CardieX, a Sydney-based global health technology company, has announced successful trial of its pulse wave analysis (PWA) technology in a wearable blood pressure sensor.
The results from an eight-week, 15-person trial of the technology in partnership with the Macquarie University’s faculty of medicine and health sciences have validated the commercial applications of the technology, CardieX announced today.
The trial has proven hat using the CardieX technology, together with blood pressure monitoring sensors developed by Silicon Valley based company Blumio, one could accurately extract cardiovascular signals from a variety of trial patient subjects with differing cardiovascular conditions as well as track changes in cardiovascular events in those patients .
CardieX’s trial objective was to determine the degree of blood pressure data that could be extracted from Blumio’s radar frequency sensor using CardieX’s PWA technology and the comparison of that sensor data to Cardiex’s (non-sensor based) XCEL device.
“In order to develop a commercial wearable blood pressure sensor, it requires the extraction of precise cardiovascular and blood pressure data from the relevant sensor. This initial trial is extremely encouraging as a clear demonstration of the use of our technology to extract specific blood pressure data from a wearable sensor,” CardieX CEO, Craig Cooper said.
As part of a multi-phase program, this first trial assessed the feasibility of obtaining cardiovascular blood pressure (BP) related data using Blumio’s radar frequency sensor and analysing it via CardieX PWA technology. The trial was led by CardieX Principal Scientist, Dr. Ahmad Qasem together with Professor Alberto Avolio of Macquarie University.
The trial also gave a strong indication that further central blood pressure (cBP) readings can be extracted from the sensor in a wearable device using CardieX neural learning and proprietary algorithms (A.I).
“We are continuing to refine the data obtained in the trial but our analysis to date has shown that with further neural learning – and the application of our proprietary algorithms – we have the ability to extract a wide range of commercially viable medical and consumer data from the sensor,”Cooper said.