Short bio and research interests

Flavia Maria Guerra S. A. Oliveira is an Associate Professor at the Department of Electrical Engineering (ENE) at the University of Brasilia (UnB). She earned her undergraduate degree in electrical engineering at the University of Brasilia. She obtained her M.Sc. degree from the University of Campinas (Unicamp) in Electrical Engineering, School of Electrical and Computer Engineering (FEEC), and her M.Sc. and Ph.D. degrees in biomedical engineering from the University of Southern California (USC), Department of Biomedical Engineering (BME). She worked in the Cardiorespiratory Sleep Laboratory at USC. She is a member of the IEEE Engineering in Medicine and Biology Society (EMBS), of the Brazilian Society of Biomedical Engineering (SBEB) and of the American Thoracic Society (ATS). 

Research interests

Type 2 diabetes mellitus (T2DM) is a growing global health concern, strongly linked to heightened cardiovascular risk. This association is driven by complications such as cardiovascular autonomic neuropathy (CAN), a condition that disrupts autonomic control of the heart and blood vessels. Research indicates that CAN increases mortality risk by approximately 3.45 times in T2DM patients, highlighting its role as a critical factor in cardiovascular complications.

CAN is characterized by dysfunction in the autonomic nerves responsible for regulating heart rate and vascular compliance, impairing the cardiovascular system’s ability to adapt to various physiological stimuli. This autonomic dysfunction elevates the risk of arrhythmias, silent myocardial infarction, and sudden cardiac death in diabetic individuals. Additionally, autonomic dysfunction in peripheral vascular regulation contributes to chronic hypoperfusion of the lower limbs, a critical factor in the development and poor healing of diabetic foot ulcers (DFUs), significantly impacting patients’ quality of life.

Early detection of autonomic dysfunction in T2DM is vital for effective management. Within this context, prof. Flavia Maria G. S. A. Oliveira’s research focuses on applying engineering techniques—such as computational modeling, digital signal processing, and machine learning—to study cardiorespiratory and cardiovascular systems, aiming to identify  quantitive markers of the autonomic nervous system for early detection of autonomic dysfunction in T2DM. These noninvasive metrics, derived from electrocardiogram (ECG), continuous blood pressure, photoplethysmography (PPG) and respiration signals, facilitate early detection before severe cardiovascular complications arise. Her research also explores these markers for long-term monitoring, risk stratification, and targeted interventions in T2DM. 

By integrating statistical approaches and machine learning, her work seeks to identify the most discriminative autonomic markers for the early detection of type 2 diabetes (T2DM) and its autonomic complications, enabling more accurate and timely diagnoses. These findings not only enhance understanding of the mechanisms underlying autonomic dysfunction but also inform the development of personalized preventive and therapeutic interventions in clinical practice. Ultimately, this work aims to reduce morbidity and improve the quality of life for diabetic patients.

Early identification of autonomic dysfunction early allows for timely interventions, including lifestyle modifications, pharmacological treatments, and personalized management strategies. These steps can mitigate cardiovascular risks, improve clinical outcomes, and reduce mortality. By integrating dynamic cardiorespiratory coupling, Oliveira’s work not only advances T2DM management but also extends to broader applications, such as understanding autonomic-metabolic interactions in conditions like sleep apnea. Early intervention is key to breaking the cycle of T2DM-related cardiovascular decline.