The consortium

PTB’s division on “Medical Physics and Metrological IT” works on a wide range of medically relevant measurement techniques including biosignal measurements and data analysis involving cooperation with many clinical participants. The department of “Mathematical Modelling and Data Analysis” at PTB has more than 30 scientists including 9 PhD students with expertise in mathematical modelling and statistical data analysis and is viewed as one of the leading groups in uncertainty quantification for metrology. PTB will coordinate this project and participate in all technical WPs. Further to this, PTB has experience in coordinating previous medical related EMPIR projects (JRP 18HLT07).

CMI as a notified body provides a broad range of services in fundamental and legal metrology such as calibrations of standards and measuring instruments as well as approvals of legal metrology instruments. Beside that CMI focuses on R&D in all fields which are in the scope of its activities including big data, AI in image processing, and cloud computing. CMI has expertise in coordination of research projects such as EURAMET-EMPIR. Medical Devices Certification Center (CMI Medical) is an organizational unit of CMI which is responsible for certifications in the field of conformity assessment of medical devices under Regulation (EU) No. 2017/745 (MDR) and which brings together experts from various fields of medicine, technology and law. CMI will mainly contribute to WP3 and WP4.

IMBiH is the National Institute of Metrology of Bosnia and Herzegovina and has significant international projects and management of working groups (international projects, grants, research, bilateral projects). IMBiH brings in expertise on uncertainty evaluation. IMBiH is a member of the European Centre for Mathematics and Statistics in Metrology (MATHMET), responsible for strengthening interactions with stakeholders from industry, regulatory bodies, and academics needing consultation on mathematics and statistics in metrology providing guidelines, regular workshops, and advice. IMBiH will use its expertise to contribute mainly to WP1, WP3 and WP4.

IPQ is the National Metrology Institute of Portugal and besides Scientific and Applied Metrology also includes Legal Metrology. It has been involved in several successful European Research Projects, within the framework of EMRP and EMPIR. IPQ has a Mathematical and Programming Group and brings expertise in Machine Learning algorithms and their use in metrology, and in uncertainty evaluation, being an active member of EMN MATHMET Steering Committee. Hence IPQ will contribute mainly to WP1 and WP3.

LNE´s department ‘Mathematics and Statistics’ is mainly concerned with methods for uncertainty evaluation, statistical inverse problems, deconvolution methods in a probabilistic framework or treatment of interlaboratory studies. Jointly with NPL and PTB, the department at LNE led a successful EURAMET inter-disciplinary research project on uncertainty analysis applied to dynamic measurement problems. LNE will take part in the realisation of all WPs.

Fibri Check (FC) is an industrial partner in this project and redefines the way cardiac care is delivered by leveraging PPG technology. FC have global market access with medical device approvals in Europe, USA, Australia, the Middle-East and Singapore. FC have a strong connection with the medical community and end users, especially in Belgium, the Netherlands and the UK, and collaborate closely with prominent cardiologists. Since 2015 FC has been collecting PPG data via the FC application, in real-world settings and clinical settings, including several studies data in which data with the FC application is simultaneously collected with data from ECG technologies, the gold standard in cardiac care, leading to a database today of more than 10 million minutes of PPG data. FC will use the expertise mentioned above to contribute mainly to WP2.

WIAS is a worldwide leading institution for the development of highly qualified methods for the integrated solution of complex applied mathematical problems. It has extensive experience with numerical simulations and optimisation, model order reduction techniques and the analysis of stochastic systems. WIAS will mainly be involved in WP1 and contribute its expertise in the treatment of high-dimensional partial differential equations and Bayesian inverse problems with surrogate models and machine learning techniques.

KTU has expertise in biomedical engineering, biomedical signal processing, and PPG signal sensors. It also has experience in the application of PPG-based technology to heart arrhythmia, blood pressure, mental stress, and pain monitoring. KTU’s expertise will be used primarily in WP1, where uncertainty quantification methods will be developed using transform domain classification and regression methods, and in WP2, where relevant datasets from KTU projects KidneyLife, TriggersAF and Painless will be adapted and standardised to the project format, also in WP3 by contributing to the development of the open-source toolbox for uncertainty estimation and dissemination of results in conferences.

THM has expertise in clinical data generation and the application of machine learning techniques and sensitivity analysis. THM has a leading role in WP2, i.e., the selection of appropriate datasets and measuring and classification and regression problems. THM is able to provide clinical datasets on continuous blood-pressure (i.e., PPG, ECG and pressure measurements – continuous as well as beatto-beat systolic, mean and diastolic values). Furthermore, THM is able to generate datasets for regression/classification of aneurysm and stenoses in-silico, in-vitro and in-vivo. THM will also work in WP1 on the design of optimal pre-processing pipeline, uncertainty quantification on feature extraction and classification. This work includes the quantification of measurement uncertainties, the optimisation of parametric pre-processing pipeline and uncertainty propagation/elimination and the quantification of preprocessing uncertainties and feature extraction on classification quality.

Ghent University’s bioMMeda research group studies (fluid) mechanical aspects of and transport processes in a native organ or system, in artificial organs and prosthetic devices. One of the main research topics of IBiTech-bioMMeda is non-invasive vascular research, drawing on expertise from multiple faculties within Ghent University and performing research within an extended international network of research collaborations focused on the advanced modelling of cardiovascular interaction, normative values of arterial function and structure. Ghent University will use this expertise to contribute mainly to WP2.

Carl von Ossietzky Universität Oldenburg (UOL) has broad expertise in the analysis of health data using machine learning. In particular, this includes the modelling of medical time series data as raw data using deep learning as well as research on different quality criteria for machine learning algorithms in health, both of which are central aspects of WP1.

KCL has strong expertise in biomedical engineering (specialised in medical imaging, computational modelling, AI and machine learning) and pharmacological and physiological sciences. KCL’s contribution will focus on creating reference datasets of synthetic PPG signals for thousands of virtual subjects to benchmark machine learning models for analysing these signals. Through existing networks, the KCL team has access to healthy human volunteer and clinical datasets and labs to validate synthetic PPG signals under physiological and pathophysiological conditions. KCL will contribute to all WPs with a main focus on WP2. KCL is an associated partner associated to all beneficiaries.

NPL has a group dedicated to data science which consists of 45 permanent members of staff, 5 Joint Appointments (with the Universities of Surrey and Strathclyde) and 20 PhD students, specialising in machine learning, medical image and signal processing, mathematical modelling, simulation of measuring systems and uncertainty evaluation. It also plays a leading role in the Joint Committee for Guides in Metrology (JCGM) that is responsible for publishing the Guide to the expression of uncertainty in measurement (GUM). NPL will contribute to all WPs. NPL is an associated partner associated to all beneficiaries.

Sector Health is the leading wearables design and development technology company, creating wearable devices from idea to mass production. The company continues to expand, fuelled by an increasing demand for innovation in both consumer and medical devices. Operating at the intersection of so many diverse projects, Sector Health is exposed to innovations from across the field and works with the latest sensing and manufacturing technologies. Sector Health also runs service design sessions with clinicians, patients, clients and their customers; all of which allow to understand what the user needs are for devices and what factors increase technology use adherence. Using the expert knowledge on PPG signals and clinically relevant scenarios, Sector Health will mainly contribute to WP2. Sector Health is an associated partner associated to all beneficiaries.

The Department of Clinical and Experimental Medicine of the University of Surrey has expertise in clinical and translational cardiovascular and metabolic medicine. Strong links exist with the Surrey Institute for People-Centred Artificial Intelligence and the Faculty of Engineering and Physical Sciences. Ongoing research projects focus on healthy vascular ageing, the pathophysiology, development of innovative technologies for early detection and interventions for treatment of micro- and macrovascular disease. Links with the National Health service and vascular medical networks allow implementation into healthcare including guideline development. Contribution to the project by the University of Surrey include the provision of clinical expertise, access to small scale well characterised PPG datasets, and access to Healthcare organisations and medical networks for WP2. The University of Surrey is an associated partner associated to all beneficiaries.

The Department of Public Health and Primary Care of the University of Cambridge has expertise in simulating and processing PPG signals and benchmarking PPG analysis techniques (WP2). The Department is running the world’s largest trial of screening for atrial fibrillation, which provides electrocardiogram data from which to simulate PPG signals (WP2) and has experience in translating physiological measurement devices into clinical practice, working closely with patients and clinicians (WP3). The University of Cambridge is associated to all beneficiaries.