Research
The research activities at the BIT lab of the University of Palermo follow two main directions: the development of analytical and computational methods rooted in information theory for the analysis of physiological networks, and the utilization of biosensors and signal processing tools for the acquisition and monitoring of biosignals.
Information dynamics of Network Systems
The representation of complex systems as networks whose units are functionally connected is ubiquitous in many fields of science, ranging from climatology to econometrics and the study of brain and physiological systems. At BIT lab we develop tools rooted in the general framework of information theory to quantify how information is generated at each node of a complex network, transferred between pairs of nodes, and shared in redundant and synergistic ways among several nodes. Applied to both discrete and continuous time processes studied in the time or frequency domains, we use these tools to describe how network function emerges from network structure and changes across diverse states and conditions.
Main publications:
L Faes et al., A new framework for the time- and frequency-domain assessment of high-order interactions in networks of random processes, IEEE Transactions on Signal Processing, 2022
C. Barà et al, Comparison of entropy rate measures for the evaluation of time series complexity: Simulations and application to heart rate and respiratory variability, Biocybernetics and Biomedical Engineering 2024
Multi-System Analysis of the Human Physiological Network
Based on the view of the human body as an integrated network composed by several organ systems, each with its own internal dynamics but also functionally connected to the other organs, we apply novel methods for multivariate time series analysis to the nonlinear, multi-scale and time-variant output signals of brain, heart and peripheral physiological systems. This integrated unconventional approach aims at providing new insight on the functional structure of the human physiological networks and on its evolution across different physiological states and pathological conditions.
Main publications:
G. Mijatovic et al, Assessing High-Order Links in Cardiovascular and Respiratory Networks via Static and Dynamic Information Measures, IEEE Open Journal of Engineering in Medicine and Biology 2024
L Sparacino et al, 'A method to assess Granger causality, isolation and autonomy in the time and frequency domains: theory and application to cerebrovascular variability', IEEE Transactions on Biomedical Engineering 2024
Brain-Heart interactions
The brain and the heart are complex systems, whose interaction shows emergent properties that cannot be highlighted by the analysis of each system independently. While clinical evidence points to the functional interplay between brain and body, a solid comprehensive data modelling/processing framework linking the very different physiological dynamics is still missing. We use electroencephalographic (EEG) and electrocardiographic (ECG) data to measure brain and heart activities and we develop and apply data-driven methodologies to identify brain-heart interactions in several different physiological conditions (e.g. enteroception and sleep stages).
Main publications:
C Barà et al, Local and global measures of information storage for the assessment of heartbeat-evoked cortical responses, Biomedical Signal Processing and Control 2023
R. Pernice et al, Pairwise and higher-order measures of brain-heart interactions in children with temporal lobe epilepsy, Journal of Neural Engineering, 2022
Brain connectivity
Brain connectivity refers to the intricate network of connections that exist within the human brain. It is a fundamental aspect of neuroscience and is crucial for understanding how the brain processes information, performs various functions, and gives rise to cognition and behavior. Among the several definitions of brain connectivity, the concept of functional connectivity is the most known and spread in the literature. It is based on the idea that regions of the brain that are active together, even though anatomically, are likely to be functionally connected. We use functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data to measure brain activity and identify patterns of synchronous neural activity.
Main publications:
Y. Antonacci et al, Measuring Connectivity in Linear Multivariate Processes with Penalized Regression Techniques, IEEE Access 2024
G Mijatovic et al, An information-theoretic framework to measure the dynamic interaction between neural spike trains, IEEE Transactions on Biomedical Engineering 2021
Automatic classification of physiopathological states
In recent years, the application of machine learning (ML) algorithms to the analysis of medical data has increased significantly in healthcare research and industry. ML algorithms have been used to help classify physiological and pathological states related to, for example, different types of stress. The use of time series in data analysis allows the temporal dynamics of pathophysiological states to be captured, enabling machine learning models to identify hidden correlations and complex patterns. These algorithms can thus provide important decision support tools for the early diagnosis, monitoring, and treatment of stress-related disorders, enabling more effective and personalised management of patient well-being.
Main publications:
M Iovino, et al, Classification of Physiological States through Machine Learning Algorithms Applied to Ultra-Short-Term Heart Rate and Pulse Rate Variability Indices on a single-feature basis. 16th Med. Conf. on Med. Biol. Eng. COmput. (MEDICON), 2023.
M Iovino et al, Comparison of Machine Learning Approaches for Physiological States Classification Using Heart Rate and Pulse Rate Variability Indices, Proc. GNB 2023
Monitoring and processing of biosignals
This activity focuses on the extraction of relevant information about vital signs, biological rhythms and markers of the physiological state from multiple biomedical signals acquired from portable or minimally invasive wearable devices. The analysis of electrocardiographic, photoplethysmographic and breathing signals is performed to assess the psychophysical state of subjects monitored in different experimental conditions. This approach is employed to assess changes in the vital signs related to altered psychophysiological states, including among others the response to physiological stressors or emotions.
Main Publications:
G. Volpes et al., Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements, Biosensors, 2024
S. Valenti et al, Wearable multisensor ring-shaped probe assessing stress and blood oxygenation: design and preliminary measurements, Biosensors, 2023