By Alexandra Thompson Senior Health Reporter For Mailonline
Published: 12:39 BST, 11 September 2019 | Updated: 12:39 BST, 11 September 2019
A new AI-based tool could detect heart failure from just one heartbeat, research suggests.
Scientists 'fed' the system with electrocardiograms (ECG) that made up more than 490,000 heartbeats.
The technology was then exposed to a series of 'five minute ECG excerpts' taken from 24 hour recordings.
Results showed the convolutional neural network, as it is called, was 100 per cent accurate at spotting patients with heart failure.
An AI model could detect heart failure from one heartbeat with 100 per cent accuracy (stock)
The University of Surrey team hope their tool will one day help doctors diagnose HF sooner, 'benefiting patients and easing pressures on NHS resources'.
Heart failure occurs when the organ's muscles are too weak or stiff to pump blood around the body effectively.
This can be due to high blood pressure or the arteries narrowing. Drinking too much alcohol can also cause it, the NHS says.
The condition affects around 26million people worldwide to some extent, according to the European Society of Cardiology.
In the most severe cases, up to 40 per cent of patients die from the condition, the researchers wrote.
It is also one of the main causes of hospitalisation among elderly people, the team added.
With life expectancy on the rise, the team set out to uncover a more accurate way of diagnosing HF early on.
Existing methods look at heart rate variability (HRV), which describes inconsistencies in the space between consecutive heartbeats.
However, HF can generally only be diagnosed after a person's HRV is looked at for around 24 hours.
To overcome this, the researchers led by Dr Sebastiano Massaro focused