How To Detect Heart Failure From A Single Heartbeat

Researchers have developed a neural network approach that can accurately identify congestive heart failure with 100% accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat, a new study reports.

Congestive heart failure (CHF) is a chronic progressive condition that affects the pumping power of the heart muscles. Associated with high prevalence, significant mortality rates and sustained healthcare costs, clinical practitioners and health systems urgently require efficient detection processes.

Dr Sebastiano Massaro, Associate Professor of Organisational Neuroscience at the University of Surrey, has worked with colleagues Mihaela Porumb and Dr Leandro Pecchia at the University of Warwick and Ernesto Iadanza at the University of Florence, to tackle these important concerns by using Convolutional Neural Networks (CNN) – hierarchical neural networks highly effective in recognising patterns and structures in data.

Published in Biomedical Signal Processing and Control Journal, their research drastically improves existing CHF detection methods typically focused on heart rate variability that, whilst effective, are time-consuming and prone to errors. Conversely, their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100% accuracy.

We trained and tested the CNN model on large publicly available ECG datasets featuring subjects with CHF as well as healthy, non-arrhythmic hearts. Our model delivered 100% accuracy: by checking just one heartbeat we are able detect whether or not a person has heart failure. Our model is also one of the first known to be able to identify the ECG’ s morphological features specifically associated to the severity of the condition,”  explains Dr Massaro.  Dr Pecchia, President at European Alliance for Medical and Biological Engineering, explains the implications of these findings: “With approximately 26 million people worldwide affected by a form of heart failure, our research presents a major advancement on the current methodology. Enabling clinical practitioners to access an accurate CHF detection tool can make a significant societal impact, with patients benefitting from early and more efficient diagnosis and easing pressures on NHS resources.”

Source: https://www.surrey.ac.uk/

Tool Speeds Up Manufacturing Of Powered Wearable

People could soon power items such as their mobile phones or personal health equipment by simply using their daily movements, thanks to a new research tool that could be used by manufacturers.

In a new paper published by Nano Energy, experts from the Advanced Technology Institute (ATI) at the University of Surrey (UK) detail a new  methodology that allows designers of smart-wearables to better understand and predict how their products would perform once manufactured and in use.

The technology is centred on materials that become electrically charged after they come into contact with each other, known as triboelectric materials – for example, a comb through hair can create an electrical charge. Triboelectric Nanogenerators (TENGs), use this static charge to harvest energy from movement through a process called electrostatic induction. Over the years, a variety of TENGs have been designed which can convert almost any type of movement into electricity. The University of Surrey’s tool gives manufacturers an accurate understanding of the output power their design would create once produced.

This follows the news earlier this year of the ATI announcing the creation of its £4million state-of-the-art Nano-Manufacturing Hub. The new facility will produce plastic nanoscale electronics for wearable sensors, electronic tags and other electronic devices.

Ishara Dharmasena, lead scientist on this project from the University of Surrey, said: “The future global energy mix will depend on renewable energy sources such as solar power, wind, motion, vibrations and tidal. TENGs are a leading technology to capture and convert motion energy into electricity, extremely useful in small scale energy harvesting applications. Our work will, for the first time, provide universal guidance to develop, compare and improve various TENG designs. We expect this technology in household and industrial electronic products, catering to a new generation of mobile and autonomous energy requirements.”

Source: https://www.surrey.ac.uk/