Group: Biomedical signal processing and interpretation
Group leader: Raimón Jané (rjane@ibecbarcelona.eu)
Among patients with chronic obstructive pulmonary disease, asthma is one of the most prevalent pathology. Patients with asthma have poor quality of life and consume many healthcare resources. The effect of asthma is characterized by persistent airflow limitation and is classically assessed by spirometry studying the bronchodilator response.
However, this classical approach does not permit an accurate estimation of asthmatic patient severity, assessment of drug response or early detection of acute exacerbations. Therefore, there is a need to develop new methods to identify and assess patients with asthma. These include the analysis of the activity of respiratory muscles, and the analysis of respiratory sounds, for the detection and characterization of continuous adventitious respiratory (CAS) sounds.
The aim of this study is to propose and develop novel multimodal physiological biomarkers (MPBs), estimated by signal processing algorithms to detect and characterize CAS, such as wheezes, which can differentiate asthmatic patients with and without significant bronchodilator response (BDR). On the other hand, non-invasive monitoring of these MPBs, including respiratory muscle activity, will be developed for mHealth tools in platforms including wearables and smartphones devices.
To achieve this objective, it will be used multimodal sensors (biomechanical, bioelectrical and bioacoustic), health patches and built-in sensors of smartphones, to acquire biomedical signals, related to muscle, respiratory and cardiac activity. These big amount of health data will be analysed by advanced signal processing and interpretation, including personalised modelling, machine learning and novel deep learning techniques for different patient’s health condition.
The project will be based on recent contributions to novel MPB, developed in our laboratory (Lozano et al. PlosOne 2017; Lozano et al. JBHI 2016, Lozano et al. Signal Processing 2016) and will be adapted and improved for non-invasive monitoring of asthmatic patients using mHealth tools.
The study will be carried out in collaboration with international and national scientific and clinical groups.
