CAPE - Cardiac Analysis for Pressure Establishment

CAPE is a Machine Learning Project using Artificial Intelligence to probe the world of ECG signals.

We use the power of Google's Deepmind to probe, analyse and pattern match almost 100,000 publicly accessible ECG signals.

The aims of CAPE are to establish if there are hidden bio-markers within the ECG signals that can help us understand the cardiovascular system better. This could be understanding how the vascular system is operating in terms of arterial elasticity, blood viscosity etc.

CAPE builds on the companies works to date of establishing multiple physiological parameters from wearable technologies such as smart-watches and fit bits.

We use the latest Artificial Intelligence thinking to look for patterns from one physiological parameter to another, which may not have been recorded. This enables us to create a system where the whole health of the individual may be recorded continuously and trended allowing for true performance scoring to take place.

Using data visualisation software from F1 Motorsport we link the data that CAPE is processing to a realisation system allowing us to visualise how one part of the physiological system is affecting the other.

The outputs of CAPE are simply an algorithm, but one that can see predict physiological performance based on the ECG system data being fed into it.

This is novel and is very applicable to the smart-watch market with Apple enabling the Apple Watch 4, in Europe, to record ECG in the last few months.

We all live in a busy world where getting to see a doctor and having readings on our physiology performed in the doctors surgery are getting harder to achieve owing to demand our stripping supply. We believe that the works undertaken as part of the CAPE project will allow for some of these performance physiological markers to be recorded on smart-watches and uploaded to the cloud to provide trending and analysis that can then be reviewed online by your GP or family doctor.

We are in effect through CAPE working to optimise the way in which we acquire and present physiological performance data to our clinicians. We still have a long way to go, but CAPE provides an exciting look into the world of physiological monitoring and using Machine Learning to see if there are aspects of the ECG signal - the hearts electrical system - that we currently do not understand or even realise that they are there.