towards an efficient prognostic model for fetal state assessment. Measurement. 2021; 185 ():110034.
Chicago/Turabian StyleManuel Gonçalves Da Silva Neto; João Paulo Do Vale Madeiro; João Alexandre Lobo Marques; Danielo G. Gomes. 2021. "Towards an efficient prognostic model for fetal state assessment." Measurement 185, no. : 110034.
Joao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. Predictive Models for Decision Support in the COVID-19 Crisis. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications 2021, 1 .
AMA StyleJoao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong. Predictive Models for Decision Support in the COVID-19 Crisis. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications. 2021; ():1.
Chicago/Turabian StyleJoao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. 2021. "Predictive Models for Decision Support in the COVID-19 Crisis." Tunable Low-Power Low-Noise Amplifier for Healthcare Applications , no. : 1.
The support provided by geographic data and the corresponding processing tools can play an essential role to support decision-making process, especially for public healthcare during the current pandemic outbreak of the COVID-19. Geographic data collection may be challenging when is necessary to obtain precise latitude and longitude, for example. The current chapter presents a new tool for the geographic location prediction of new cases of COVID-19, considering the confirmed cases in the city of Fortaleza, capital of the State of Ceara, Brazil. The methodology is based on a sequential approach of four clustering algorithms: Agglomerative Clustering, DBSCAN, Mean Shift, and K-Means followed by a two-dimensional predictor based on the Kalman filter. The results are presented following a case study approach with different examples of implementation and the corresponding analysis of the results. The proposed technique could generally predict the trend of the infection geographically in Fortaleza and effectively supported the decision-making process of public healthcare analysts and managers from the Secretariat of Health of the State of Ceara.
Joao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications 2020, 89 -98.
AMA StyleJoao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong. Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications. 2020; ():89-98.
Chicago/Turabian StyleJoao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. 2020. "Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil." Tunable Low-Power Low-Noise Amplifier for Healthcare Applications , no. : 89-98.
The use of computational intelligence techniques is being considered for a vast number of applications not only because of its increasing popularity but also because the results achieve good performance and are promising to keep improving. In this chapter, we present the basic theoretical aspects and assumptions of the LSTM model and H20 AutoML framework. It is evaluated on the prediction of the COVID-19 epidemiological data series for five different countries (China, United States, Brazil, Italy, and Singapore), each of them with specific curves, which are results of policies and decisions during the pandemic spread. The discussion about the results is performed with the focus on three evaluation criteria: \(R^2\) Score, MAE, and MSE. Higher \(R^2\) Score was obtained when the sample time series was smoothly increasing or decreasing. The results obtained by the AutoML framework achieved a higher \(R^2\) Score and lower MAE and MSE when compared with LSTM and also with other techniques proposed in the book, such as ARIMA and Kalman predictor. The application of machine learning algorithm selector might be a promising candidate for a good predictor for epidemic time series.
Joao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications 2020, 69 -87.
AMA StyleJoao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong. Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications. 2020; ():69-87.
Chicago/Turabian StyleJoao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. 2020. "Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML." Tunable Low-Power Low-Noise Amplifier for Healthcare Applications , no. : 69-87.
The process of decision-making when dealing with infectious diseases is firmly based on mathematical modeling nowadays. One usual approach is to consider the adoption of compartmental methods such as SIR and SEIR and a large number of corresponding variations for modeling and prediction epidemic time series. Nevertheless, the COVID-19 epidemic characteristics and curves are apparently challenging the results obtained by these models. This chapter presents the results of two traditional compartmental models, SIR (Susceptible—Infected–Recovered) and SEIR (Susceptible–Exposed–Infected–Recovered), and an adapted version of the SEIR, called SEIR with Intervention, which captures the impact of containment measures for the dynamics of the infection rate. The analysis is performed for five countries: China, United States, Brazil, Italy, and Singapore, each of them with specific characteristics of dealing with the pandemic. A sequence of results is presented, considering different parameters, in order to understand the feasibility of application for each model.
Joao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. Epidemiology Compartmental Models—SIR, SEIR, and SEIR with Intervention. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications 2020, 15 -39.
AMA StyleJoao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong. Epidemiology Compartmental Models—SIR, SEIR, and SEIR with Intervention. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications. 2020; ():15-39.
Chicago/Turabian StyleJoao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. 2020. "Epidemiology Compartmental Models—SIR, SEIR, and SEIR with Intervention." Tunable Low-Power Low-Noise Amplifier for Healthcare Applications , no. : 15-39.
When considering time-series forecasting, the application of autoregressive models is a popular and simple technique that is usually considered. In this chapter, we present the basic theoretical aspects and assumptions of the ARIMA—Autoregressive Integrated Moving Average model. It is considered for the prediction of the COVID-19 epidemiological data series of five different countries (China, United States, Brazil, Italy, and Singapore), each of them with specific curves, which are results of the virus reproduction itself but also of policies and government decisions during the pandemic spread. The discussion about the results is performed with the focus on the three evaluation criteria of the model: \(R^2\) Score, MAE, and MSE. Higher \(R^2\) Score was obtained when the sample time series was smoothly increasing or decreasing. The error metrics were higher when the prediction was performed for oscillating data series. This may indicate that the use of ARIMA models may be suitable as a prediction tool for the COVID-19 when the country is not facing severe oscillations in the number of infections.
Joao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. Forecasting COVID-19 Time Series Based on an Autoregressive Model. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications 2020, 41 -54.
AMA StyleJoao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong. Forecasting COVID-19 Time Series Based on an Autoregressive Model. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications. 2020; ():41-54.
Chicago/Turabian StyleJoao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. 2020. "Forecasting COVID-19 Time Series Based on an Autoregressive Model." Tunable Low-Power Low-Noise Amplifier for Healthcare Applications , no. : 41-54.
Considering the application of prediction techniques to support the decision-making process during a dynamic environment such as the one faced during the COVID-19 pandemic, demands the evaluation of several different strategies to compare and define the most suitable solution for each necessity of prediction. Analyzing the epidemic time series, for example, the number of new confirmed cases of COVID-19 per day, classic compartmental models or linear regressions may not provide results with enough precision to support managerial or clinical decisions. The application of nonlinear models is an alternative to improve the performance of these models. The Kalman Filter (KF) is a state-space model that is used in several applications as a predictor. The filter algorithm requires low computational power and provides estimates of some unknown variables given the measurements observed over time. In this chapter, the KF predictor is considered in the analysis of five countries (China, United States, Brazil, Italy, and Singapore). Similarly to the ARIMA methodology, the results are evaluated based on three criteria: \(R^2\) Score, MAE (Mean Absolute Error), and MSE (Mean Square Error). It is important to notice that the definition of a predictor for epidemiological time series shall be carefully evaluated and more complex implementations do not always represent a better prediction on average. For the proposed KF predictor, there were specific time-series samples with no satisfactory result, achieving a negative \(R^2\) Score, for example, while, on the other, other samples achieved higher \(R^2\) Score and lower MAE and MSE, when compared to other linear predictors.
Joao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications 2020, 55 -68.
AMA StyleJoao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong. Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications. 2020; ():55-68.
Chicago/Turabian StyleJoao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. 2020. "Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering." Tunable Low-Power Low-Noise Amplifier for Healthcare Applications , no. : 55-68.
The task known as prediction is widely applied in several different areas of knowledge, from popular applications such as weather forecasting, going through supply chain management, an increasing range of adoption in healthcare and, more specifically in epidemiology, the central topic of this book. The new challenges brought with the COVID-19 pandemic highlighted the possibilities and necessity of using prediction techniques to support decisions related to epidemiology in both managerial and clinical areas. In practice, the current outbreak created a strong need for the adoption of different computational models to support both medical teams and public health administrators. The methods vary from simple linear regressions to very complex algorithms based on Artificial Intelligence (AI) techniques. The present chapter contextualizes the use of prediction for decision support as a foundation of the following chapters which are focused on the application for the COVID-19 pandemic time series. With such a large number of methods for data-driven predictions, a clear distinction between explanation and prediction is firstly provided. From there, a methodological framework is presented, from the data source definition and selection of countries as references for the analysis, going through data handling for validation, until the definition of the evaluation criteria for the proposed models.
Joao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. Prediction for Decision Support During the COVID-19 Pandemic. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications 2020, 1 -13.
AMA StyleJoao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong. Prediction for Decision Support During the COVID-19 Pandemic. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications. 2020; ():1-13.
Chicago/Turabian StyleJoao Alexandre Lobo Marques; Francisco Nauber Bernardo Gois; José Xavier-Neto; Simon James Fong. 2020. "Prediction for Decision Support During the COVID-19 Pandemic." Tunable Low-Power Low-Noise Amplifier for Healthcare Applications , no. : 1-13.
Intrauterine Growth Restriction (IUGR) is a condition in which a fetus does not grow to the expected weight during pregnancy. There are several well documented causes in the literature for this issue, such as maternal disorder, and genetic influences. Nevertheless, besides the risk during pregnancy and labour periods, in a long term perspective, the impact of IUGR condition during the child development is an area of research itself. The main objective of this work is to propose a machine learning solution to identify the most significant features of importance based on physiological, clinical or socioeconomic factors correlated with previous IUGR condition after 10 years of birth. In this work, 41 IUGR (18 male) and 34 Non-IUGR (22 male) children were followed up 9 years after the birth, in average (9.1786 ± 0.6784 years old). A group of machine learning algorithms is proposed to classify children previously identified as born under IUGR condition based on 24-hours monitoring of ECG (Holter) and blood pressure (ABPM), and other clinical and socioeconomic attributes. In additional, an algorithm of relevance determination based on the classifier is also proposed, to determine the level of importance of the considered features. The proposed classification solution achieved accuracy up to 94.73%, and better performance than seven state-of-the-art machine learning algorithms. Also, relevant latent factors related to HRV and BP monitoring are proposed, such as: day-time heart rate (day-time HR), day-night systolic blood pressure (day-night SBP), 24-hour standard deviation (SD) of SBP, dropped, morning cortisol creatinine, 24-hour mean of SDs of all NN intervals for each 5 minutes segment (24-hour SDNNi), among others. With outstanding accuracy of our proposed solutions, the classification system and the indication of relevant attributes may support medical teams on the clinical monitoring of IUGR children during their childhood development.
Sau Nguyen Van; J.A. Lobo Marques; T.A. Biala; Ye Li. Identification of Latent Risk Clinical Attributes for Children Born Under IUGR Condition Using Machine Learning Techniques. Computer Methods and Programs in Biomedicine 2020, 200, 105842 .
AMA StyleSau Nguyen Van, J.A. Lobo Marques, T.A. Biala, Ye Li. Identification of Latent Risk Clinical Attributes for Children Born Under IUGR Condition Using Machine Learning Techniques. Computer Methods and Programs in Biomedicine. 2020; 200 ():105842.
Chicago/Turabian StyleSau Nguyen Van; J.A. Lobo Marques; T.A. Biala; Ye Li. 2020. "Identification of Latent Risk Clinical Attributes for Children Born Under IUGR Condition Using Machine Learning Techniques." Computer Methods and Programs in Biomedicine 200, no. : 105842.
The use of learning analytics (LA) in real-world educational applications is growing very fast as academic institutions realize the positive potential that is possible if LA is integrated in decision making. Education in schools on public health need to evolve in response to the new knowledge and the emerging needs like how to deal with violence or eviction as well as understanding health pandemics like the Corona virus. However, in education, emotion should be considered prior to a full cognition. While negative emotions tend to make one clearly remember data including the minutest detail, positive emotions tend to help one remember more complex things. Using learning analytics, the authors based on LA extended the SCARF model to include social life indicators like happiness. The hypothesis of the extended SSCARF model has been via ignited by the experimentation and data mining from this work with a voluntary teaching program in a local rural school. The results show of SSCARF model reveals that happiness is of more value in the children's learning compared to the material wealth.
Tengyue Li; Joao Alexandre Lobo Marques; Simon Fong. Health and Well-Being Education. International Journal of Extreme Automation and Connectivity in Healthcare 2020, 2, 42 -53.
AMA StyleTengyue Li, Joao Alexandre Lobo Marques, Simon Fong. Health and Well-Being Education. International Journal of Extreme Automation and Connectivity in Healthcare. 2020; 2 (2):42-53.
Chicago/Turabian StyleTengyue Li; Joao Alexandre Lobo Marques; Simon Fong. 2020. "Health and Well-Being Education." International Journal of Extreme Automation and Connectivity in Healthcare 2, no. 2: 42-53.
Crowdsensing exploits the sensing abilities offered by smart phones and users' mobility. Users can mutually help each other as a community with the aid of crowdsensing. The potential of crowdsensing has yet to be fully realized for improving public health. A protocol based on gamification to encourage data sharing and mutual assistance is proposed. The game is called “Lemmings,” which stands for location-based mutual and mobile information navigation system; it is based on a classical video game where a group of creatures have to work and win through the puzzle game together. This game includes an asynchronized messaging system where a player may proactively seek for answers or advice by depositing a question on the messaging server. The server will automatically disseminate the question, which is related to a specific location, to a group of users who are either within the proximity currently or have just recently been there. The users/players are encouraged to help each other in post-pandemic Corona virus period; karma scoring distinguishes the most helpful users in the community.
Renfei Luo; João Alexandre Lôbo Marques; Kok-Leong Ong; Simon Fong. Crowdsensing-Based Gamification for Collective Assistance for Post-Era of Coronavirus Epidemic in Community Living. International Journal of Extreme Automation and Connectivity in Healthcare 2020, 2, 54 -64.
AMA StyleRenfei Luo, João Alexandre Lôbo Marques, Kok-Leong Ong, Simon Fong. Crowdsensing-Based Gamification for Collective Assistance for Post-Era of Coronavirus Epidemic in Community Living. International Journal of Extreme Automation and Connectivity in Healthcare. 2020; 2 (2):54-64.
Chicago/Turabian StyleRenfei Luo; João Alexandre Lôbo Marques; Kok-Leong Ong; Simon Fong. 2020. "Crowdsensing-Based Gamification for Collective Assistance for Post-Era of Coronavirus Epidemic in Community Living." International Journal of Extreme Automation and Connectivity in Healthcare 2, no. 2: 54-64.
O impacto dos impostos no desempenho das escolas privadas é um tema muito pertinente em vista das recentes alterações na estrutura de impostos de Angola e a severa crise econômica e financeira que o país atravessa, o que faz que o Estado estabeleça medidas que visem contribuir para o aumento das receitas públicas, sendo o sistema fiscal uma das áreas estratégicas. O presente trabalho tem como objetivo avaliar a influência dos impostos na gestão de escolas privadas no Município do Lobito, em Angola, de forma a compreender o impacto dos impostos e as obrigações fiscais nas decisões de gestão, bem como a influência decorrente do atual regime fiscal e as exigências tributárias. Por meio da revisão bibliográfica, pretende-se fornecer informação e conteúdos referentes à informação contabilística e o seu papel na gestão empresarial, além de abordar a complementaridade entre a contabilidade, a fiscalidade e a gestão. A temática da gestão fiscal é abordada com realce para o sistema fiscal angolano. Relativamente ao tipo de atividade em estudo, apresenta-se uma abordagem sobre a realidade atual do país, destacando-se a importância das escolas privadas como principal parceiro do estado na luta do analfabetismo e na redução de crianças fora do sistema de ensino e analisando as vantagens e as desvantagens de tal parceria. No estudo empírico, realiza-se um diagnóstico relativo ao tema em estudo por meio de questionários enviados a trinta e quatro escolas privadas do Município do Lobito, o que se permite chegar à conclusão de que os impostos influenciam, positivamente, a gestão das escolas privadas, incentivando a organização e a melhoria do controlo interno, e de que os gestores têm certo domínio da legislação fiscal, procurando entidades que os apoiem quando surgem dúvidas. Recomentações são propostas tanto para as entidades empresariais quanto para a estrutura fiscal do país, visando auxiliar nos desafios que surgem com a implantação da nova estrutura fiscal de Angola.
Armando Carlos Hombo Nogueira; Luís Miguel Pacheco; Marcus Antonio Almeida Rodrigues; João Alexandre Lobo Marques. Influência da nova estrutura fiscal de impostos de Angola na gestão de escolas privadas do município do Lobito. Revista Gestão em Análise 2019, 8, 11 -30.
AMA StyleArmando Carlos Hombo Nogueira, Luís Miguel Pacheco, Marcus Antonio Almeida Rodrigues, João Alexandre Lobo Marques. Influência da nova estrutura fiscal de impostos de Angola na gestão de escolas privadas do município do Lobito. Revista Gestão em Análise. 2019; 8 (2):11-30.
Chicago/Turabian StyleArmando Carlos Hombo Nogueira; Luís Miguel Pacheco; Marcus Antonio Almeida Rodrigues; João Alexandre Lobo Marques. 2019. "Influência da nova estrutura fiscal de impostos de Angola na gestão de escolas privadas do município do Lobito." Revista Gestão em Análise 8, no. 2: 11-30.
The visual analysis of cardiotocographic examinations is a very subjective process. The accurate detection and segmentation of the fetal heart rate (FHR) features and their correlation with the uterine contractions in time allow a better diagnostic and the possibility of anticipation of many problems related to fetal distress. This paper presents a computerized diagnostic aid system based on digital signal processing techniques to detect and segment changes in the FHR and the uterine tone signals automatically. After a pre-processing phase, the FHR baseline detection is calculated. An auxiliary signal called detection line is proposed to support the detection and segmentation processes. Then, the Hilbert transform is used with an adaptive threshold for identifying fiducial points on the fetal and maternal signals. For an antepartum (before labor) database, the positive predictivity value (PPV) is 96.80% for the FHR decelerations, and 96.18% for the FHR accelerations. For an intrapartum (during labor) database, the PPV found was 91.31% for the uterine contractions, 94.01% for the FHR decelerations, and 100% for the FHR accelerations. For the whole set of exams, PPV and SE were both 100% for the identification of FHR DIP II and prolonged decelerations.
Joao Alexandre Lobo Marques; Paulo Cesar Cortez; Joao Paulo Do Vale Madeiro; Simon James Fong; Fernando Soares Schlindwein; Victor Hugo C. De Albuquerque. Automatic Cardiotocography Diagnostic System Based on Hilbert Transform and Adaptive Threshold Technique. IEEE Access 2019, 7, 73085 -73094.
AMA StyleJoao Alexandre Lobo Marques, Paulo Cesar Cortez, Joao Paulo Do Vale Madeiro, Simon James Fong, Fernando Soares Schlindwein, Victor Hugo C. De Albuquerque. Automatic Cardiotocography Diagnostic System Based on Hilbert Transform and Adaptive Threshold Technique. IEEE Access. 2019; 7 ():73085-73094.
Chicago/Turabian StyleJoao Alexandre Lobo Marques; Paulo Cesar Cortez; Joao Paulo Do Vale Madeiro; Simon James Fong; Fernando Soares Schlindwein; Victor Hugo C. De Albuquerque. 2019. "Automatic Cardiotocography Diagnostic System Based on Hilbert Transform and Adaptive Threshold Technique." IEEE Access 7, no. : 73085-73094.
The nonlinear analysis of biological time series provides new possibilities to improve computer aided diagnostic systems, traditionally based on linear techniques. The cardiotocography (CTG) examination records simultaneously the fetal heart rate (FHR) and the maternal uterine contractions. This paper shows, at first, that both signals present nonlinear components based on the surrogate data analysis technique and exploratory data analysis with the return (lag) plot. After that, a nonlinear complexity analysis is proposed considering two databases, intrapartum (CTG-I) and antepartum (CTG-A) with previously identified normal and suspicious/pathological groups. Approximate Entropy (ApEn) and Sample Entropy (SampEn), which are signal complexity measures, are calculated. The results show that low entropy values are found when the whole examination is considered, \(\hbox {ApEn}=0.3244\pm 0.1078\) and \(\hbox {SampEn}=0.2351\pm 0.0758\) (\(\hbox {average}\pm \hbox {standard}\) deviation). Besides, no significant difference was found between the normal (\(\hbox {ApEn}=0.3366\pm 0.1250\) and \(\hbox {SampEn}=0.2532\pm 0.0818\)) and suspicious/pathological (\(\hbox {ApEn}=0.3420\pm 0.1220\) and \(\hbox {SampEn}=0.2457\pm 0.0850\)) groups for the CTG-A database. For a better analysis, this work proposes a windowed entropy calculation considering 5-min window. The windowed entropies presented higher average values (\(\hbox {ApEn}=0.6505\pm 0.2301\) and \(\hbox {SampEn}=0.5290\pm 0.1188\)) for the CTG-A and (\(\hbox {ApEn}=0.5611\pm 0.1970\) and \(\hbox {SampEn}=0.4909\pm 0.1782\)) for the CTG-I. The changes during specific long-term events show that entropy can be considered as a first-level indicator for strong FHR decelerations (\(\hbox {ApEn}=0.1487\pm 0.0341\) and \(\hbox {SampEn}=0.1289\pm 0.0301\)), FHR accelerations (\(\hbox {ApEn}=0.1830\pm 0.1078\) and \(\hbox {SampEn}=0.1501\pm 0.0703\)) and also for pathological behavior such as sinusoidal FHR (\(\hbox {ApEn}=0.1808\pm 0.0445\) and \(\hbox {SampEn}=0.1621\pm 0.0381\)).
João Alexandre Lobo Marques; Paulo C. Cortez; João P. V. Madeiro; Victor Hugo C. de Albuquerque; Simon James Fong; Fernando S. Schlindwein. Nonlinear characterization and complexity analysis of cardiotocographic examinations using entropy measures. The Journal of Supercomputing 2018, 76, 1305 -1320.
AMA StyleJoão Alexandre Lobo Marques, Paulo C. Cortez, João P. V. Madeiro, Victor Hugo C. de Albuquerque, Simon James Fong, Fernando S. Schlindwein. Nonlinear characterization and complexity analysis of cardiotocographic examinations using entropy measures. The Journal of Supercomputing. 2018; 76 (2):1305-1320.
Chicago/Turabian StyleJoão Alexandre Lobo Marques; Paulo C. Cortez; João P. V. Madeiro; Victor Hugo C. de Albuquerque; Simon James Fong; Fernando S. Schlindwein. 2018. "Nonlinear characterization and complexity analysis of cardiotocographic examinations using entropy measures." The Journal of Supercomputing 76, no. 2: 1305-1320.
João Paulo Do Vale Madeiro; João Alexandre Lôbo Marques; Paulo César Cortez. Análise comparativa de desempenho das transformadas Wavelet e Hilbert na detecção do QRS em ECG. Revista Brasileira de Engenharia Biomédica 2009, 25, 153 -166.
AMA StyleJoão Paulo Do Vale Madeiro, João Alexandre Lôbo Marques, Paulo César Cortez. Análise comparativa de desempenho das transformadas Wavelet e Hilbert na detecção do QRS em ECG. Revista Brasileira de Engenharia Biomédica. 2009; 25 (3):153-166.
Chicago/Turabian StyleJoão Paulo Do Vale Madeiro; João Alexandre Lôbo Marques; Paulo César Cortez. 2009. "Análise comparativa de desempenho das transformadas Wavelet e Hilbert na detecção do QRS em ECG." Revista Brasileira de Engenharia Biomédica 25, no. 3: 153-166.
João Alexandre Lôbo Marques; Paulo César Cortez; Francisco Edson De Lucena Feitosa. Sistema inteligente para auxílio ao diagnóstico no monitoramento fetal eletrônico por análise de cardiotocografias. Revista Brasileira de Engenharia Biomédica 2008, 24, 91 -98.
AMA StyleJoão Alexandre Lôbo Marques, Paulo César Cortez, Francisco Edson De Lucena Feitosa. Sistema inteligente para auxílio ao diagnóstico no monitoramento fetal eletrônico por análise de cardiotocografias. Revista Brasileira de Engenharia Biomédica. 2008; 24 (2):91-98.
Chicago/Turabian StyleJoão Alexandre Lôbo Marques; Paulo César Cortez; Francisco Edson De Lucena Feitosa. 2008. "Sistema inteligente para auxílio ao diagnóstico no monitoramento fetal eletrônico por análise de cardiotocografias." Revista Brasileira de Engenharia Biomédica 24, no. 2: 91-98.