The Machines and the Heart: Using AI to predict cardiovascular risks from PPG signals

Description

This is an exciting funded PhD project to apply novel deep learning techniques to PPG signals for the diagnosis of heart diseases, suited to candidates with an applied mathematics, computer science, electrical engineering, medical imaging, biomedical engineering, physics or equivalent MSc/BSc degree.

The overarching aim of this project is to develop new artificial intelligence prediction tools to automatically predict risks of cardiovascular diseases using photoplethysmography (PPG) signals of smart wearable devices. Cardiovascular diseases (CVDs) are diseases of the heart and blood vessels. According to the World Health Organisation (WHO), 17.9 million people die each year from CVDs, an estimated 31% of all deaths worldwide. The successful candidate will develop novel deep-learning signal analysis solutions to support the prediction of CVDs based on an unprecedented large dataset in Liverpool.

The successful PhD candidate will benefit from working with a multidisciplinary team in which there exists extensive experience in the areas of computer science, image and signal processing, high performance computing, mathematics, and medicine. All postgraduate students undertake the PGR Development Programme which aims to enhance their skills for a successful research experience and career. They are required to maintain an online record of their progress and record their personal and professional development throughout their research degree. The 1st Year Development Workshops encourage inter- and cross-disciplinary thinking and identify and develop the knowledge, skills, behaviours and personal qualities that all students require. In the 2nd year all students take part in a Poster Day to provide an opportunity to present their research to a degree educated general public, and in the 3rd year students complete a career development module. Other online training, such as ‘Managing your supervisor’ and ‘Thesis writing’ is provided centrally.

Any specific eligibility requirements:   The successful candidate should have, or expect to have an Honours Degree at 2.1 or above (or equivalent) in Mathematics, Engineering, Physics or Computer Science. It is essential to have good background knowledge in mathematics, machine learning, computer programming (e.g., Python or C++), and signal/image processing plus a proactive approach to their work. Candidates whose first language is not English should have an IELTS score of 6.5 (with no band below 5.5) or equivalent.

To apply please send your CV and a covering letter to Prof Yalin Zheng (yalin.zheng@liverpool.ac.uk) with a copy to ilcamspgradmin@liv.ac.uk.

Informal enquiries regarding this project should be made to Prof Yalin Zheng (yalin.zheng@liverpool.ac.uk). All general enquiries should be directed to Mrs Eleanor Toole (ilcamspgradmin@liv.ac.uk.)

 

Availability

Open to students worldwide

Funding information

Funded studentship

Stipend (approx): £15,000 per annum tax-free, full UK home tuition fees and research bench fees paid. Exact amount TBC.

Supervisors

References

1. Sadad T, et al. Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing. Comput Intell Neurosci. 2022 Aug 4;2022:1672677. doi: 10.1155/2022/1672677.

2. Guo Y, et al. Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Prediction: A Report from the Huawei Heart Study. JACC Asia. 2021 Dec 21;1(3):399-408. doi: 10.1016/j.jacasi.2021.09.004.

3. Pereira T, et al. Photoplethysmography based atrial fibrillation detection: a review. NPJ Digit Med. 2020 Jan 10;3:3. doi: 10.1038/s41746-019-0207-9.