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	<title>Cardiac Electrophysiology &#8211; BIOENGINEER.ORG</title>
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	<title>Cardiac Electrophysiology &#8211; BIOENGINEER.ORG</title>
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		<title>Machine Learning Identifies Early Right Ventricular Activation</title>
		<link>https://bioengineer.org/machine-learning-identifies-early-right-ventricular-activation/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Mon, 01 Dec 2025 21:38:20 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[**Etiketler:** Machine Learning]]></category>
		<category><![CDATA[Arrhythmia Localization **Açıklama:** İçeriğin ana konularına odaklanan]]></category>
		<category><![CDATA[Cardiac Electrophysiology]]></category>
		<category><![CDATA[kısa ve aranabilir etiketler seçilmiştir: - **Machine Learning**: Çalışmanın temel metodolojisi. - **Cardiac Electrophysiology**:]]></category>
		<category><![CDATA[QRS Analysis]]></category>
		<category><![CDATA[Right Ventricular Activation]]></category>
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					<description><![CDATA[In an evolving landscape of cardiac care, a groundbreaking study has emerged that proposes a novel approach to the localization of early right ventricular activation sites. Spearheaded by researchers Seagren, Lancini, and Ni, this research taps into the distinguished capabilities of machine learning algorithms to enhance the understanding of heart rhythm disorders. The implications of [&#8230;]]]></description>
		
		
		
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