2016
Autores
Leite, A; Silva, ME; Rocha, AP;
Publicação
2016 38TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
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.
2018
Autores
Leite, Argentina; Silva, MariaEduarda; Rocha, AnaPaula;
Publicação
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
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.
2020
Autores
Leite, A; Silva, ME; Rocha, AP;
Publicação
2020 11TH CONFERENCE OF THE EUROPEAN STUDY GROUP ON CARDIOVASCULAR OSCILLATIONS (ESGCO): COMPUTATION AND MODELLING IN PHYSIOLOGY NEW CHALLENGES AND OPPORTUNITIES
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.
2019
Autores
Sampaio, P; Leite, A; Pereira, LT; Martinez, JP; Vasconcelos Raposo, J;
Publicação
2019 6TH IEEE PORTUGUESE MEETING IN BIOENGINEERING (ENBENG)
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.
2019
Autores
Leite, A; Pereira, LT; Ricardo, S;
Publicação
História da Ciência e Ensino: construindo interfaces
Abstract
2021
Autores
Soares, AA; Carvalho, FA; Leite, A;
Publicação
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
Abstract
In this paper, a numerical study is conducted to investigate the influence of imposed inlet velocity profile on the hemodynamics in the region of the abdominal aorta bifurcation, for a patient specific. The influences of two different inflow velocity profiles on the hemodynamics of the abdominal aorta bifurcation have been investigated. The simulations were carried out under the same conditions changing only the shape of the inlet velocity profile in abdominal aorta. The simulations were performed with a parabolic profile (PP) and a uniform profile (UP) to quantify the hemodynamic differences between them, in the arterial regions, that is, in the upstream bifurcation, in the bifurcation and downstream bifurcation in each of the common iliac arteries. The results reported provide fundamental knowledge to a better understand of the inflow velocity profiles influence in the hemodynamics of the abdominal aorta bifurcation, such as the distribution of the velocity, pressure and wall shear stress (WSS), as well as the distribution of the stress hemodynamic descriptors on the artery wall. The results highlighted that the influence of the inlet velocity profiles in the time-averaged wall shear stress (AWSS) and relative residence time (RRT) is not significant after the abdominal aorta bifurcation. In general, for the hemodynamic descriptors studied, the correlation between the results obtained with the two velocity profiles reaches values close to 1 in the iliac arteries, in contrast to the abdominal region, where the correlation is less than 0.6.
The access to the final selection minute is only available to applicants.
Please check the confirmation e-mail of your application to obtain the access code.