Abstract
Modeling Heart Rate Variability (HRV) data has become important for clinical applications and as a research tool. These data exhibit long memory and time-varying conditional variance (volatility). In HRV, volatility is traditionally estimated by recursive least squares combined with short memory AutoRegressive (AR) models. This work considers a parametric approach based on long memory Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with heteroscedastic errors. To model the heteroscedasticity nonlinear Generalized Autoregressive Conditionally Heteroscedastic (GARCH) and Exponential Generalized Autoregressive Conditionally Heteroscedastic (EGARCH) models are considered. The latter are necessary to model empirical characteristics of conditional volatility such as clustering and asymmetry in the response, usually called leverage in time series literature. The ARFIMA-EGARCH models are used to capture and remove long memory and characterize conditional volatility in 24 hour HRV recordings from the Noltisalis database.

Abstract
The wireless capsule endoscopy has revolutionized early diagnosis of small bowel diseases. However, a single examination has up to 10 h of video and requires between 30–120 min to read. Computational methods are needed to increase both efficiency and accuracy of the diagnosis. In this paper, an evaluation of deep learning U-Net architecture is presented, to detect and segment red lesions in the small bowel. Its results were compared with those obtained from the literature review. To make the evaluation closer to those used in clinical environments, the U-Net was also evaluated in an annotated sequence by using the Suspected Blood Indicator tool (SBI). Results found that detection and segmentation using U-Net outperformed both the algorithms used in the literature review and the SBI tool. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
Several Heart Rate Variability (HRV) based novel methodologies for describing heart rate dynamics have been proposed in the literature with the aim of risk assessment. One such methodology is ARFIMA-EGARCH modeling which allows the quantification of long range dependence and time-varying volatility with the aim of describing non-linear and complex characteristics of HRV. This study applies the ARFIMA-EGARCH modeling of HRV recordings from 30 patients of the Noltisalis database to investigate the discrimination power of a set of features comprising currently used linear HRV features (low and high frequency components) and new measures obtained from the modeling such as, long memory in the mean, and persistence and asymmetry in volatility. A subset of the multidimensional HRV features is selected in a two-step procedure using Principal Components Analysis (PCA). Additionally, supervised classification by quadratic discriminant analysis achieves 93.3% of discrimination accuracy between the groups using the new feature set created by PCA.

Abstract
This work focus on detection of diseases from Heart Rate Variability (HRV) series using Long Short-Term Memory (LSTM) networks. First, non-linear models are used to extract sequences of features that characterize the HRV series. These time sequences are then used as input for the LSTM. HRV recordings from the Noltisalis database are used for training and testing this approach. The results indicate that the procedure provides accuracy scores in the range of 86.7% to 90.0% on the test set.

Abstract
The concept of health indicates physical, mental and social well-being. Psychological stress is commonly present among freshmen due to social and environmental changes. An approach to study the impact of stress on students relied on biological data assessment. In this work, electrocardiogram signals from first year students, from the Biomedical Engineering course, were collected during an oral presentation, acquiring the RR time series. Linear and nonlinear methodologies are used to extract features that best characterize the RR time series in young subjects under stress conditions.

Abstract
Resumo A ana´lise de sinais biome´dicos desempenha, atualmente, um papel fundamental no auxi´lio ao diagno´stico cli´nico. Os avanc¸os cienti´ficos multiplicam-se a um ritmo frene´tico numa sociedade cada vez mais consciente e desejosa de soluc¸o~es para os problemas de sau´de. Este trabalho pretende dar uma perspetiva histo´rica dos avanc¸os no estudo dos sinais biome´dicos e refletir a sua importa^ncia na sociedade.Palavras-chave: Sinais Biome´dicos; diagno´stico cli´nico; avanc¸os Abstract Currently the biomedical signal analysis plays a fundamental role in helping clinical diagnosis. Scientific advances multiply at a frenetic pace in a society that is increasingly aware of and desirous of solutions to health problems. The need for constant updating of the means of clinical diagnosis is necessary to meet the demands of the world today. This work intends to give a historical perspective of advances in the biomedical signal study and to reflect its importance in society. Keywords: Biomedical Signal analysis, clinical diagnosis, scientific advances