An Imperial team is one of the major winners in a global competition, after creating a device that analyses the body's sounds to detect illnesses.

The GUES team from Imperial College London has won $120,000 in the XPRIZE Nokia Sensing XCHALLENGE, in which they were the only UK based team. The international competition aimed to develop breakthrough high impact medical sensing technologies that will ultimately enable faster diagnoses and easier personal health monitoring.  The winners were announced this week in Los Angeles, where the team had flown to take part in the finals.

The researchers have developed a wearable, wireless device, the approximate size of a pound coin, which sticks onto a person’s neck or chest to detect sounds emanating from the heart and respiratory system. AcuPebble uses advanced algorithms to sift through a range of sounds to determine parameters that may indicate deteriorating health or illness in patients.

Team leader Dr Esther Rodriguez-Villegas said: “The response to AcuPebble has been absolutely overwhelming. We’ve had people writing to us from the five continents, telling us how much they love the technology.  This competition has shown us just how important it is to get AcuPebble into the market as quickly as possible, where it will have the most benefit to society. We can’t wait to get started on the next steps to make this a reality.”

A versatile sensor

Based on their research and development work over the past eight years, the Imperial team believes that AcuPebble could be used in a range of clinical settings including as a diagnostic tool for diseases such as sleep apnoea, chronic obstructive pulmonary disease or asthma, a health monitor and as an early warning device for both adults and children. The sensor will collect data in real-time, interpret it automatically and transmit this information to an application that can be downloaded onto any smart device, so that patients can be self-diagnosed and doctors can monitor their patients anywhere in the world.

AcuPebble is the size of a one pound piece

The researchers believe that AcuPebble could be used to improve the diagnosis of a range of respiratory and cardiac conditions, including sleep apnoea, whooping cough, pneumonia, chronic obstructive pulmonary disease and congestive heart failure. 

Dr Rodriguez Villegas is joined in Team GUES by her research assistants Guangwei Chen and Syed Anas Imtiaz, who are all part of the Department of Electrical and Electronic Engineering at Imperial College London.

Dr Rodriguez-Villegas said: “Hundreds of millions of people suffer every day from chronic respiratory diseases. It is almost certain that many of us will be faced with these health conditions in our lives. However, there aren’t sufficient resources for doctors to give an early and quick diagnosis to everyone. We need accurate and reliable technologies that can speed up diagnosis and free up resources in hospitals, while also improving the quality of life for patients. We believe that AcuPebble can provide an easy-to-use diagnostic tool, making personal health information accessible to trusted sources anywhere in the world, ultimately improving how we look after ourselves.”

Test run

The researchers, in conjunction with academics from the National Hospital for Neurology and Neurosurgery, have also carried out a small pilot clinical study using the AcuPebble. The trial consisted of 30 people, including 20 controls and ten patients who had been referred to the hospital with suspected sleep apnoea. The team found that AcuPebble could automatically detect at least nine out of ten individual apnoea episodes in those included in the  pilot study.   

AcuPebble was able to automatically analyse a range of acoustic signals from both inside and outside the body to determine the sounds that could indicate sleep apnoea. For example, the sensor analysed turbulence in the airways, which can indicate an obstruction, the depth and duration of breathing and other vital signs such as a person’s heart rhythm. The device was able to provide accurate information about breathing rate, heart rate and lung volumes and the type of apnoea a patient was experiencing.