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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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Publications

2021

The Entropy Universe

Authors
Ribeiro, M; Henriques, T; Castro, L; Souto, A; Antunes, L; Costa Santos, C; Teixeira, A;

Publication
Entropy

Abstract
About 160 years ago, the concept of entropy was introduced in thermodynamics by Rudolf Clausius. Since then, it has been continually extended, interpreted, and applied by researchers in many scientific fields, such as general physics, information theory, chaos theory, data mining, and mathematical linguistics. This paper presents The Entropy Universe, which aims to review the many variants of entropies applied to time-series. The purpose is to answer research questions such as: How did each entropy emerge? What is the mathematical definition of each variant of entropy? How are entropies related to each other? What are the most applied scientific fields for each entropy? We describe in-depth the relationship between the most applied entropies in time-series for different scientific fields, establishing bases for researchers to properly choose the variant of entropy most suitable for their data. The number of citations over the past sixteen years of each paper proposing a new entropy was also accessed. The Shannon/differential, the Tsallis, the sample, the permutation, and the approximate entropies were the most cited ones. Based on the ten research areas with the most significant number of records obtained in the Web of Science and Scopus, the areas in which the entropies are more applied are computer science, physics, mathematics, and engineering. The universe of entropies is growing each day, either due to the introducing new variants either due to novel applications. Knowing each entropy’s strengths and of limitations is essential to ensure the proper improvement of this research field.

2020

Preoperative Blood Pressure Complexity Indices as a Marker for Frailty in Patients Undergoing Cardiac Surgery

Authors
Rangasamy, V; Henriques, TS; Xu, XL; Subramaniam, B;

Publication
Journal of Cardiothoracic and Vascular Anesthesia

Abstract
Objective: Frailty, a state of decreased physiological reserve, increases the risk of adverse outcomes. There is no standard tool for frailty during perioperative period. Autonomic dysfunction, an underlying process in frailty, could result in hemodynamic fluctuations. Complexity, the physiological adaptability of a system can quantify these fluctuations. The authors hypothesized that complexity could be a marker for frailty and explored their relationship in cardiac surgical patients. Design: Prospective, observational study. Setting: Single-center teaching hospital. Participants: Three hundred and sixty-four adult patients undergoing cardiac surgery. Intervention: None. Measurements and Main Results: Preoperative beat-to-beat systolic arterial pressure (SAP) and mean arterial pressure (MAP) time series were obtained. Complexity indices were calculated using multiscale entropy (MSE) analysis. Frailty was assessed from: age >70 years, body mass index <18.5, hematocrit <35%, albumin <3.4 g/dL, and creatinine >2.0 mg/dL. The association between complexity indices and frailty was explored by logistic regression and predictive ability by C-statistics. In total, 190 (52%) patients had frailty. The complexity index (MSES) median (quartile 1, quartile 3) of SAP and MAP time series decreased significantly in frail patients (SAP: 8.32 [7.27, 9.24] v 9.13 [8.00, 9.72], p < 0.001 and MAP: 8.56 [7.56; 9.27] v 9.18 [8.26; 9.83], p < 0.001). MSE S demonstrated a fair predictive ability of frailty (C-statistic: SAP 0.62 and MAP 0.64). Conclusion: Preoperative BP complexity indices correlate and predict frailty. Impaired autonomic control is the underlying mechanism to explain this finding. A simple automated measure of preoperative BP complexity in the surgeon's office has the potential to reliably assess frailty. © 2019 Elsevier Inc.

2020

Changes in nonlinear dynamic complexity measures of blood pressure during anesthesia for cardiac surgeries using cardio pulmonary bypass

Authors
Rangasamy, V; Henriques, TS; Mathur, PA; Davis, RB; Mittleman, MA; Subramaniam, B;

Publication
Journal of Clinical Monitoring and Computing

Abstract
Nonlinear complexity measures computed from beat-to-beat arterial BP dynamics have shown associations with standard cardiac surgical risk indices. They reflect the physiological adaptability of a system and has been proposed as dynamical biomarkers of overall health status. We sought to determine the impact of anesthetic induction and cardiopulmonary bypass (CPB) upon the complexity measures computed from perioperative BP time series. In this prospective, observational study, 300 adult patients undergoing cardiac surgery were included. Perioperative period was divided as: (1) Preoperative (PreOp); (2) ORIS—induction to sternotomy; (3) ORSB- sternotomy to CPB; (4) ORposB—post CPB and within 30 min before leaving OR and (5) postoperative phase (PostOp)—initial 30 min in the cardiac surgical intensive care unit. BP waveforms for systolic (SAP), diastolic (DAP), mean arterial pressure (MAP) and pulse pressure (PP) were recorded, and their corresponding complexity index (MSE?) was calculated. Significant decrease in MSE? from Preop to PostOp phases was observed for all BP time series. Maximum fall was seen during post anesthetic induction (ORIS) phase. Mild recovery during the subsequent phases was observed but they never reached the baseline values. In an exploratory analysis, preoperative MSE? showed a significant correlation with postoperative length of ICU stay. Blood pressure complexity varies at different time points and is not fixed for a given individual. Preoperative BP Complexity decreased significantly following anesthetic induction and did not recover to baseline until 30 min after surgery. Prevention of this significant fall may offer restoration of MSE? throughout surgery. Furthermore, preoperative BP complexity needs to be explored as a predictor of major postoperative adverse events by itself or in addition with the current risk indices. © 2019, Springer Nature B.V.

2018

Complexity of preoperative blood pressure dynamics: possible utility in cardiac surgical risk assessment

Authors
Henriques, TS; Costa, MD; Mathur, P; Mathur, P; Davis, RB; Mittleman, MA; Khabbaz, KR; Goldberger, AL; Subramaniam, B;

Publication
Journal of Clinical Monitoring and Computing

Abstract

2018

Comparison of Invasive and Noninvasive Blood Pressure Measurements for Assessing Signal Complexity and Surgical Risk in Cardiac Surgical Patients

Authors
Gibson, LE; Henriques, TS; Costa, MD; Davis, RB; Mittleman, MA; Mathur, P; Subramaniam, B;

Publication
Anesthesia & Analgesia

Abstract