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An Automated Strategy for Early Risk Identification of Sudden Cardiac Death by Using Machine Learning Approach on Measurable Arrhythmic Risk Markers

Authors: Lai, D.; Advisor: -; Participants: Zhang, Y.; Zhang, X.; Su, Y.; Heyat, M. B. Bin (2019)

ABSTRACT Early risk identi cation of an unexpected sudden cardiac death (SCD) in a person who is suffering malignant ventricular arrhythmias is highly signi cant for timely intervention and increasing the survival rate. For this purpose, we have presented an automated strategy for prediction of SCD with a high-level accuracy by using measurable arrhythmic markers in this paper. The set of arrhythmic parameters includes three repolarization interval ratios, such as TpTe/QT, JTp/JTe, and T and two conduction-repolarization markers, such as TpTe/QRS and TpT/(QT QRS). Each of them is calculated directly from the detected QRS complex waves and T-wave of electrocradiogram (ECG) signals. ...