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Evaluating the feasibility of remote haemodynamic monitoring in a cohort of multi-ethnic chronic stroke patients using a novel user interface and artificial intelligence algorithms

   Cardiovascular Sciences

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  Dr Jatinder Minhas, Prof T Robinson, Prof K Khunti  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

Supervisors: Dr Jatinder S. Minhas [Email Address Removed] ; Professor Thompson G. Robinson [Email Address Removed] ; Professor Kamlesh Khunti [Email Address Removed]

Collaborators: Professor Ronney B Panerai ; Dr Lucy Beishon ; Dr Ashiq Anjum (Informatics) ; Infiniwell.AI

This PhD studentship offers a unique opportunity to develop inter-disciplinary cardiovascular research training from world-leading teams with a strong collaborative track record. Furthermore, this studentship presents an excellent opportunity for the candidate to work with an industry collaborator (Infiniwell.AI) by engaging in clinical testing of a decision-support system able to interact with real-time data collected from state-of-the-art remote monitoring devices.

Dr Jatinder Minhas (NIHR Clinical Lecturer), Professor Thompson Robinson (NIHR Senior Investigator) and Professor Kamlesh Khunti (NIHR Senior Investigator) will form the supervisory team. They have internationally recognised expertise in delivering bench-to-bedside haemodynamic studies, vascular preventative studies and studies assessing cardiovascular risk in ethnic minority groups. Their research questions integrate patient and public involvement through local support groups and the Centre for Ethnic Health Research.

This project aims to assess the feasibility of recording basic haemodynamic parameters using a novel mobile device in a multi-ethnic patient population with chronic stroke. This project builds on two recent successfully delivered funded projects. The first, a Phase V Collaborations for Leadership in Applied Health Research and Care (CLAHRC) project which provided for the first time a blood pressure threshold for improved outcomes for those diagnosed with atrial fibrillation and treated with anticoagulation (Minhas et al. 2020 Journal of Hypertension)[1]. The second, LPMI Industry and Academic Exchange (IAX) meeting funding, which permitted a successful academia, industry, patient and carer event to take place (, consolidating our collaboration with Infiniwell.AI.

This PhD will provide formal training in haemodynamic monitoring post-stroke (specific focus on blood pressure), ethnicity focussed research, exposure to artificial intelligence algorithms, and remote monitoring device-testing and development. Beyond this PhD, we envisage the candidate being able to deliver high quality intervention focussed cross-collaborative cutting-edge post-doctoral research across the spectrum of cardiovascular diseases including stroke.

This PhD project will permit alignment of current workstreams within the Cerebral Haemodynamics in Ageing and Stroke Medicine (CHiASM) group and the Leicester Diabetes Centre/Centre for Ethnic Health Research groups. By combining expertise, infrastructure and existing knowledge gained through collaboration[1,2], the student will benefit from joined up training from a supervisory team with broad expertise across the spectrum of bench-to-bedside cardiovascular research.

Whilst the proposed focus of testing the Clarity Medical Recobro Vigile device and the Infiniwell.AI algorithms is a core aim, as the PhD evolves, other high risk groups for cardiovascular complications (including arrhythmias, hypertension and secondary infection) can and will be assessed.

The aims of this PhD studentship are:

1. Assess the feasibility of recording basic haemodynamic parameters using a novel mobile device in a multi-ethnic patient population with chronic stroke

2. Assess variability of basic haemodynamic parameters (with particular focus on blood pressure and carbon dioxide) in patients with chronic stroke using Infiniwell.AI Artificial Intelligence (AI) algorithm software for biomarker identification

In this project, using cutting-edge mobile (home-based) monitoring equipment we will be able to gather high-resolution (1/sec) continuous vital sign readings over longer timespans (days and weeks) whilst the patient is at their home. Additionally, the monitoring devices are internet-connected, and can therefore be processed and analysed in real-time. This data set will give us a unique picture of the patient and will for the first time allow us to model in great detail how vital sign parameters change in response to treatment and follow-up. To date there exist no such data sets with equivalent detail, and no such data set which is collected from patients in their home setting[3,4,5].

The overall PhD study will be partially based on the existing Leicester Cerebral Haemodynamics (LCH) database of stroke patients, and partially on the data being collected as part of the project.

Infiniwell.AI will visit Leicester and the PhD student will visit Norway to ensure our Leicester Haemodynamic Database is equipped prospectively to receive the data generated through their CE marked devices and platform software.

Candidates will have a background in clinical engineering, informatics, biological sciences or medical sciences with a good BSc and/ or MSc in relevant subjects. Previous experience of recruiting to a clinical study and/or expertise in AI/data science would be desirable.


[1] Minhas JS, et al. What is the optimal blood pressure level for patients with atrial fibrillation treated with direct oral anticoagulants?  J Hypertens 2020.

[2] Mankoo, et al. Clinical relevance of orthostatic hypotension in patients with atrial fibrillation and suspected transient ischemic attack? High Blood Press Cardiovasc Prev 2020.

[3] Bivard A, et al. AI for decision support in acute stroke - current roles and potential Nat Rev Neurol 2020.

[4] Olive-Gadea M, et al. Deep Learning Based Software to Identify Large Vessel Occlusion on Noncontrast Computed Tomography Stroke 2020.

[5] Salinet ASM, et al. Do acute stroke patients develop hypocapnia? A systematic review and meta-analysis. J Neurol Sci 2019.



Funding details:

This 3-year NIHR ARC East Midlands Studentship provides:

  • UK tuition fee waiver
  • Annual stipend at UKRI rates: TBC for 2022/23 (£15,609 for 2021/22)

Entry requirements:

Applicants are required to hold/or expect to obtain a UK Bachelor Degree 2:1 or better in a relevant subject (or overseas equivalent). 

Candidates will be from a clinical engineering, informatics, biological sciences or medical sciences background with a good BSc and/ or MSc. Previous experience of recruiting to a clinical study and/or expertise in AI/data science would be desirable.

The University of Leicester English language requirements apply where applicable.

Application advice:

To apply please, refer to the guidance at:

Project / Funding Enquiries:

We are keen to attract the very best applicants for this exceptional opportunity. We will be pleased to have informal conversations with interested individuals prior to applications - please contact: Dr Jatinder Minhas [Email Address Removed]

Application enquiries to [Email Address Removed]

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