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 parametrization of autonomic nervous system

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

Long-term monitoring using wearable devices

Analysis of cardiovascular and respiratory information from wearable devices, such as the heart rate, its variability (HRV), respiratory rate, and tidal volume, and its application to cardiovascular and/or respiratory pathologies as well as wellbeing monitoring.

Coverage studies
Techniques for maximizing the coverage
Development and/or adaptation of methods for extracting cardiovascular and/or respiratory information from wearable devices
Applications that may benefit from a long-term monitoring, such as sleep studies, chronic respiratory diseases, daily monitoring of stress, ambulatory monitoring of depression, among others

Neural interfaces with the central nervous system to study movement

This line aims to develop new techniques to study the human motor nervous system in health and disease.

Design of new diagnostic and therapeutic solutions movement disorders or other neural conditions affecting motor function
Computational neural models to study brain-muscle interactions
Closed-loop brain- and spinal-cord stimulation platforms
Neural signal processing to study neural connectivity and coding during voluntary movements
Human-motor augmentation through peripheral neural interfaces
Human-machine interfaces
Blind-source separation-based techniques to extract neural information noninvasively from humans
Design and validation of plasticity induction paradigms

Genetics of cardiovascular risk

The most recent tools of genetic statistics and bioinformatics will be used to study the architecture and genetic predisposition to suffer cardiovascular diseases. The specific objectives are:

Identification and monitoring of genes associated with cardiovascular events, as well as different non-invasive markers of cardiovascular risk
Development of genetic risk markers that indicate the genetic predisposition to suffer a cardiovascular event

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).