Wearable devices have transformed the way we monitor our health. From tracking steps to monitoring heart rates, these gadgets provide valuable insights.
However, they’re not perfect. A significant challenge has been the noisy data that wearable devices collect, especially regarding electrocardiograms (ECGs).
This noise can interfere with accurately detecting cardiovascular issues using artificial intelligence (AI).
Yale’s Innovative Solution
Researchers at Yale, combining expertise in cardiovascular medicine and computer science, have tackled this challenge head-on.
Their recent study showcases the development of an AI model specially designed to work with the noisy ECGs from wearables.
To create this model, the team used a whopping 385,601 ECGs. They developed two AI models: a standard one and a noise-adapted one.
The latter was trained using ECGs modified with custom noises in four frequency ranges. These noises were designed to mimic the kind of disturbances commonly found in real-world wearable data.
Results Speak for Themselves
When tested on traditional ECGs, both AI models showed similar performance. However, the real test was with noisy wearable data.
Here, the noise-adapted AI model stood out, proving significantly better at detecting left ventricular systolic dysfunction (LVSD) – a key marker for heart issues.
Implications for the Future
Historically, there’s been a gap in AI’s diagnostic capabilities when comparing wearable ECG data to the more refined data from clinical 12-lead ECGs.
Yale’s research may be bridging this gap. By improving the accuracy of heart monitoring on wearable devices, there’s potential for earlier detection of heart conditions.
This research signifies a step forward in making wearable devices even more reliable health monitors for everyday use.
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The study was published in npj Digital Medicine.
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