Predicting and monitoring cardiovascular outcomes using wearable devices and novel machine learning techniques

Lead Research Organisation: University College London
Department Name: Medical Physics and Biomedical Eng

Abstract

1) Brief description of the context of the research including potential impact

Diagnosis and monitoring of cardiovascular disease(CVD) requires intrusive, in-clinic testing. This requires patients to take time out of their daily lives, as well as clinical resources to perform tests, and only reflects a small snapshot of time. Wearable devices offer a solution to this problem - patients can wear them freely in their daily lives, whilst having relevant clinical data collected regularly or even continuously.
Wearables have been massively adopted by consumers, with over 1 billion connected devices as of 2022 [1]. This adoption shows that people are comfortable with electronics that monitor their health, and many products such as the Apple Watch use health awareness as a large part of their product offering through heart rate monitoring, SpO2 max., and other fitness measurements.
By combining this wealth of data with novel time series methods borne out of advancements in machine learning/artificial intelligence, wearables can help move cardiovascular monitoring from the clinic to the home, as well as help at-risk users seek medical attention that could stop or slow the progression of their conditions. The potential impact for this technology is huge - cardiovascular disease is responsible for 25% of deaths in the UK, and clinical resources are already stretched. Moving the burden of diagnosis from the clinic to an algorithm, or simply offering clinicians another metric for prioritising patient care, could have a massive effect on patient wellbeing.

2) Aims and Objectives

The key aim of this research is to investigate whether advanced ML/AI based time series analysis techniques can predict cardiac outcomes using data collected from wearable devices.

3) Novelty of Research Methodology

This research aims to use previously unused features in wearable signals (e.g. photoplethysmography) to predict cardiac outcomes, as well as applying new methods to known signals to uncover or improve their predictive power, making them more useful and trusted in a clinical environment.

4) Alignment to EPSRC's strategies and research areas

Healthcare technologies, artificial intelligence

5) Any companies or collaborators involved
No

[1] Statista. "Global connected wearable devices 2016-2022". Available at: https://www.statista.com/statistics/487291/global-connected-wearable-devices/. Accessed [8 February 2022]

Planned Impact

The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.

In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S021930/1 01/10/2019 31/03/2028
2720272 Studentship EP/S021930/1 01/10/2022 30/09/2026 George Searle