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	<title>masked autoencoder &#8211; BIOENGINEER.ORG</title>
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		<title>Self-supervised AI rebuilds missing fetal heart rate signals from Tanzanian labor recordings</title>
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		<pubDate>Mon, 26 Oct 2026 16:15:05 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[fetal heart rate]]></category>
		<category><![CDATA[health informatics]]></category>
		<category><![CDATA[masked autoencoder]]></category>
		<category><![CDATA[Moyo monitor]]></category>
		<category><![CDATA[Safer Births]]></category>
		<category><![CDATA[Self-Supervised Learning]]></category>
		<category><![CDATA[signal inpainting]]></category>
		<category><![CDATA[Tanzania]]></category>
		<category><![CDATA[time-series forecasting]]></category>
		<category><![CDATA[Transformer]]></category>
		<category><![CDATA[wearable monitoring]]></category>
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					<description><![CDATA[A self-supervised transformer model trained on more than 4,400 fetal heart rate recordings from Tanzanian hospitals can reconstruct missing signal segments while preserving the variability patterns clinicians rely on, and forecast short-term heart rate changes over a 15-second horizon.]]></description>
		
		
		
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