Principal Investigator: Dr Chris Crockford
Department: Relative Health Limited, Burton in KendalTags: 57170, ai, blood pressure, cardiovascular, future-health, Hypertension, Machine Learning
Having built an artificially intelligent machine learning algorithm that can determine Blood Pressure from two physiological standard biomarkers, we wish to test out model on the population Blood Pressure and ECG data held within biobank. Our aims are to identify new biomarkers within the ECG signal that relate to the Blood Pressure directly.
The scientific rationale for doing these works is to establish new linkages between the cardio and vascular side of the cardiovascular system and to provide a new means to acquire Blood Pressure data without having to use time consuming, uncomfortable inflatable cuffs.
The project duration is 6 months.
The public health impact is significant as Blood Pressure is one of the least adhered to physiological parameters.
In the EU nearly 24 % of the deaths of 1.7m persons younger than 75 could have been avoided.
The WHO accredits 63% of global deaths to non-communicable diseases that are largely preventable.
Hypertension – Blood pressure (BP) increases the risk of cardiovascular diseases, strokes, and arterial stiffness.
The Lancet concurs that: Despite the widespread availability of effective treatment, control of hypertension in the community remains sub-optimal. Key reasons cited are clinical inertia, poor adherence, and organisational failure. However it is known that self-monitoring is an effective way to improve BP control. NHS England state cardiovascular disease affects 6 million people and costs £7 billion a year. CAVE addresses this healthcare need by developing novel algorithms to derive BP from ECG signals alone.