Medical Technologies: Biosignal Analysis for Rehabilitation and Therapy
BIOART (Biosignal Analysis for Rehabilitation and Therapy) is a multidisciplinary research group made up of research engineers and physicians. It is a consolidated research group officially recognised by the Government of Catalonia (ref. 2017 SGR-1706) and is a member of the Biomedical Engineering Research Centre (CREB) of the Universitat Politècnica de Catalunya (UPC). It is also a member of the CIBER (Centre for Biomedical Network Research) on Bioengineering, Biomaterials and Nanomedicine (CIBERBBN).
Our research focuses on applying engineering techniques in medicine and healthcare (biomedical engineering, BME) to enhance rehabilitation processes and clinical therapies. In these fields, computerised support systems for treatment evaluation based on programmed packages have proven useful in improving the quality of medical services.
The techniques used in signal processing and recording systems are cutting edge, e.g. recording motor rehabilitation exercises using high-density electromyography (HD-EMG) signals.
- Use of modelling techniques and processing of biomedical systems and signals in the following areas:
- Neuromuscular: Developing quantitative tools based primarily on HD-EMG to monitor muscle activity and the tendency to fatigue, as well as evaluating different strategies for muscle activation during rehabilitation exercises.
- Neurological: Improving the detection and characterisation of brain activity and connectivity measured by electroencephalography for the evaluation of neurodegenerative diseases, as well as the interaction of drugs with brain activity and their effects when awake and asleep.
- Respiratory: Developing new tools to aid clinicians provide assisted ventilation for patients with severe respiratory failure, based on the modelling of the respiratory control system, lung diseases and mechanical ventilators, and monitoring of ventilatory control disorders with the study of respiratory muscles.
Neuromuscular: To create devices and methods for non-intrusive monitoring of physiological parameters.
- To develop new textile-based sensors to measure biopotentials using HD-EMG.
- To develop medical devices for motor rehabilitation of the upper arm based on HD-EMG.
- To develop a computer support system to predict rehabilitation outcomes in stroke patients based on HD-EMG
- To develop algorithms to decode individual neuronal activation from HD-EMG signals.
Neurological: To characterise the electrical characteristics of neuronal cells and tissues.
- To identify new biomarkers from neurophysiological data to aid in the presurgical evaluation of childhood refractory epilepsy and to understand the mechanisms of epileptic seizures.
- To assess cognitive function in patients with Rett syndrome by analysing electroencephalography (EEG) signals captured while carrying out cognitive functions.
- To characterise brain dynamics associated with different mental health disorders (schizophrenia, bipolar disorder, autism, etc.).
- To carry out a quantitative evaluation of sleep using spectral analysis and connectivity techniques that enable us to study the importance of sleep quality and how it interacts in different neurological diseases.
Respiratory: To analyse the dynamics of the cardiorespiratory system and develop simulation models for training on mechanical ventilation for clinical staff.
- To establish models of the dynamics of the cardiorespiratory system according to specific patients: personalised modelling.
- To simulate various respiratory diseases (obstructive, restrictive, or acute respiratory distress syndrome).
- To develop a computer system to manage mechanical ventilation and capable of simulating specific ventilated patient types.
- To develop virtual teaching and learning laboratories.
- To develop algorithms to estimate respiratory effort and mechanics.
Area/Field of expertise
The group has extensive experience in different research and translational projects in collaboration with companies and hospitals. The BIOART group coordinated the European project WOMEN UP within the framework of the H2020 programme from 2015 to 2019. The consortium included eight partners (two companies, three hospitals, two universities and a patient association) from six different countries. The project received funding of over 3 million euros. This research and innovation action covered the entire process of the design and transfer of a medical device based on electromyography, including system specifications, electronic and software development for the commercial prototype, CE marking, clinical trials in three European hospitals and the business strategy. Intellectual Property Rights and royalty agreements have been signed by the consortium for licensing.
We have an extremely active group, spearheading numerous competitive research and innovation projects with Tecnospring, the Ministry of Economic Affairs and Digital Transformation (MINECO), H2020, MAPFRE Foundation, BBVA Foundation, etc. We should highlight that BIOART has attained four consecutive MINECO projects focused on the development of innovative Information and Communications Technology (ICT) systems aimed at providing decision-making assistance for therapies and rehabilitation in neurological and neuromuscular diseases. In addition, the BIOART group has established close ties with various national and international groups in biomedical research. These collaborations have resulted in several joint publications, in conjunction with, for example, the Center for Brain Health at New York University (USA), Montreal National Institute (MNI) at McGill University (Canada), Lisin Bioengineering Centre at the University of Turin (Italy), Clinical Engineering and Bioinstrumentation Research Group at the University of Antioquia (Colombia), Department of Bioengineering at Imperial College London (UK), etc.
- Migliorelli C, Romero S, Bachiller A, Aparicio J, Alonso Lopez JF, Mañanas MA and San Antonio-Arce MV Improving the ripple classification in focal pediatric epilepsy: identifying pathological high-frequency oscillations by Gaussian mixture model clustering. JOURNAL OF NEURAL ENGINEERING . 18(4): .
- Marateb HR, Tasdighi Z, Mohebian MR, Naghavi A, Hess M, Motlagh ME, Heshmat R, Mansourian M, Mañanas MA, Binder H and Kelishadi R Classification of psychiatric symptoms using deep interaction networks: the CASPIAN-IV study. SCIENTIFIC REPORTS . 11(1): 15706-15706.
- Tost A, Migliorelli C, Bachiller A, Medina-Rivera IF, Romero S, Garcia-Cazorla A and Mañanas MA Choosing Strategies to Deal with Artifactual EEG Data in Children with Cognitive Impairment ENTROPY . 23(8): 1030.
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