AI smart watches could predict higher risk of heart failure
Wearable devices such as smart watches with local machine learning could be used to detect a higher risk of developing heart failure and irregular heart rhythms in later life according to a study in the UK.
Lack of data on the effectiveness of the sensors and detection algorithms in wearable devices has limited the use. A study, published in The European Heart Journal – Digital Health, looked at data from 83,000 people who had undergone a 15-second electrocardiogram (ECG) comparable to the kind carried out using smart watches and phone devices.
The researchers at UCL in London identified ECG recordings containing extra heart beats which are usually benign but, if they occur frequently, are linked to conditions such as heart failure and irregular heartbeats, or arrhythmia.
They found that people with an extra beat in this short recording (one in 25 of the total) had a twofold risk of developing heart failure or an irregular heart rhythm (atrial fibrillation) over the next 10 years.
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The ECG recordings analysed were from people aged 50 to 70 who had no known cardiovascular disease at the time. Consumer-grade wearable devices rely on two sensors (single-lead) embedded in a single device and are less cumbersome as a result but may be less accurate.
The research team used machine learning and an automated computer tool to identify recordings with extra beats. These extra beats were classed as either premature ventricular contractions (PVCs), coming from the lower chambers of the heart, or premature atrial contractions (PACs), coming from the upper chambers.
The recordings identified as having extra beats, and some recordings that were not judged to have extra beats, were then reviewed by two experts to ensure the classification was correct.
“Our study suggests that ECGs from consumer-grade wearable devices may help with detecting and preventing future heart disease,” said Dr Michele Orini from UCL’s Institute of Cardiovascular Science. “The next step is to investigate how screening people using wearables might best work in practice.
“Such screening could potentially be combined with the use of artificial intelligence and other computer tools to quickly identify the ECGs indicating higher risk, as we did in our study, leading to a more accurate assessment of risk in the population and helping to reduce the burden of these diseases.”
The researchers first looked at data from 54,016 participants of the UK Biobank project with a median age of 58, whose health was tracked for an average of 11.5 years after their ECG was recorded. They then looked at a second group of 29,324 participants, with a median age of 64, who were followed up for 3.5 years.
After adjusting for potentially confounding factors such as age and medication use, the researchers found that an extra beat coming from the lower chambers of the heart was linked to a twofold increase in later heart failure, while an extra beat from the top chambers of the heart was linked to a twofold increase in cases of atrial fibrillation.
The study involved researchers at UCL Institute of Cardiovascular Science, the MRC Unit for Lifelong Health and Ageing at UCL, Barts Heart Centre (Barts Health NHS Trust) and Queen Mary University of London. It was supported by the Medical Research Council and the British Heart Foundation, as well as the NIHR Barts Biomedical Research Centre.
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