Researchers at Texas Tech University are developing a wearable real-time heart monitoring sensor that uses nanoparticles, AI and quantum phenomena.

The team from the university’s Edward E Whitacre Jr College of Engineering (www.depts.ttu.edu/coe/) received a National Institute of General Medical Sciences grant of over $580,000 to fund the four-year project to develop a wearable magnetic sensor that uses machine-learning models for accurate and predictive monitoring.

The project is led by Kai Wu, assistant professor in the Department of Electrical & Computer Engineering, and Minxiang “Glenn” Zeng, assistant professor in the Department of Chemical Engineering.

The device combines nanoparticles, materials and polymers to create a lightweight sensor that easily adheres and adjusts to wherever it is placed.

“The bio sensor would be like a temporary tattoo that can stick on a person’s chest,” Wu said. “It’s non-invasive, wearable and won’t cause any abnormal or uncomfortable feelings for a user.”

The sensor could monitor cardiac activity and better predict and distinguish more than ten types of abnormal heartbeats. This, Wu said, would be especially important for patients after cardiovascular surgery who would want 24-hour monitoring of their heart activity.

Traditional heart monitoring devices record electrocardiograms (ECGs) that measure electrical activity in a person’s heart. More advanced tools such as magnetocardiography (MCG) detect the tiny magnetic fields generated by the heart. While ECGs are more readily accessible, they may not show the full picture of a person’s cardiac health; meanwhile, MCGs provide more accurate readings but are limited in terms of commercial use because they rely on large and expensive equipment.

The project aims to address this and bring advanced cardiac monitoring directly to people through the development of granular magnetoresistive sensors.

The sensors would rely on two quantum phenomena occurring between the nanoparticles and polymers of the sensor: tunnelling and hopping. Tunnelling occurs when a particle passes through a potential energy barrier that it should not be able to cross. Hopping occurs when a particle transitions between quantum states with the assistance of thermal energy.

“Based on the magnetic field produced by the human heart, we can convert the field into the voltage signal from the sensor itself, and we can do 24-hour recordings of the signals from the sensor,” Wu said.

Machine-learning models can be vital in analysing such a large amount of data and giving users real-time monitoring of their heart’s condition.

“Take a smartwatch, like the one I’m wearing,” Wu said. “It can monitor a person’s heartbeat, but it does not involve AI to say what condition a person is going through, right? These devices can only tell you what your heart rate is. But the AI algorithm, combined with the wearable sensor, could provide more information if a person is going to suffer from some specific abnormal heartbeat.”

Wu said a motivating factor behind this project was the end-user’s experience, and this extended to the cost of the eventual sensor, too.

To create the sensors, the team will use 3D printing, a first for Wu. Over the past ten years, Wu has developed sensors using nanofabrication facilities. The 3D printing will not only help keep the development costs down but also could play a factor in future commercialisation and accessibility.

Wu said this sensor could help address an imbalance of resources in healthcare, especially for rural patients who often must drive hours for specialised medical care.

“3D printing is not only cheaper, but it can print the sensor very fast,” he said. “We can mass produce the sensor in large volumes, which would also cut down on costs. This would be a big benefit for people in rural or resource-limited areas.”