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About

About

I'm a postdoc researcher at the Centro de Sistemas de Computação Avançada (CRACS) of INESCTEC, in Porto. In 2015 I received my PhD in Clinical Health Services Research from the Faculty of Medicine, University of Porto, with a thesis entitled Assessing Complexity of Physiological Interactions. In 2010 I obtained M.Sc. degree in Mathematical Engineering, from the Faculty of Science, University of Porto.

From 2012 to 2015 I worked as a Pre/Post-Doctoral at  the  Wyss  Institute  for  Biologically  Inspired  Engineering, Harvard   Medical   School,   Boston,   USA under supervison of Dr. Ary Goldberger and Madalena Costa. In 2015-2016 I worked has a research  fellow  at Center  for  Anesthesia  Research  Excellence  (CARE), Beth  Israel Deaconess  Medical  Center,  Boston,  USA (Supervisors:  Ary  L.  Goldberger,MD  and Balachundhar Subramaniam, MD, MPH).

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Details

Details

  • Name

    Teresa Sarmento Henriques
  • Cluster

    Computer Science
  • Role

    Researcher
  • Since

    01st October 2014
001
Publications

2016

Multiscale Poincare plots for visualizing the structure of heartbeat time series

Authors
Henriques, TS; Mariani, S; Burykin, A; Rodrigues, F; Silva, TF; Goldberger, AL;

Publication
BMC MEDICAL INFORMATICS AND DECISION MAKING

Abstract
Background: Poincare delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincare (MSP) plots. Methods: Starting with the original RR time series, the method employs a coarse-graining procedure to create a family of time series, each of which represents the system's dynamics in a different time scale. Next, the Poincare plots are constructed for the original and the coarse-grained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency. Results: We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation. Conclusions: This generalized multiscale approach to Poincare plots may be useful in visualizing other types of time series.

2016

Analysis of the sleep EEG in the complexity domain

Authors
Mariani, S; Borges, AFT; Henriques, T; Thomas, RJ; Leistedt, SJ; Linkowski, P; Lanquart, J; Goldberger, AL; Costa, MD;

Publication
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Abstract

2016

Sublimation-like behavior of cardiac dynamics in heart failure: A malignant phase transition?

Authors
Goldberger, AL; Henriques, TS; Mariani, S;

Publication
Complexity

Abstract

2015

Derivation of a clinical decision rule for predictive factors for the development of pharyngocutaneous fistula postlaryngectomy

Authors
Cecatto, SB; Monteiro Soares, M; Henriques, T; Monteiro, E; Ferreira Pinto Moura, CIFP;

Publication
BRAZILIAN JOURNAL OF OTORHINOLARYNGOLOGY

Abstract
Introduction: Pharyngocutaneous fistula after larynx and hypopharynx cancer surgery can cause serveral damages. This study's aim was to derive a clinical decision rule to predict pharyngocutaneous fistula development after pharyngolaryngeal cancer surgery. Methods: A retrospective cohort study was conducted, including all patients performing total laryngectomy/pharyngolaryngectomy (n = 171). Association between pertinent variables and pharyngocutaneous fistula development was assessed and a predictive model proposed. Results: American Society of Anesthesiologists scale, chemoradiotherapy, and tracheotomy before surgery were associated with fistula in the univariate analysis. In the multivariate analysis, only American Society of Anesthesiologists maintained statistical significance. Using logistic regression, a predictive model including the following was derived: American Society of Anesthesiologists, alcohol, N-classification, and diabetes mellitus. The model's score area under the curve was 0.76 (95% CI 0.64-0.87). The high-risk group presented specificity of 93%, positive likelihood ratio of 7.10, and positive predictive value of 76%. Including the medium-low, medium-high, and high-risk groups, a sensitivity of 92%, negative likelihood ratio of 0.25, and negative predictive value of 89% were observed. Conclusion: A clinical decision rule was created to identify patients with high risk of pharyngocutaneous fistula development. Prognostic accuracy measures were substantial. Nevertheless, it is essential to conduct larger prospective studies for validation and refinement.

2015

Remembrance of time series past: simple chromatic method for visualizing trends in biomedical signals

Authors
Burykin, A; Mariani, S; Henriques, T; Silva, TF; Schnettler, WT; Costa, MD; Goldberger, AL;

Publication
PHYSIOLOGICAL MEASUREMENT

Abstract
Analysis of biomedical time series plays an essential role in clinical management and basic investigation. However, conventional monitors streaming data in real-time show only the most recent values, not referenced to past dynamics. We describe a chromatic approach to bring the 'memory' of the physiologic system's past behavior into the current display window. The method employs the estimated probability density function of a time series segment to colorize subsequent data points. For illustrative purposes, we selected open-access recordings of continuous: (1) fetal heart rate during the pre-partum period, and (2) heart rate and systemic blood pressure from a critical care patient during a spontaneous breathing trial. The colorized outputs highlight changes from the 'baseline' reference state, the latter defined as the mode value assumed by the signal, i.e. the maximum of its probability density function. A colorization method may facilitate the recognition of relevant features of time series, especially shifts in baseline dynamics and other trends (including transient and longer-term deviation from baseline values) which may not be as readily noticed using traditional displays. This method may be applicable in clinical monitoring (real-time or off-line) and in research settings. Prospective studies are needed to assess the utility of this approach.