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This study presents a novel multi-class classification approach for the early-stage prediction of sudden cardiac death (SCD) using heart rate variability (HRV) as a biomarker. The proposed algorithm achieved an average classification accuracy of 83.33% in predicting SCD ten minutes before its occurrence, distinguishing SCD patients from those with heart failure and healthy individuals. The findings suggest a promising method for early identification of individuals at risk of SCD, although further testing on

Author

karimulla shaik

Language

EN