University Health Network Reports Findings in Cardiomyopathies (Machine Learning Identifies Arrhythmogenic Features of QRS Fragmentation in Human Cardiomyopathy : Implications for Improving Risk Stratification)
University Health Network Reports Findings in Cardiomyopathies (Machine Learning Identifies Arrhythmogenic Features of QRS Fragmentation in Human Cardiomyopathy : Implications for Improving Risk Stratification)
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Heart Disorders and Di seases - Cardiomyopathies is the subjectof a report. According to news reportin g originating from Toronto, Canada, by NewsRx correspondents,research stated, “ Heterogeneous ventricular activation can provide the substrate for ventricular a rrhythmias(VA), but its manifestation on the electrocardiogram (ECG) as a risk stratifier is not well-defined. Tocharacterize the spatiotemporal features of Q RS peaks that best predict VA in patients with cardiomyopathy(CM) using machine learning (ML).”
Key words
Toronto/Canada/North and Central Ameri ca/Cardiology/Cardiomyopathies/Cardiovascular Diseases and Conditions/Cyborg s/Emerging Technologies/Health and Medicine/Heart Disease/Heart Disorders an d Diseases/Machine Learning
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