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		<datestamp>2026-01-01 </datestamp>
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			<dc:title><![CDATA[ Comparison of Chest Leads in ECG-Based Human Identification   ]]></dc:title>
			<dc:creator>TAŞTEKİN,Yasin</dc:creator>
			<dc:description><![CDATA[Electrocardiogram (ECG) signals can be used as an alternative method in biometric identification systems due to the unique cardiac structure of each individual. In this study, the person identification performances of six different chest leads (V1-V6) were compared. ECG recordings of 290 patients from the PTB Diagnostic ECG Database were used, and heartbeats were segmented after R-peak detection. A convolutional neural network (CNN)-based deep learning model was trained for each lead. According to the results obtained, the V3 lead showed the highest performance with 98.50% accuracy. The V2 lead ranked second with 98.43% accuracy, while the V1 lead exhibited the lowest performance with 97.69%. The AUC values of all leads were above 0.999, indicating excellent discrimination capacity. The findings demonstrate that biometric identification can be performed with high accuracy even using a single lead, and V2-V3 leads are the most suitable options for practical applications. ]]></dc:description>
			<dc:date>2026-01-01</dc:date>
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