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The AI wave has swept the tech industry, and big and small companies are working to incorporate AI-powered features into their products.As more businesses climb the AI bandwagon, the demand for such infrastructure will inevitably grow. However, Mark Hersam, a professor of Materials Science at Northwestern University, points out that the approach is energy-intensive since data is collected, sent to the cloud for analysis, and then results are sent back to the user.
"The integration of two disparate materials into one device allows us to strongly modulate the current flow with applied voltages, enabling dynamic reconfigurability," Hersam added in the press release. The device was then asked to classify the data into six types of heartbeats that are often seen, viz., normal, atrial premature beat, premature ventricular contraction, paced beat, left bundle branch block beat, and right bundle branch block beat for 10,000 ECG samples, which it successfully achieved with 95 percent accuracy.
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