Imagine monitoring both your Cardio and Vascular systems  
continuously through wearable technology 
Imagine understanding your body's performance in terms of circadian rhythm

Welcome to Relative Health
We develop technologies that allow physiological monitoring through new means.
Our first goal is to change the way we monitor our Blood Pressure.
We will remove the need for uncomfortable, unreliable, inflatable cuffs, finally allowing 
Consumer administered continuous BP monitoring linked to circadian rhythm.

MORPHEUS-ML
Morpheus-ML utilises a five beat cycle process to ascertain Blood Pressure thus providing a unique insight into continuous BP monitoring for the wearables, primary and secondary care monitoring markets. 
MORPHEUS-ML can be tailored to accept signal data from a wide range of acquisition sources, frequencies and locations. 
"In under 24 months we have developed MORPHEUS to outperform all previous literature when it comes to calculating continuous Blood Pressure from non invasive means."
Dr Chris Crockford - Founder



                                                                                                                                         

Feeling the Pressure?
We're calling time on traditional ways to measure blood pressure. Using an inflatable cuff to obtain a single point reading is inaccurate and unreliable. Our technology is working to rethink Blood Pressure monitoring.

Each year, cardiovascular disease (CVD) kills more than 18 million people worldwide, a third of all global deaths, with this proportion expected to increase further. High blood pressure is the leading cause of CVD and it will kill nearly 10 million people this year—more people than all infectious diseases combined.

The global direct medical costs of hypertension are estimated at $370 billion per year, with the health care savings from effective management of blood pressure projected at roughly $100 billion per year.

Our part in all of this is to make monitoring your Blood Pressure considerably easier.





Our Machines learn in glass boxes
 Machine learning algorithms and models are becoming both ubiquitous and more trusted across industries. This, in turn, will lead us as humans spending less time questioning the output and simply allowing the system to tell us the answer. If ever there was an industry that needed proof that the models were correct it is healthcare.

The power of Machine Learning today means that it is simple to create a Black box solution to a problem. Simply ask the machine to pattern match data for you and see if it can discover new correlations that you previously did not know existed. This black box approach doesn't allow you to understand what or how the machine has learnt. 

We code differently, our Machine Learning works have already delivered powerful insights into the human cardio-vascular system.

We code to create Glass Boxes so we may understand the inner workings and understand what the machine is learning from.




“What all of us have to do is to make sure we are using AI in a way that is for the benefit of humanity, not to the detriment of humanity.” 
Tim Cook
MORPHEUS in the digital world
Our unique modular ML engine design allows for us to take in all manner of physiological data to find the hidden bio-markers previously unseen.
MORPHEUS-ML's data input pipeline can take in a wide range of PPG, ECG, and bio-metric data through its unique import pipeline. 

MORPHEUS-ML takes into account the location of the acquired data, the frequency and then timebases this accordingly, providing a continuous stream of synchronous physiological data.

Signal Quality Indexing is used to identify physiological data that meets the acceptance criteria and then the system utilises our unique synchronous amalgamation processing cycle to establish the Systolic and Diastolic Blood Pressures whilst calculating other variables such as PWV, PTT along the way.

We develop a transfer function for each manufacturers hardware which handles the different nuances of data presentation and quality.





Relative Health link
F1 visualisation technology with Machine Learning
Working with our partners at D&F Tech.com Relative Health's MORPHEUS-ML engine now exports its data to motorsport's leading ATLAS visualisation software.

Each cardiac cycle is now visualised in "lap time" allowing for the nuances of different manufacturers data to be represented visually once pre and post processed by MORPHEUS-ML.


Building on F1's data analytics heritage Relative Health have been able to create a new visualisation pipeline allowing real time analytics to be performed on the data being sent into and coming out of MORPHEUS-ML's engine.





Changing the Status Quo

Background Hypertension (HTN) is the single greatest cardiovascular risk factor worldwide. HTN management is usually guided by brachial cuff blood pressure (BP), but questions have been raised regarding accuracy. 

Three individual participant data meta-analyses were conducted among studies (from the 1950s to 2016) that measured intra-arterial aortic BP, intra-arterial brachial BP, and cuff BP.

A total of 74 studies with 3,073 participants were included.  Cuff BP underestimated intra-arterial brachial SBP (−5.7 mm Hg; 95% CI: −8.0 to −3.5 mm Hg; p < 0.0001) but overestimated intra-arterial diastolic BP (5.5 mm Hg; 95% CI: 3.5 to 7.5 mm Hg; p < 0.0001).  

Conclusions Cuff BP has variable accuracy for measuring either brachial or aortic intra-arterial BP, and this adversely influences correct BP classification. These findings indicate that stronger accuracy standards for BP devices may improve cardiovascular risk management.

Journal of the American College of Cardiology Volume 70, Issue 5, August 2017DOI: 10.1016/j.jacc.2017.05.064 "Accuracy of Cuff-Measured Blood Pressure"





People with high blood pressure, diabetes - 
those are conditions brought about by life style. 
If you change the life style,  those conditions will leave. 
Dick Gregory


Team
Dr Chris Crockford PhD
CEO & Founder - 8 years Med Tech, Ex F1 Bus Dev Dir. MBA Corporate Strategy
Justin King B.Eng.
Commercial Director - Multiple Start up experience, Former Mil Rotary Pilot
Dr Will Maden PhD
Consultant Mathematician -MLA Design 
 Monte Carlo Sim expert
Prof. Hugh Montgomery
Intensivist UCLH
Clinical Oversight (Voluntary)
Dr Andrew Mitchell
Consultant Cardiologist - Clinical Adviser Med Tech guru - @Mitcharj
Dr Simon Rudland
Primary Care Consultant - GP
Former Navy Diver 
"We need approaches to the solutions that aren’t just arithmetic and additive, but are in some sense logarithmic. This will require us to reach across historic boundaries and unlock the potential of collaboration across the usual disciplines."
Jeffrey S. Flier, MD – Dean of the Faculty of Medicine, Harvard University




About Us
Relative Health Limited, 
Unit 2, The Stables Block, 
Dalton Hall,
Dalton Hall Business Centre,
Burton in Kendall
Cumbria, LA6 1NJ
+44(0)1206209039
 
Relative Health Limited
UK registered company number 10294825
+44(0)1206209039 
contact@relativehealth.co
www.relativehealth.tech