Research lines

BSICoS focuses its activity on the Processing, Interpretation and Computer Simulation of Biomedical Signals.

BSICoS: Biomedical Signal Interpretation and Computational Simulation

The main objective of the group is the development of methods for biomedical signal processing, driven by the physiology, for personalized interpretation (diagnosis, prognosis and therapy) of the conditions of the cardiovascular, respiratory and autonomic nervous systems and their interactions.

The goal is to improve the impact of ICTs in health and further understanding the functioning of biological systems that can be observed through noninvasive signals.

Key to this is working with clinical teams and research groups that combine the experiences of the two areas, direct research to solve relevant clinical problems and facilitate the transfer of results to clinical practice.

All this is embodied in five application domains that constitute the lines of work of the group:

Modeling and simulation of cardiac electrophysiology

Modeling and simulation of cardiac electrical activity in healthy and diseased hearts, including:

  • Investigation of atrial arrhythmias: atrial activation and manifestations on electrogram (EGM) and electrocardiogram (ECG) signals
  • Investigation of ventricular arrhythmias: mechanisms underlying the onset of reentrant arrhythmias and assessment of pro-arrhythmic risk based on ECG biomarkers
  • Dynamic modeling of myocardial ischemia
  • Stochastic modeling of ionic current fluctuations in ventricular cells: contribution to temporal and spatial
  • Repolarization variability

Noninvasive ECG markers based on the conditions for characterization and identification of arrhythmic risk

Diagnosis, monitoring, quantification and risk stratification of cardiac dysfunctions from automatic analysis of multilead ECG, including:

  • Automatic ECG wave delineation and characterization
  • Detection and monitoring of cardiac ischemia
  • Repolarization alternans detection, as an arrhythmic risk indicator
  • Study of the relationship between repolarization duration (QT interval) and the heart rhythm, together with their dynamic interactions, as an arrhythmic risk indicator

Signal processing of intracardiac electrogram (EGM) to improve planning and therapy of cardiac interventions

Processing of intracardiac electrogram (EGM) signals acquired by implantable cardioverter-defibrilator (ICDs) or in the catheterization laboratory:

  • Investigation of atrial arrhythmias: atrial activation and manifestations on electrogram (EGM) and electrocardiogram (ECG) signals
  • Investigation of new methods for guiding atrial fibrillation ablation, using regularity, synchronization and propagation information extracted from EGM signals
  • Signal processing to assist the finding of ectopic foci during ventricular ablation
  • Automatic activation detection for cardiac mapping
  • Analysis of repolarization alternans in intracardiac electrograms

Non-invasive evaluation of the activity of the autonomic nervous system (ANS) quantification

Non-invasive parametrization of autonomic nervous system activity through the analysis of the dynamics and interactions of signals derived from electrocardiogram (ECG), blood pressure (BP), and pulse photoplethysmography signals (PPG), such as heart rate variability (HRV), BP variability and pulse transit time (PTT). Applications:

  • Analysis and modeling of HRV during exercise
  • Detection of drowsiness in drivers
  • Identification of stress, emotional states and effects of music
  • Discrimination of patients resistant or prone to suffer hypotension during hemodialysis
  • Prediction of hypotension during spinal anesthesia in cesarean delivery

Processing and characterization of biomedical signals in respiratory diseases

Obstructive Sleep Apnea Syndrome diagnosis and monitoring based on pulse photoplethysmography signal (PPG)

  • Detection of sympathetic activations associated with apnea
  • Study of the relationship between sympathetic activations and apnea based on Autonomic Nervous System analysis from HRV, PTT and PRV
  • Deriving respiration from PPG

Experimental characterization and in-vitro modeling of cardiac aging

Experimental characterization and in-vitro modeling of human cardiac aging for:

  • Temporal and spatial variability characterization of the electrophysiology of the human ventricle based on age
  • Studying of structural, molecular and autonomic changes in human ventricles induced by aging
  • Creating, characterizing and validating an aging in-vitro model based on human iPSC
  • Searching for anti-aging targets at molecular level which explain electrophysiological changes linked to age
  • Analyzing the effects of aging on congenital cardiomiopathies

The lines of research addressed by the Group pursued basic scientific and technological progress through the development of algorithms and methods to improve diagnosis, prognosis and therapy application in cardiorespiratory and autonomic nervous system diseases, as well as a better understanding of physiology pathological or not pathological situations (drowsiness, stress, microgravity, etc).

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