Invasive, non-invasive, and contactless measurements are now becoming the standard for patient monitoring, both in clinical and non-clinical environments. Lately, exemplary applications of engineered solutions on a large variety of biomedical signals measuring the heart cycle (e.g. the electrocardiogram, phonocardiogram, seismocardiogram, photoplethysmogram), blood pressure (both invasive and non-invasive measurements), respiratory volume and flow, pupil dilation, electrodermal activity, among others, have been collected and analyzed for basic patient monitoring both within a clinical context (e.g. clinical health records) and for the development of advanced approaches for sport, work, and affective states monitoring in non-clinical environments, to name a few examples. Notably, most of these contexts are starting to be powered by the application and development of artificial intelligence tools.
This special issue is aimed at collecting original research articles, reviews, and any specific contributions highlighting the effectiveness of advanced cardiovascular and respiratory data processing and monitoring tools, stressing at the same time the importance of physiological modeling and systems understanding, as well as improving interpretability and explainability of results extracted from artificial intelligence algorithms using biomedical information.
Only high-quality and original contributions will be considered. Topics for this Special Issue include (not limited to):
· Novel signal processing strategies for cardiopulmonary and physiology-based engineering.
· Novel methods for modeling human cardiovascular and cardiorespiratory physiology.
· Development or adaptation of advanced signal processing tools, multivariate cardiovascular models, modeling strategies including invasive, non-invasive, and/or contactless monitoring approaches.
· Application of signal processing methods for clinical monitoring applications.
· Benchmarking and innovative solutions employing artificial intelligence tools, deep and reinforcement learning algorithms, or any strategy applied to large clinical datasets.
· New tools for classification, prediction and/or feature extraction procedures applied to cardiovascular and cardiorespiratory studies in computational, experimental, and clinical set-ups.
· New benchmarks and literature reviews highlighting advanced applications of artificial intelligence tools in modeling biomedical data and/or gathering a significant understanding of patients’ pathology.
In addition to the proposed list, any study employing new or existing tools that bring new perspectives to the cardiopulmonary and physiology-based engineering field is more than welcome.
- Cardiovascular and respiratory signal processing
- Cardiovascular and respiratory system modeling
- Cardiovascular and respiratory Pathologies
- Cardiovascular and respiratory monitoring devices
Riccardo Barberi, Ph.D., Politecnico di Milano
Deadline: October 15th, 2023